![Page 1: Ontology Acquisition for Automatic Building of Scientific ...Ontology Acquisition for Automatic Building of Scientific Portals Pavel Smrˇz1 V´ıt Nov´aˇcek2 1Faculty of Information](https://reader033.vdocument.in/reader033/viewer/2022041712/5e488cc38cbe72002128e9e4/html5/thumbnails/1.jpg)
Introduction Ontologies in PortaGe OLE Preliminary Results Future Directions
Ontology Acquisition for Automatic Building ofScientific Portals
Pavel Smrz1 Vıt Novacek2
1Faculty of Information Technology,Brno University of Technology, Czech Republic
E-mail: [email protected]
2Faculty of Informatics,Masaryk University, Brno Czech Republic
E-mail: [email protected]
January 23, 2006
![Page 2: Ontology Acquisition for Automatic Building of Scientific ...Ontology Acquisition for Automatic Building of Scientific Portals Pavel Smrˇz1 V´ıt Nov´aˇcek2 1Faculty of Information](https://reader033.vdocument.in/reader033/viewer/2022041712/5e488cc38cbe72002128e9e4/html5/thumbnails/2.jpg)
Introduction Ontologies in PortaGe OLE Preliminary Results Future Directions
Outline
1 Introduction — PortaGe architecture
2 The role of ontologies in portal building
3 OLE — Ontology LEarning framework
4 Preliminary results
5 Future directions
![Page 3: Ontology Acquisition for Automatic Building of Scientific ...Ontology Acquisition for Automatic Building of Scientific Portals Pavel Smrˇz1 V´ıt Nov´aˇcek2 1Faculty of Information](https://reader033.vdocument.in/reader033/viewer/2022041712/5e488cc38cbe72002128e9e4/html5/thumbnails/3.jpg)
Introduction Ontologies in PortaGe OLE Preliminary Results Future Directions
Outline
1 Introduction — PortaGe architecture
2 The role of ontologies in portal building
3 OLE — Ontology LEarning framework
4 Preliminary results
5 Future directions
![Page 4: Ontology Acquisition for Automatic Building of Scientific ...Ontology Acquisition for Automatic Building of Scientific Portals Pavel Smrˇz1 V´ıt Nov´aˇcek2 1Faculty of Information](https://reader033.vdocument.in/reader033/viewer/2022041712/5e488cc38cbe72002128e9e4/html5/thumbnails/4.jpg)
Introduction Ontologies in PortaGe OLE Preliminary Results Future Directions
Outline
1 Introduction — PortaGe architecture
2 The role of ontologies in portal building
3 OLE — Ontology LEarning framework
4 Preliminary results
5 Future directions
![Page 5: Ontology Acquisition for Automatic Building of Scientific ...Ontology Acquisition for Automatic Building of Scientific Portals Pavel Smrˇz1 V´ıt Nov´aˇcek2 1Faculty of Information](https://reader033.vdocument.in/reader033/viewer/2022041712/5e488cc38cbe72002128e9e4/html5/thumbnails/5.jpg)
Introduction Ontologies in PortaGe OLE Preliminary Results Future Directions
Outline
1 Introduction — PortaGe architecture
2 The role of ontologies in portal building
3 OLE — Ontology LEarning framework
4 Preliminary results
5 Future directions
![Page 6: Ontology Acquisition for Automatic Building of Scientific ...Ontology Acquisition for Automatic Building of Scientific Portals Pavel Smrˇz1 V´ıt Nov´aˇcek2 1Faculty of Information](https://reader033.vdocument.in/reader033/viewer/2022041712/5e488cc38cbe72002128e9e4/html5/thumbnails/6.jpg)
Introduction Ontologies in PortaGe OLE Preliminary Results Future Directions
Outline
1 Introduction — PortaGe architecture
2 The role of ontologies in portal building
3 OLE — Ontology LEarning framework
4 Preliminary results
5 Future directions
![Page 7: Ontology Acquisition for Automatic Building of Scientific ...Ontology Acquisition for Automatic Building of Scientific Portals Pavel Smrˇz1 V´ıt Nov´aˇcek2 1Faculty of Information](https://reader033.vdocument.in/reader033/viewer/2022041712/5e488cc38cbe72002128e9e4/html5/thumbnails/7.jpg)
Introduction Ontologies in PortaGe OLE Preliminary Results Future Directions
Outline
1 Introduction — PortaGe architecture
2 The role of ontologies in portal building
3 OLE — Ontology LEarning framework
4 Preliminary results
5 Future directions
![Page 8: Ontology Acquisition for Automatic Building of Scientific ...Ontology Acquisition for Automatic Building of Scientific Portals Pavel Smrˇz1 V´ıt Nov´aˇcek2 1Faculty of Information](https://reader033.vdocument.in/reader033/viewer/2022041712/5e488cc38cbe72002128e9e4/html5/thumbnails/8.jpg)
Introduction Ontologies in PortaGe OLE Preliminary Results Future Directions
PortaGe — Basic Ideas
the main aim – (semi)automatically generate a scientific webportal for a domain given by initial data
the target group – PhD students, young researchers
long-term interest in the subject
an extension of Google Scholar and CiteSeer services
current search engines – keywords, phrases, documentsimilarity
digital libraries (ACM DL, Springer Link, arxiv.gov) –metainformation – author, journal, conference proceedings,year, . . .
results sorted according to relevance estimations
what “relevant” means in each particular case
![Page 9: Ontology Acquisition for Automatic Building of Scientific ...Ontology Acquisition for Automatic Building of Scientific Portals Pavel Smrˇz1 V´ıt Nov´aˇcek2 1Faculty of Information](https://reader033.vdocument.in/reader033/viewer/2022041712/5e488cc38cbe72002128e9e4/html5/thumbnails/9.jpg)
Introduction Ontologies in PortaGe OLE Preliminary Results Future Directions
PortaGe — Basic Ideas
the main aim – (semi)automatically generate a scientific webportal for a domain given by initial data
the target group – PhD students, young researchers
long-term interest in the subject
an extension of Google Scholar and CiteSeer services
current search engines – keywords, phrases, documentsimilarity
digital libraries (ACM DL, Springer Link, arxiv.gov) –metainformation – author, journal, conference proceedings,year, . . .
results sorted according to relevance estimations
what “relevant” means in each particular case
![Page 10: Ontology Acquisition for Automatic Building of Scientific ...Ontology Acquisition for Automatic Building of Scientific Portals Pavel Smrˇz1 V´ıt Nov´aˇcek2 1Faculty of Information](https://reader033.vdocument.in/reader033/viewer/2022041712/5e488cc38cbe72002128e9e4/html5/thumbnails/10.jpg)
Introduction Ontologies in PortaGe OLE Preliminary Results Future Directions
PortaGe — Basic Ideas
the main aim – (semi)automatically generate a scientific webportal for a domain given by initial data
the target group – PhD students, young researchers
long-term interest in the subject
an extension of Google Scholar and CiteSeer services
current search engines – keywords, phrases, documentsimilarity
digital libraries (ACM DL, Springer Link, arxiv.gov) –metainformation – author, journal, conference proceedings,year, . . .
results sorted according to relevance estimations
what “relevant” means in each particular case
![Page 11: Ontology Acquisition for Automatic Building of Scientific ...Ontology Acquisition for Automatic Building of Scientific Portals Pavel Smrˇz1 V´ıt Nov´aˇcek2 1Faculty of Information](https://reader033.vdocument.in/reader033/viewer/2022041712/5e488cc38cbe72002128e9e4/html5/thumbnails/11.jpg)
Introduction Ontologies in PortaGe OLE Preliminary Results Future Directions
PortaGe — Basic Ideas
the main aim – (semi)automatically generate a scientific webportal for a domain given by initial data
the target group – PhD students, young researchers
long-term interest in the subject
an extension of Google Scholar and CiteSeer services
current search engines – keywords, phrases, documentsimilarity
digital libraries (ACM DL, Springer Link, arxiv.gov) –metainformation – author, journal, conference proceedings,year, . . .
results sorted according to relevance estimations
what “relevant” means in each particular case
![Page 12: Ontology Acquisition for Automatic Building of Scientific ...Ontology Acquisition for Automatic Building of Scientific Portals Pavel Smrˇz1 V´ıt Nov´aˇcek2 1Faculty of Information](https://reader033.vdocument.in/reader033/viewer/2022041712/5e488cc38cbe72002128e9e4/html5/thumbnails/12.jpg)
Introduction Ontologies in PortaGe OLE Preliminary Results Future Directions
PortaGe — Basic Ideas
the main aim – (semi)automatically generate a scientific webportal for a domain given by initial data
the target group – PhD students, young researchers
long-term interest in the subject
an extension of Google Scholar and CiteSeer services
current search engines – keywords, phrases, documentsimilarity
digital libraries (ACM DL, Springer Link, arxiv.gov) –metainformation – author, journal, conference proceedings,year, . . .
results sorted according to relevance estimations
what “relevant” means in each particular case
![Page 13: Ontology Acquisition for Automatic Building of Scientific ...Ontology Acquisition for Automatic Building of Scientific Portals Pavel Smrˇz1 V´ıt Nov´aˇcek2 1Faculty of Information](https://reader033.vdocument.in/reader033/viewer/2022041712/5e488cc38cbe72002128e9e4/html5/thumbnails/13.jpg)
Introduction Ontologies in PortaGe OLE Preliminary Results Future Directions
PortaGe — Basic Ideas
the main aim – (semi)automatically generate a scientific webportal for a domain given by initial data
the target group – PhD students, young researchers
long-term interest in the subject
an extension of Google Scholar and CiteSeer services
current search engines – keywords, phrases, documentsimilarity
digital libraries (ACM DL, Springer Link, arxiv.gov) –metainformation – author, journal, conference proceedings,year, . . .
results sorted according to relevance estimations
what “relevant” means in each particular case
![Page 14: Ontology Acquisition for Automatic Building of Scientific ...Ontology Acquisition for Automatic Building of Scientific Portals Pavel Smrˇz1 V´ıt Nov´aˇcek2 1Faculty of Information](https://reader033.vdocument.in/reader033/viewer/2022041712/5e488cc38cbe72002128e9e4/html5/thumbnails/14.jpg)
Introduction Ontologies in PortaGe OLE Preliminary Results Future Directions
PortaGe — Basic Ideas
the main aim – (semi)automatically generate a scientific webportal for a domain given by initial data
the target group – PhD students, young researchers
long-term interest in the subject
an extension of Google Scholar and CiteSeer services
current search engines – keywords, phrases, documentsimilarity
digital libraries (ACM DL, Springer Link, arxiv.gov) –metainformation – author, journal, conference proceedings,year, . . .
results sorted according to relevance estimations
what “relevant” means in each particular case
![Page 15: Ontology Acquisition for Automatic Building of Scientific ...Ontology Acquisition for Automatic Building of Scientific Portals Pavel Smrˇz1 V´ıt Nov´aˇcek2 1Faculty of Information](https://reader033.vdocument.in/reader033/viewer/2022041712/5e488cc38cbe72002128e9e4/html5/thumbnails/15.jpg)
Introduction Ontologies in PortaGe OLE Preliminary Results Future Directions
PortaGe — Basic Ideas
the main aim – (semi)automatically generate a scientific webportal for a domain given by initial data
the target group – PhD students, young researchers
long-term interest in the subject
an extension of Google Scholar and CiteSeer services
current search engines – keywords, phrases, documentsimilarity
digital libraries (ACM DL, Springer Link, arxiv.gov) –metainformation – author, journal, conference proceedings,year, . . .
results sorted according to relevance estimations
what “relevant” means in each particular case
![Page 16: Ontology Acquisition for Automatic Building of Scientific ...Ontology Acquisition for Automatic Building of Scientific Portals Pavel Smrˇz1 V´ıt Nov´aˇcek2 1Faculty of Information](https://reader033.vdocument.in/reader033/viewer/2022041712/5e488cc38cbe72002128e9e4/html5/thumbnails/16.jpg)
Introduction Ontologies in PortaGe OLE Preliminary Results Future Directions
PortaGe — Basic Ideas
the main aim – (semi)automatically generate a scientific webportal for a domain given by initial data
the target group – PhD students, young researchers
long-term interest in the subject
an extension of Google Scholar and CiteSeer services
current search engines – keywords, phrases, documentsimilarity
digital libraries (ACM DL, Springer Link, arxiv.gov) –metainformation – author, journal, conference proceedings,year, . . .
results sorted according to relevance estimations
what “relevant” means in each particular case
![Page 17: Ontology Acquisition for Automatic Building of Scientific ...Ontology Acquisition for Automatic Building of Scientific Portals Pavel Smrˇz1 V´ıt Nov´aˇcek2 1Faculty of Information](https://reader033.vdocument.in/reader033/viewer/2022041712/5e488cc38cbe72002128e9e4/html5/thumbnails/17.jpg)
Introduction Ontologies in PortaGe OLE Preliminary Results Future Directions
PortaGe — Initial Data
1 keywords, known authors, journals, conferences or projectscharacterizing the subject field
2 seed documents and conference/project web pages relevantfor the current search
3 nodes in a current ontology (can be automatically extractedfrom the given and retrieved documents)
PortaGe combines responses from several information sources:
search results from Google Scholar;
articles and papers found in digital libraries;
information from freely accessible web services;
metainformation on hard-copies (books, journals, proceedings)in the faculty library and other traditional repositories.
![Page 18: Ontology Acquisition for Automatic Building of Scientific ...Ontology Acquisition for Automatic Building of Scientific Portals Pavel Smrˇz1 V´ıt Nov´aˇcek2 1Faculty of Information](https://reader033.vdocument.in/reader033/viewer/2022041712/5e488cc38cbe72002128e9e4/html5/thumbnails/18.jpg)
Introduction Ontologies in PortaGe OLE Preliminary Results Future Directions
PortaGe — Initial Data
1 keywords, known authors, journals, conferences or projectscharacterizing the subject field
2 seed documents and conference/project web pages relevantfor the current search
3 nodes in a current ontology (can be automatically extractedfrom the given and retrieved documents)
PortaGe combines responses from several information sources:
search results from Google Scholar;
articles and papers found in digital libraries;
information from freely accessible web services;
metainformation on hard-copies (books, journals, proceedings)in the faculty library and other traditional repositories.
![Page 19: Ontology Acquisition for Automatic Building of Scientific ...Ontology Acquisition for Automatic Building of Scientific Portals Pavel Smrˇz1 V´ıt Nov´aˇcek2 1Faculty of Information](https://reader033.vdocument.in/reader033/viewer/2022041712/5e488cc38cbe72002128e9e4/html5/thumbnails/19.jpg)
Introduction Ontologies in PortaGe OLE Preliminary Results Future Directions
PortaGe — Initial Data
1 keywords, known authors, journals, conferences or projectscharacterizing the subject field
2 seed documents and conference/project web pages relevantfor the current search
3 nodes in a current ontology (can be automatically extractedfrom the given and retrieved documents)
PortaGe combines responses from several information sources:
search results from Google Scholar;
articles and papers found in digital libraries;
information from freely accessible web services;
metainformation on hard-copies (books, journals, proceedings)in the faculty library and other traditional repositories.
![Page 20: Ontology Acquisition for Automatic Building of Scientific ...Ontology Acquisition for Automatic Building of Scientific Portals Pavel Smrˇz1 V´ıt Nov´aˇcek2 1Faculty of Information](https://reader033.vdocument.in/reader033/viewer/2022041712/5e488cc38cbe72002128e9e4/html5/thumbnails/20.jpg)
Introduction Ontologies in PortaGe OLE Preliminary Results Future Directions
PortaGe — Initial Data
1 keywords, known authors, journals, conferences or projectscharacterizing the subject field
2 seed documents and conference/project web pages relevantfor the current search
3 nodes in a current ontology (can be automatically extractedfrom the given and retrieved documents)
PortaGe combines responses from several information sources:
search results from Google Scholar;
articles and papers found in digital libraries;
information from freely accessible web services;
metainformation on hard-copies (books, journals, proceedings)in the faculty library and other traditional repositories.
![Page 21: Ontology Acquisition for Automatic Building of Scientific ...Ontology Acquisition for Automatic Building of Scientific Portals Pavel Smrˇz1 V´ıt Nov´aˇcek2 1Faculty of Information](https://reader033.vdocument.in/reader033/viewer/2022041712/5e488cc38cbe72002128e9e4/html5/thumbnails/21.jpg)
Introduction Ontologies in PortaGe OLE Preliminary Results Future Directions
PortaGe — Initial Data
1 keywords, known authors, journals, conferences or projectscharacterizing the subject field
2 seed documents and conference/project web pages relevantfor the current search
3 nodes in a current ontology (can be automatically extractedfrom the given and retrieved documents)
PortaGe combines responses from several information sources:
search results from Google Scholar;
articles and papers found in digital libraries;
information from freely accessible web services;
metainformation on hard-copies (books, journals, proceedings)in the faculty library and other traditional repositories.
![Page 22: Ontology Acquisition for Automatic Building of Scientific ...Ontology Acquisition for Automatic Building of Scientific Portals Pavel Smrˇz1 V´ıt Nov´aˇcek2 1Faculty of Information](https://reader033.vdocument.in/reader033/viewer/2022041712/5e488cc38cbe72002128e9e4/html5/thumbnails/22.jpg)
Introduction Ontologies in PortaGe OLE Preliminary Results Future Directions
PortaGe — Initial Data
1 keywords, known authors, journals, conferences or projectscharacterizing the subject field
2 seed documents and conference/project web pages relevantfor the current search
3 nodes in a current ontology (can be automatically extractedfrom the given and retrieved documents)
PortaGe combines responses from several information sources:
search results from Google Scholar;
articles and papers found in digital libraries;
information from freely accessible web services;
metainformation on hard-copies (books, journals, proceedings)in the faculty library and other traditional repositories.
![Page 23: Ontology Acquisition for Automatic Building of Scientific ...Ontology Acquisition for Automatic Building of Scientific Portals Pavel Smrˇz1 V´ıt Nov´aˇcek2 1Faculty of Information](https://reader033.vdocument.in/reader033/viewer/2022041712/5e488cc38cbe72002128e9e4/html5/thumbnails/23.jpg)
Introduction Ontologies in PortaGe OLE Preliminary Results Future Directions
PortaGe — Initial Data
1 keywords, known authors, journals, conferences or projectscharacterizing the subject field
2 seed documents and conference/project web pages relevantfor the current search
3 nodes in a current ontology (can be automatically extractedfrom the given and retrieved documents)
PortaGe combines responses from several information sources:
search results from Google Scholar;
articles and papers found in digital libraries;
information from freely accessible web services;
metainformation on hard-copies (books, journals, proceedings)in the faculty library and other traditional repositories.
![Page 24: Ontology Acquisition for Automatic Building of Scientific ...Ontology Acquisition for Automatic Building of Scientific Portals Pavel Smrˇz1 V´ıt Nov´aˇcek2 1Faculty of Information](https://reader033.vdocument.in/reader033/viewer/2022041712/5e488cc38cbe72002128e9e4/html5/thumbnails/24.jpg)
Introduction Ontologies in PortaGe OLE Preliminary Results Future Directions
PortaGe — Initial Data
1 keywords, known authors, journals, conferences or projectscharacterizing the subject field
2 seed documents and conference/project web pages relevantfor the current search
3 nodes in a current ontology (can be automatically extractedfrom the given and retrieved documents)
PortaGe combines responses from several information sources:
search results from Google Scholar;
articles and papers found in digital libraries;
information from freely accessible web services;
metainformation on hard-copies (books, journals, proceedings)in the faculty library and other traditional repositories.
![Page 25: Ontology Acquisition for Automatic Building of Scientific ...Ontology Acquisition for Automatic Building of Scientific Portals Pavel Smrˇz1 V´ıt Nov´aˇcek2 1Faculty of Information](https://reader033.vdocument.in/reader033/viewer/2022041712/5e488cc38cbe72002128e9e4/html5/thumbnails/25.jpg)
Introduction Ontologies in PortaGe OLE Preliminary Results Future Directions
PortaGe — Initial Data
1 keywords, known authors, journals, conferences or projectscharacterizing the subject field
2 seed documents and conference/project web pages relevantfor the current search
3 nodes in a current ontology (can be automatically extractedfrom the given and retrieved documents)
PortaGe combines responses from several information sources:
search results from Google Scholar;
articles and papers found in digital libraries;
information from freely accessible web services;
metainformation on hard-copies (books, journals, proceedings)in the faculty library and other traditional repositories.
![Page 26: Ontology Acquisition for Automatic Building of Scientific ...Ontology Acquisition for Automatic Building of Scientific Portals Pavel Smrˇz1 V´ıt Nov´aˇcek2 1Faculty of Information](https://reader033.vdocument.in/reader033/viewer/2022041712/5e488cc38cbe72002128e9e4/html5/thumbnails/26.jpg)
Introduction Ontologies in PortaGe OLE Preliminary Results Future Directions
PortaGe — Major Components
text mining for ontology acquisition;
efficient local document classification and indexing;
extraction of metainformation from the documents
citation analysis (provided by CiteSeer)
metasearch in digital libraries
analysis of “Publications” web pages
metadata annotation of web resources
merging of information
continuous search and source-change analysis
portal personalization
![Page 27: Ontology Acquisition for Automatic Building of Scientific ...Ontology Acquisition for Automatic Building of Scientific Portals Pavel Smrˇz1 V´ıt Nov´aˇcek2 1Faculty of Information](https://reader033.vdocument.in/reader033/viewer/2022041712/5e488cc38cbe72002128e9e4/html5/thumbnails/27.jpg)
Introduction Ontologies in PortaGe OLE Preliminary Results Future Directions
PortaGe — Major Components
text mining for ontology acquisition;
efficient local document classification and indexing;
extraction of metainformation from the documents
citation analysis (provided by CiteSeer)
metasearch in digital libraries
analysis of “Publications” web pages
metadata annotation of web resources
merging of information
continuous search and source-change analysis
portal personalization
![Page 28: Ontology Acquisition for Automatic Building of Scientific ...Ontology Acquisition for Automatic Building of Scientific Portals Pavel Smrˇz1 V´ıt Nov´aˇcek2 1Faculty of Information](https://reader033.vdocument.in/reader033/viewer/2022041712/5e488cc38cbe72002128e9e4/html5/thumbnails/28.jpg)
Introduction Ontologies in PortaGe OLE Preliminary Results Future Directions
PortaGe — Major Components
text mining for ontology acquisition;
efficient local document classification and indexing;
extraction of metainformation from the documents
citation analysis (provided by CiteSeer)
metasearch in digital libraries
analysis of “Publications” web pages
metadata annotation of web resources
merging of information
continuous search and source-change analysis
portal personalization
![Page 29: Ontology Acquisition for Automatic Building of Scientific ...Ontology Acquisition for Automatic Building of Scientific Portals Pavel Smrˇz1 V´ıt Nov´aˇcek2 1Faculty of Information](https://reader033.vdocument.in/reader033/viewer/2022041712/5e488cc38cbe72002128e9e4/html5/thumbnails/29.jpg)
Introduction Ontologies in PortaGe OLE Preliminary Results Future Directions
PortaGe — Major Components
text mining for ontology acquisition;
efficient local document classification and indexing;
extraction of metainformation from the documents
citation analysis (provided by CiteSeer)
metasearch in digital libraries
analysis of “Publications” web pages
metadata annotation of web resources
merging of information
continuous search and source-change analysis
portal personalization
![Page 30: Ontology Acquisition for Automatic Building of Scientific ...Ontology Acquisition for Automatic Building of Scientific Portals Pavel Smrˇz1 V´ıt Nov´aˇcek2 1Faculty of Information](https://reader033.vdocument.in/reader033/viewer/2022041712/5e488cc38cbe72002128e9e4/html5/thumbnails/30.jpg)
Introduction Ontologies in PortaGe OLE Preliminary Results Future Directions
PortaGe — Major Components
text mining for ontology acquisition;
efficient local document classification and indexing;
extraction of metainformation from the documents
citation analysis (provided by CiteSeer)
metasearch in digital libraries
analysis of “Publications” web pages
metadata annotation of web resources
merging of information
continuous search and source-change analysis
portal personalization
![Page 31: Ontology Acquisition for Automatic Building of Scientific ...Ontology Acquisition for Automatic Building of Scientific Portals Pavel Smrˇz1 V´ıt Nov´aˇcek2 1Faculty of Information](https://reader033.vdocument.in/reader033/viewer/2022041712/5e488cc38cbe72002128e9e4/html5/thumbnails/31.jpg)
Introduction Ontologies in PortaGe OLE Preliminary Results Future Directions
PortaGe — Major Components
text mining for ontology acquisition;
efficient local document classification and indexing;
extraction of metainformation from the documents
citation analysis (provided by CiteSeer)
metasearch in digital libraries
analysis of “Publications” web pages
metadata annotation of web resources
merging of information
continuous search and source-change analysis
portal personalization
![Page 32: Ontology Acquisition for Automatic Building of Scientific ...Ontology Acquisition for Automatic Building of Scientific Portals Pavel Smrˇz1 V´ıt Nov´aˇcek2 1Faculty of Information](https://reader033.vdocument.in/reader033/viewer/2022041712/5e488cc38cbe72002128e9e4/html5/thumbnails/32.jpg)
Introduction Ontologies in PortaGe OLE Preliminary Results Future Directions
PortaGe — Major Components
text mining for ontology acquisition;
efficient local document classification and indexing;
extraction of metainformation from the documents
citation analysis (provided by CiteSeer)
metasearch in digital libraries
analysis of “Publications” web pages
metadata annotation of web resources
merging of information
continuous search and source-change analysis
portal personalization
![Page 33: Ontology Acquisition for Automatic Building of Scientific ...Ontology Acquisition for Automatic Building of Scientific Portals Pavel Smrˇz1 V´ıt Nov´aˇcek2 1Faculty of Information](https://reader033.vdocument.in/reader033/viewer/2022041712/5e488cc38cbe72002128e9e4/html5/thumbnails/33.jpg)
Introduction Ontologies in PortaGe OLE Preliminary Results Future Directions
PortaGe — Major Components
text mining for ontology acquisition;
efficient local document classification and indexing;
extraction of metainformation from the documents
citation analysis (provided by CiteSeer)
metasearch in digital libraries
analysis of “Publications” web pages
metadata annotation of web resources
merging of information
continuous search and source-change analysis
portal personalization
![Page 34: Ontology Acquisition for Automatic Building of Scientific ...Ontology Acquisition for Automatic Building of Scientific Portals Pavel Smrˇz1 V´ıt Nov´aˇcek2 1Faculty of Information](https://reader033.vdocument.in/reader033/viewer/2022041712/5e488cc38cbe72002128e9e4/html5/thumbnails/34.jpg)
Introduction Ontologies in PortaGe OLE Preliminary Results Future Directions
PortaGe — Major Components
text mining for ontology acquisition;
efficient local document classification and indexing;
extraction of metainformation from the documents
citation analysis (provided by CiteSeer)
metasearch in digital libraries
analysis of “Publications” web pages
metadata annotation of web resources
merging of information
continuous search and source-change analysis
portal personalization
![Page 35: Ontology Acquisition for Automatic Building of Scientific ...Ontology Acquisition for Automatic Building of Scientific Portals Pavel Smrˇz1 V´ıt Nov´aˇcek2 1Faculty of Information](https://reader033.vdocument.in/reader033/viewer/2022041712/5e488cc38cbe72002128e9e4/html5/thumbnails/35.jpg)
Introduction Ontologies in PortaGe OLE Preliminary Results Future Directions
PortaGe — Major Components
text mining for ontology acquisition;
efficient local document classification and indexing;
extraction of metainformation from the documents
citation analysis (provided by CiteSeer)
metasearch in digital libraries
analysis of “Publications” web pages
metadata annotation of web resources
merging of information
continuous search and source-change analysis
portal personalization
![Page 36: Ontology Acquisition for Automatic Building of Scientific ...Ontology Acquisition for Automatic Building of Scientific Portals Pavel Smrˇz1 V´ıt Nov´aˇcek2 1Faculty of Information](https://reader033.vdocument.in/reader033/viewer/2022041712/5e488cc38cbe72002128e9e4/html5/thumbnails/36.jpg)
Introduction Ontologies in PortaGe OLE Preliminary Results Future Directions
PortaGe — Major Components
text mining for ontology acquisition;
efficient local document classification and indexing;
extraction of metainformation from the documents
citation analysis (provided by CiteSeer)
metasearch in digital libraries
analysis of “Publications” web pages
metadata annotation of web resources
merging of information
continuous search and source-change analysis
portal personalization
![Page 37: Ontology Acquisition for Automatic Building of Scientific ...Ontology Acquisition for Automatic Building of Scientific Portals Pavel Smrˇz1 V´ıt Nov´aˇcek2 1Faculty of Information](https://reader033.vdocument.in/reader033/viewer/2022041712/5e488cc38cbe72002128e9e4/html5/thumbnails/37.jpg)
Introduction Ontologies in PortaGe OLE Preliminary Results Future Directions
The Role of Ontologies in PortaGe (1)
The basic role consists in the definition of portal structures.
The core ontology contains concepts of publishers, books and bookseries, journals and their special issues, conferences, conferencetracks workshops, projects, research teams, authors, papers, webpages, etc.
PortaGe supposes that the most of this can be shared amongvarious scientific fields (different disciplines slightly differ in theconceptualisation of their research areas).
For a particular domain, it needs to be extended by individualinstances of journals, conferences, etc.
It is one of the tasks of the ontology extraction engine.
![Page 38: Ontology Acquisition for Automatic Building of Scientific ...Ontology Acquisition for Automatic Building of Scientific Portals Pavel Smrˇz1 V´ıt Nov´aˇcek2 1Faculty of Information](https://reader033.vdocument.in/reader033/viewer/2022041712/5e488cc38cbe72002128e9e4/html5/thumbnails/38.jpg)
Introduction Ontologies in PortaGe OLE Preliminary Results Future Directions
The Role of Ontologies in PortaGe (1)
The basic role consists in the definition of portal structures.
The core ontology contains concepts of publishers, books and bookseries, journals and their special issues, conferences, conferencetracks workshops, projects, research teams, authors, papers, webpages, etc.
PortaGe supposes that the most of this can be shared amongvarious scientific fields (different disciplines slightly differ in theconceptualisation of their research areas).
For a particular domain, it needs to be extended by individualinstances of journals, conferences, etc.
It is one of the tasks of the ontology extraction engine.
![Page 39: Ontology Acquisition for Automatic Building of Scientific ...Ontology Acquisition for Automatic Building of Scientific Portals Pavel Smrˇz1 V´ıt Nov´aˇcek2 1Faculty of Information](https://reader033.vdocument.in/reader033/viewer/2022041712/5e488cc38cbe72002128e9e4/html5/thumbnails/39.jpg)
Introduction Ontologies in PortaGe OLE Preliminary Results Future Directions
The Role of Ontologies in PortaGe (1)
The basic role consists in the definition of portal structures.
The core ontology contains concepts of publishers, books and bookseries, journals and their special issues, conferences, conferencetracks workshops, projects, research teams, authors, papers, webpages, etc.
PortaGe supposes that the most of this can be shared amongvarious scientific fields (different disciplines slightly differ in theconceptualisation of their research areas).
For a particular domain, it needs to be extended by individualinstances of journals, conferences, etc.
It is one of the tasks of the ontology extraction engine.
![Page 40: Ontology Acquisition for Automatic Building of Scientific ...Ontology Acquisition for Automatic Building of Scientific Portals Pavel Smrˇz1 V´ıt Nov´aˇcek2 1Faculty of Information](https://reader033.vdocument.in/reader033/viewer/2022041712/5e488cc38cbe72002128e9e4/html5/thumbnails/40.jpg)
Introduction Ontologies in PortaGe OLE Preliminary Results Future Directions
The Role of Ontologies in PortaGe (1)
The basic role consists in the definition of portal structures.
The core ontology contains concepts of publishers, books and bookseries, journals and their special issues, conferences, conferencetracks workshops, projects, research teams, authors, papers, webpages, etc.
PortaGe supposes that the most of this can be shared amongvarious scientific fields (different disciplines slightly differ in theconceptualisation of their research areas).
For a particular domain, it needs to be extended by individualinstances of journals, conferences, etc.
It is one of the tasks of the ontology extraction engine.
![Page 41: Ontology Acquisition for Automatic Building of Scientific ...Ontology Acquisition for Automatic Building of Scientific Portals Pavel Smrˇz1 V´ıt Nov´aˇcek2 1Faculty of Information](https://reader033.vdocument.in/reader033/viewer/2022041712/5e488cc38cbe72002128e9e4/html5/thumbnails/41.jpg)
Introduction Ontologies in PortaGe OLE Preliminary Results Future Directions
The Role of Ontologies in PortaGe (1)
The basic role consists in the definition of portal structures.
The core ontology contains concepts of publishers, books and bookseries, journals and their special issues, conferences, conferencetracks workshops, projects, research teams, authors, papers, webpages, etc.
PortaGe supposes that the most of this can be shared amongvarious scientific fields (different disciplines slightly differ in theconceptualisation of their research areas).
For a particular domain, it needs to be extended by individualinstances of journals, conferences, etc.
It is one of the tasks of the ontology extraction engine.
![Page 42: Ontology Acquisition for Automatic Building of Scientific ...Ontology Acquisition for Automatic Building of Scientific Portals Pavel Smrˇz1 V´ıt Nov´aˇcek2 1Faculty of Information](https://reader033.vdocument.in/reader033/viewer/2022041712/5e488cc38cbe72002128e9e4/html5/thumbnails/42.jpg)
Introduction Ontologies in PortaGe OLE Preliminary Results Future Directions
The Role of Ontologies in PortaGe (1)
The basic role consists in the definition of portal structures.
The core ontology contains concepts of publishers, books and bookseries, journals and their special issues, conferences, conferencetracks workshops, projects, research teams, authors, papers, webpages, etc.
PortaGe supposes that the most of this can be shared amongvarious scientific fields (different disciplines slightly differ in theconceptualisation of their research areas).
For a particular domain, it needs to be extended by individualinstances of journals, conferences, etc.
It is one of the tasks of the ontology extraction engine.
![Page 43: Ontology Acquisition for Automatic Building of Scientific ...Ontology Acquisition for Automatic Building of Scientific Portals Pavel Smrˇz1 V´ıt Nov´aˇcek2 1Faculty of Information](https://reader033.vdocument.in/reader033/viewer/2022041712/5e488cc38cbe72002128e9e4/html5/thumbnails/43.jpg)
Introduction Ontologies in PortaGe OLE Preliminary Results Future Directions
The Role of Ontologies in PortaGe (2)
Ontologies used to classify the content of documents in PortaGe.
Important especially for very narrow subfields with a limitednumber of documents that can be applied for training of thestandard classifiers.
The automatic classification process can base its decision on theknowledge extracted from other documents in a previous run, suchas the fact that a particular method is used for machine learning inother fields.
![Page 44: Ontology Acquisition for Automatic Building of Scientific ...Ontology Acquisition for Automatic Building of Scientific Portals Pavel Smrˇz1 V´ıt Nov´aˇcek2 1Faculty of Information](https://reader033.vdocument.in/reader033/viewer/2022041712/5e488cc38cbe72002128e9e4/html5/thumbnails/44.jpg)
Introduction Ontologies in PortaGe OLE Preliminary Results Future Directions
The Role of Ontologies in PortaGe (2)
Ontologies used to classify the content of documents in PortaGe.
Important especially for very narrow subfields with a limitednumber of documents that can be applied for training of thestandard classifiers.
The automatic classification process can base its decision on theknowledge extracted from other documents in a previous run, suchas the fact that a particular method is used for machine learning inother fields.
![Page 45: Ontology Acquisition for Automatic Building of Scientific ...Ontology Acquisition for Automatic Building of Scientific Portals Pavel Smrˇz1 V´ıt Nov´aˇcek2 1Faculty of Information](https://reader033.vdocument.in/reader033/viewer/2022041712/5e488cc38cbe72002128e9e4/html5/thumbnails/45.jpg)
Introduction Ontologies in PortaGe OLE Preliminary Results Future Directions
The Role of Ontologies in PortaGe (2)
Ontologies used to classify the content of documents in PortaGe.
Important especially for very narrow subfields with a limitednumber of documents that can be applied for training of thestandard classifiers.
The automatic classification process can base its decision on theknowledge extracted from other documents in a previous run, suchas the fact that a particular method is used for machine learning inother fields.
![Page 46: Ontology Acquisition for Automatic Building of Scientific ...Ontology Acquisition for Automatic Building of Scientific Portals Pavel Smrˇz1 V´ıt Nov´aˇcek2 1Faculty of Information](https://reader033.vdocument.in/reader033/viewer/2022041712/5e488cc38cbe72002128e9e4/html5/thumbnails/46.jpg)
Introduction Ontologies in PortaGe OLE Preliminary Results Future Directions
The Role of Ontologies in PortaGe (2)
Ontologies used to classify the content of documents in PortaGe.
Important especially for very narrow subfields with a limitednumber of documents that can be applied for training of thestandard classifiers.
The automatic classification process can base its decision on theknowledge extracted from other documents in a previous run, suchas the fact that a particular method is used for machine learning inother fields.
![Page 47: Ontology Acquisition for Automatic Building of Scientific ...Ontology Acquisition for Automatic Building of Scientific Portals Pavel Smrˇz1 V´ıt Nov´aˇcek2 1Faculty of Information](https://reader033.vdocument.in/reader033/viewer/2022041712/5e488cc38cbe72002128e9e4/html5/thumbnails/47.jpg)
Introduction Ontologies in PortaGe OLE Preliminary Results Future Directions
The Role of Ontologies in PortaGe (3)
Ontologies provide mechanisms for context specification.
Users can restrict the search for documents reflecting certainsemantic relations based on the ontology, e.g. limit the output tothe documents discussing “context-free grammars” as a “tool-for”“analysis of protein sequences”.
The OLE framework interlinks individual pieces of such knowledgewith lexico-syntactic patterns able to identify the relations in theretrieved documents.
![Page 48: Ontology Acquisition for Automatic Building of Scientific ...Ontology Acquisition for Automatic Building of Scientific Portals Pavel Smrˇz1 V´ıt Nov´aˇcek2 1Faculty of Information](https://reader033.vdocument.in/reader033/viewer/2022041712/5e488cc38cbe72002128e9e4/html5/thumbnails/48.jpg)
Introduction Ontologies in PortaGe OLE Preliminary Results Future Directions
The Role of Ontologies in PortaGe (3)
Ontologies provide mechanisms for context specification.
Users can restrict the search for documents reflecting certainsemantic relations based on the ontology, e.g. limit the output tothe documents discussing “context-free grammars” as a “tool-for”“analysis of protein sequences”.
The OLE framework interlinks individual pieces of such knowledgewith lexico-syntactic patterns able to identify the relations in theretrieved documents.
![Page 49: Ontology Acquisition for Automatic Building of Scientific ...Ontology Acquisition for Automatic Building of Scientific Portals Pavel Smrˇz1 V´ıt Nov´aˇcek2 1Faculty of Information](https://reader033.vdocument.in/reader033/viewer/2022041712/5e488cc38cbe72002128e9e4/html5/thumbnails/49.jpg)
Introduction Ontologies in PortaGe OLE Preliminary Results Future Directions
The Role of Ontologies in PortaGe (3)
Ontologies provide mechanisms for context specification.
Users can restrict the search for documents reflecting certainsemantic relations based on the ontology, e.g. limit the output tothe documents discussing “context-free grammars” as a “tool-for”“analysis of protein sequences”.
The OLE framework interlinks individual pieces of such knowledgewith lexico-syntactic patterns able to identify the relations in theretrieved documents.
![Page 50: Ontology Acquisition for Automatic Building of Scientific ...Ontology Acquisition for Automatic Building of Scientific Portals Pavel Smrˇz1 V´ıt Nov´aˇcek2 1Faculty of Information](https://reader033.vdocument.in/reader033/viewer/2022041712/5e488cc38cbe72002128e9e4/html5/thumbnails/50.jpg)
Introduction Ontologies in PortaGe OLE Preliminary Results Future Directions
The Role of Ontologies in PortaGe (3)
Ontologies provide mechanisms for context specification.
Users can restrict the search for documents reflecting certainsemantic relations based on the ontology, e.g. limit the output tothe documents discussing “context-free grammars” as a “tool-for”“analysis of protein sequences”.
The OLE framework interlinks individual pieces of such knowledgewith lexico-syntactic patterns able to identify the relations in theretrieved documents.
![Page 51: Ontology Acquisition for Automatic Building of Scientific ...Ontology Acquisition for Automatic Building of Scientific Portals Pavel Smrˇz1 V´ıt Nov´aˇcek2 1Faculty of Information](https://reader033.vdocument.in/reader033/viewer/2022041712/5e488cc38cbe72002128e9e4/html5/thumbnails/51.jpg)
Introduction Ontologies in PortaGe OLE Preliminary Results Future Directions
The Role of Ontologies in PortaGe (4)
Ontologies in personalization of multi-user portals.
User profiles define rules to identify “the best” information for anindividual user. A novice (in the given research domain) can askfor introductory documents, others prefer new information (thedocuments that appeared/were found in the last month), need ageneral summary of used methods (usually the most referenceddocuments), or focus on the relevance only.
The user profiles and the ontologies also cover the availability ofthe resources for a particular user, user-specified amount ofdocuments that should be presented and processing timerequirements.
![Page 52: Ontology Acquisition for Automatic Building of Scientific ...Ontology Acquisition for Automatic Building of Scientific Portals Pavel Smrˇz1 V´ıt Nov´aˇcek2 1Faculty of Information](https://reader033.vdocument.in/reader033/viewer/2022041712/5e488cc38cbe72002128e9e4/html5/thumbnails/52.jpg)
Introduction Ontologies in PortaGe OLE Preliminary Results Future Directions
The Role of Ontologies in PortaGe (4)
Ontologies in personalization of multi-user portals.
User profiles define rules to identify “the best” information for anindividual user. A novice (in the given research domain) can askfor introductory documents, others prefer new information (thedocuments that appeared/were found in the last month), need ageneral summary of used methods (usually the most referenceddocuments), or focus on the relevance only.
The user profiles and the ontologies also cover the availability ofthe resources for a particular user, user-specified amount ofdocuments that should be presented and processing timerequirements.
![Page 53: Ontology Acquisition for Automatic Building of Scientific ...Ontology Acquisition for Automatic Building of Scientific Portals Pavel Smrˇz1 V´ıt Nov´aˇcek2 1Faculty of Information](https://reader033.vdocument.in/reader033/viewer/2022041712/5e488cc38cbe72002128e9e4/html5/thumbnails/53.jpg)
Introduction Ontologies in PortaGe OLE Preliminary Results Future Directions
The Role of Ontologies in PortaGe (4)
Ontologies in personalization of multi-user portals.
User profiles define rules to identify “the best” information for anindividual user. A novice (in the given research domain) can askfor introductory documents, others prefer new information (thedocuments that appeared/were found in the last month), need ageneral summary of used methods (usually the most referenceddocuments), or focus on the relevance only.
The user profiles and the ontologies also cover the availability ofthe resources for a particular user, user-specified amount ofdocuments that should be presented and processing timerequirements.
![Page 54: Ontology Acquisition for Automatic Building of Scientific ...Ontology Acquisition for Automatic Building of Scientific Portals Pavel Smrˇz1 V´ıt Nov´aˇcek2 1Faculty of Information](https://reader033.vdocument.in/reader033/viewer/2022041712/5e488cc38cbe72002128e9e4/html5/thumbnails/54.jpg)
Introduction Ontologies in PortaGe OLE Preliminary Results Future Directions
The Role of Ontologies in PortaGe (4)
Ontologies in personalization of multi-user portals.
User profiles define rules to identify “the best” information for anindividual user. A novice (in the given research domain) can askfor introductory documents, others prefer new information (thedocuments that appeared/were found in the last month), need ageneral summary of used methods (usually the most referenceddocuments), or focus on the relevance only.
The user profiles and the ontologies also cover the availability ofthe resources for a particular user, user-specified amount ofdocuments that should be presented and processing timerequirements.
![Page 55: Ontology Acquisition for Automatic Building of Scientific ...Ontology Acquisition for Automatic Building of Scientific Portals Pavel Smrˇz1 V´ıt Nov´aˇcek2 1Faculty of Information](https://reader033.vdocument.in/reader033/viewer/2022041712/5e488cc38cbe72002128e9e4/html5/thumbnails/55.jpg)
Introduction Ontologies in PortaGe OLE Preliminary Results Future Directions
Basic Requirements
The process of ontology acquisition should run without anyneed of human assistance. On the other hand, the user mustbe able to influence the learning, refine the extracted, selectrelevant information and modify the stored data manually.
The amount of the processed resources can be very high(thousands of documents). The implementation of theontology learning must be computationally efficient androbust.
The produced ontologies must reflect the stepwisedevelopment of the PortaGe system. If there is no currentneed for a particular kind of knowledge, the extraction shouldbe postponed to later phases.
![Page 56: Ontology Acquisition for Automatic Building of Scientific ...Ontology Acquisition for Automatic Building of Scientific Portals Pavel Smrˇz1 V´ıt Nov´aˇcek2 1Faculty of Information](https://reader033.vdocument.in/reader033/viewer/2022041712/5e488cc38cbe72002128e9e4/html5/thumbnails/56.jpg)
Introduction Ontologies in PortaGe OLE Preliminary Results Future Directions
Basic Requirements
The process of ontology acquisition should run without anyneed of human assistance. On the other hand, the user mustbe able to influence the learning, refine the extracted, selectrelevant information and modify the stored data manually.
The amount of the processed resources can be very high(thousands of documents). The implementation of theontology learning must be computationally efficient androbust.
The produced ontologies must reflect the stepwisedevelopment of the PortaGe system. If there is no currentneed for a particular kind of knowledge, the extraction shouldbe postponed to later phases.
![Page 57: Ontology Acquisition for Automatic Building of Scientific ...Ontology Acquisition for Automatic Building of Scientific Portals Pavel Smrˇz1 V´ıt Nov´aˇcek2 1Faculty of Information](https://reader033.vdocument.in/reader033/viewer/2022041712/5e488cc38cbe72002128e9e4/html5/thumbnails/57.jpg)
Introduction Ontologies in PortaGe OLE Preliminary Results Future Directions
Basic Requirements
The process of ontology acquisition should run without anyneed of human assistance. On the other hand, the user mustbe able to influence the learning, refine the extracted, selectrelevant information and modify the stored data manually.
The amount of the processed resources can be very high(thousands of documents). The implementation of theontology learning must be computationally efficient androbust.
The produced ontologies must reflect the stepwisedevelopment of the PortaGe system. If there is no currentneed for a particular kind of knowledge, the extraction shouldbe postponed to later phases.
![Page 58: Ontology Acquisition for Automatic Building of Scientific ...Ontology Acquisition for Automatic Building of Scientific Portals Pavel Smrˇz1 V´ıt Nov´aˇcek2 1Faculty of Information](https://reader033.vdocument.in/reader033/viewer/2022041712/5e488cc38cbe72002128e9e4/html5/thumbnails/58.jpg)
Introduction Ontologies in PortaGe OLE Preliminary Results Future Directions
Basic Requirements
The process of ontology acquisition should run without anyneed of human assistance. On the other hand, the user mustbe able to influence the learning, refine the extracted, selectrelevant information and modify the stored data manually.
The amount of the processed resources can be very high(thousands of documents). The implementation of theontology learning must be computationally efficient androbust.
The produced ontologies must reflect the stepwisedevelopment of the PortaGe system. If there is no currentneed for a particular kind of knowledge, the extraction shouldbe postponed to later phases.
![Page 59: Ontology Acquisition for Automatic Building of Scientific ...Ontology Acquisition for Automatic Building of Scientific Portals Pavel Smrˇz1 V´ıt Nov´aˇcek2 1Faculty of Information](https://reader033.vdocument.in/reader033/viewer/2022041712/5e488cc38cbe72002128e9e4/html5/thumbnails/59.jpg)
Introduction Ontologies in PortaGe OLE Preliminary Results Future Directions
OLE Design
OLE (Ontology LEarning) system for knowledge acquisition andmanagement addresses the following issues:
iterative construction and maintenance of respectiveontologies;
explicit uncertainty representation;
automatic inference of latent knowledge;
QA interface for querying data stored in ontologies.
Core functionality:
extraction and efficient storage of domain concepts, conceptclusters and their mutual relations;
semantic searching and querying stored data;
visualization of conceptual structures;
inference of implicit domain knowledge.
![Page 60: Ontology Acquisition for Automatic Building of Scientific ...Ontology Acquisition for Automatic Building of Scientific Portals Pavel Smrˇz1 V´ıt Nov´aˇcek2 1Faculty of Information](https://reader033.vdocument.in/reader033/viewer/2022041712/5e488cc38cbe72002128e9e4/html5/thumbnails/60.jpg)
Introduction Ontologies in PortaGe OLE Preliminary Results Future Directions
OLE Design
OLE (Ontology LEarning) system for knowledge acquisition andmanagement addresses the following issues:
iterative construction and maintenance of respectiveontologies;
explicit uncertainty representation;
automatic inference of latent knowledge;
QA interface for querying data stored in ontologies.
Core functionality:
extraction and efficient storage of domain concepts, conceptclusters and their mutual relations;
semantic searching and querying stored data;
visualization of conceptual structures;
inference of implicit domain knowledge.
![Page 61: Ontology Acquisition for Automatic Building of Scientific ...Ontology Acquisition for Automatic Building of Scientific Portals Pavel Smrˇz1 V´ıt Nov´aˇcek2 1Faculty of Information](https://reader033.vdocument.in/reader033/viewer/2022041712/5e488cc38cbe72002128e9e4/html5/thumbnails/61.jpg)
Introduction Ontologies in PortaGe OLE Preliminary Results Future Directions
OLE Design
OLE (Ontology LEarning) system for knowledge acquisition andmanagement addresses the following issues:
iterative construction and maintenance of respectiveontologies;
explicit uncertainty representation;
automatic inference of latent knowledge;
QA interface for querying data stored in ontologies.
Core functionality:
extraction and efficient storage of domain concepts, conceptclusters and their mutual relations;
semantic searching and querying stored data;
visualization of conceptual structures;
inference of implicit domain knowledge.
![Page 62: Ontology Acquisition for Automatic Building of Scientific ...Ontology Acquisition for Automatic Building of Scientific Portals Pavel Smrˇz1 V´ıt Nov´aˇcek2 1Faculty of Information](https://reader033.vdocument.in/reader033/viewer/2022041712/5e488cc38cbe72002128e9e4/html5/thumbnails/62.jpg)
Introduction Ontologies in PortaGe OLE Preliminary Results Future Directions
OLE Design
OLE (Ontology LEarning) system for knowledge acquisition andmanagement addresses the following issues:
iterative construction and maintenance of respectiveontologies;
explicit uncertainty representation;
automatic inference of latent knowledge;
QA interface for querying data stored in ontologies.
Core functionality:
extraction and efficient storage of domain concepts, conceptclusters and their mutual relations;
semantic searching and querying stored data;
visualization of conceptual structures;
inference of implicit domain knowledge.
![Page 63: Ontology Acquisition for Automatic Building of Scientific ...Ontology Acquisition for Automatic Building of Scientific Portals Pavel Smrˇz1 V´ıt Nov´aˇcek2 1Faculty of Information](https://reader033.vdocument.in/reader033/viewer/2022041712/5e488cc38cbe72002128e9e4/html5/thumbnails/63.jpg)
Introduction Ontologies in PortaGe OLE Preliminary Results Future Directions
OLE Design
OLE (Ontology LEarning) system for knowledge acquisition andmanagement addresses the following issues:
iterative construction and maintenance of respectiveontologies;
explicit uncertainty representation;
automatic inference of latent knowledge;
QA interface for querying data stored in ontologies.
Core functionality:
extraction and efficient storage of domain concepts, conceptclusters and their mutual relations;
semantic searching and querying stored data;
visualization of conceptual structures;
inference of implicit domain knowledge.
![Page 64: Ontology Acquisition for Automatic Building of Scientific ...Ontology Acquisition for Automatic Building of Scientific Portals Pavel Smrˇz1 V´ıt Nov´aˇcek2 1Faculty of Information](https://reader033.vdocument.in/reader033/viewer/2022041712/5e488cc38cbe72002128e9e4/html5/thumbnails/64.jpg)
Introduction Ontologies in PortaGe OLE Preliminary Results Future Directions
OLE Design
OLE (Ontology LEarning) system for knowledge acquisition andmanagement addresses the following issues:
iterative construction and maintenance of respectiveontologies;
explicit uncertainty representation;
automatic inference of latent knowledge;
QA interface for querying data stored in ontologies.
Core functionality:
extraction and efficient storage of domain concepts, conceptclusters and their mutual relations;
semantic searching and querying stored data;
visualization of conceptual structures;
inference of implicit domain knowledge.
![Page 65: Ontology Acquisition for Automatic Building of Scientific ...Ontology Acquisition for Automatic Building of Scientific Portals Pavel Smrˇz1 V´ıt Nov´aˇcek2 1Faculty of Information](https://reader033.vdocument.in/reader033/viewer/2022041712/5e488cc38cbe72002128e9e4/html5/thumbnails/65.jpg)
Introduction Ontologies in PortaGe OLE Preliminary Results Future Directions
OLE Design
OLE (Ontology LEarning) system for knowledge acquisition andmanagement addresses the following issues:
iterative construction and maintenance of respectiveontologies;
explicit uncertainty representation;
automatic inference of latent knowledge;
QA interface for querying data stored in ontologies.
Core functionality:
extraction and efficient storage of domain concepts, conceptclusters and their mutual relations;
semantic searching and querying stored data;
visualization of conceptual structures;
inference of implicit domain knowledge.
![Page 66: Ontology Acquisition for Automatic Building of Scientific ...Ontology Acquisition for Automatic Building of Scientific Portals Pavel Smrˇz1 V´ıt Nov´aˇcek2 1Faculty of Information](https://reader033.vdocument.in/reader033/viewer/2022041712/5e488cc38cbe72002128e9e4/html5/thumbnails/66.jpg)
Introduction Ontologies in PortaGe OLE Preliminary Results Future Directions
OLE Design
OLE (Ontology LEarning) system for knowledge acquisition andmanagement addresses the following issues:
iterative construction and maintenance of respectiveontologies;
explicit uncertainty representation;
automatic inference of latent knowledge;
QA interface for querying data stored in ontologies.
Core functionality:
extraction and efficient storage of domain concepts, conceptclusters and their mutual relations;
semantic searching and querying stored data;
visualization of conceptual structures;
inference of implicit domain knowledge.
![Page 67: Ontology Acquisition for Automatic Building of Scientific ...Ontology Acquisition for Automatic Building of Scientific Portals Pavel Smrˇz1 V´ıt Nov´aˇcek2 1Faculty of Information](https://reader033.vdocument.in/reader033/viewer/2022041712/5e488cc38cbe72002128e9e4/html5/thumbnails/67.jpg)
Introduction Ontologies in PortaGe OLE Preliminary Results Future Directions
OLE Design
OLE (Ontology LEarning) system for knowledge acquisition andmanagement addresses the following issues:
iterative construction and maintenance of respectiveontologies;
explicit uncertainty representation;
automatic inference of latent knowledge;
QA interface for querying data stored in ontologies.
Core functionality:
extraction and efficient storage of domain concepts, conceptclusters and their mutual relations;
semantic searching and querying stored data;
visualization of conceptual structures;
inference of implicit domain knowledge.
![Page 68: Ontology Acquisition for Automatic Building of Scientific ...Ontology Acquisition for Automatic Building of Scientific Portals Pavel Smrˇz1 V´ıt Nov´aˇcek2 1Faculty of Information](https://reader033.vdocument.in/reader033/viewer/2022041712/5e488cc38cbe72002128e9e4/html5/thumbnails/68.jpg)
Introduction Ontologies in PortaGe OLE Preliminary Results Future Directions
OLE Design
OLE (Ontology LEarning) system for knowledge acquisition andmanagement addresses the following issues:
iterative construction and maintenance of respectiveontologies;
explicit uncertainty representation;
automatic inference of latent knowledge;
QA interface for querying data stored in ontologies.
Core functionality:
extraction and efficient storage of domain concepts, conceptclusters and their mutual relations;
semantic searching and querying stored data;
visualization of conceptual structures;
inference of implicit domain knowledge.
![Page 69: Ontology Acquisition for Automatic Building of Scientific ...Ontology Acquisition for Automatic Building of Scientific Portals Pavel Smrˇz1 V´ıt Nov´aˇcek2 1Faculty of Information](https://reader033.vdocument.in/reader033/viewer/2022041712/5e488cc38cbe72002128e9e4/html5/thumbnails/69.jpg)
Introduction Ontologies in PortaGe OLE Preliminary Results Future Directions
OLE Design
OLE (Ontology LEarning) system for knowledge acquisition andmanagement addresses the following issues:
iterative construction and maintenance of respectiveontologies;
explicit uncertainty representation;
automatic inference of latent knowledge;
QA interface for querying data stored in ontologies.
Core functionality:
extraction and efficient storage of domain concepts, conceptclusters and their mutual relations;
semantic searching and querying stored data;
visualization of conceptual structures;
inference of implicit domain knowledge.
![Page 70: Ontology Acquisition for Automatic Building of Scientific ...Ontology Acquisition for Automatic Building of Scientific Portals Pavel Smrˇz1 V´ıt Nov´aˇcek2 1Faculty of Information](https://reader033.vdocument.in/reader033/viewer/2022041712/5e488cc38cbe72002128e9e4/html5/thumbnails/70.jpg)
Introduction Ontologies in PortaGe OLE Preliminary Results Future Directions
OLE Architecture
![Page 71: Ontology Acquisition for Automatic Building of Scientific ...Ontology Acquisition for Automatic Building of Scientific Portals Pavel Smrˇz1 V´ıt Nov´aˇcek2 1Faculty of Information](https://reader033.vdocument.in/reader033/viewer/2022041712/5e488cc38cbe72002128e9e4/html5/thumbnails/71.jpg)
Introduction Ontologies in PortaGe OLE Preliminary Results Future Directions
OLE Architecture
![Page 72: Ontology Acquisition for Automatic Building of Scientific ...Ontology Acquisition for Automatic Building of Scientific Portals Pavel Smrˇz1 V´ıt Nov´aˇcek2 1Faculty of Information](https://reader033.vdocument.in/reader033/viewer/2022041712/5e488cc38cbe72002128e9e4/html5/thumbnails/72.jpg)
Introduction Ontologies in PortaGe OLE Preliminary Results Future Directions
OLITE Work Flow
Resource is a structured (XML, HTML) or unstructured(plain text) file.
Preprocessor incorporates generic NLP tasks such astokenization, POS tagging and chunking.
Extraction plug-ins provide submodules implementingvarious extraction techniques.
Miniontology covers the concepts and their relationsidentified in the respective resource.
![Page 73: Ontology Acquisition for Automatic Building of Scientific ...Ontology Acquisition for Automatic Building of Scientific Portals Pavel Smrˇz1 V´ıt Nov´aˇcek2 1Faculty of Information](https://reader033.vdocument.in/reader033/viewer/2022041712/5e488cc38cbe72002128e9e4/html5/thumbnails/73.jpg)
Introduction Ontologies in PortaGe OLE Preliminary Results Future Directions
OLITE Work Flow
Resource is a structured (XML, HTML) or unstructured(plain text) file.
Preprocessor incorporates generic NLP tasks such astokenization, POS tagging and chunking.
Extraction plug-ins provide submodules implementingvarious extraction techniques.
Miniontology covers the concepts and their relationsidentified in the respective resource.
![Page 74: Ontology Acquisition for Automatic Building of Scientific ...Ontology Acquisition for Automatic Building of Scientific Portals Pavel Smrˇz1 V´ıt Nov´aˇcek2 1Faculty of Information](https://reader033.vdocument.in/reader033/viewer/2022041712/5e488cc38cbe72002128e9e4/html5/thumbnails/74.jpg)
Introduction Ontologies in PortaGe OLE Preliminary Results Future Directions
OLITE Work Flow
Resource is a structured (XML, HTML) or unstructured(plain text) file.
Preprocessor incorporates generic NLP tasks such astokenization, POS tagging and chunking.
Extraction plug-ins provide submodules implementingvarious extraction techniques.
Miniontology covers the concepts and their relationsidentified in the respective resource.
![Page 75: Ontology Acquisition for Automatic Building of Scientific ...Ontology Acquisition for Automatic Building of Scientific Portals Pavel Smrˇz1 V´ıt Nov´aˇcek2 1Faculty of Information](https://reader033.vdocument.in/reader033/viewer/2022041712/5e488cc38cbe72002128e9e4/html5/thumbnails/75.jpg)
Introduction Ontologies in PortaGe OLE Preliminary Results Future Directions
OLITE Work Flow
Resource is a structured (XML, HTML) or unstructured(plain text) file.
Preprocessor incorporates generic NLP tasks such astokenization, POS tagging and chunking.
Extraction plug-ins provide submodules implementingvarious extraction techniques.
Miniontology covers the concepts and their relationsidentified in the respective resource.
![Page 76: Ontology Acquisition for Automatic Building of Scientific ...Ontology Acquisition for Automatic Building of Scientific Portals Pavel Smrˇz1 V´ıt Nov´aˇcek2 1Faculty of Information](https://reader033.vdocument.in/reader033/viewer/2022041712/5e488cc38cbe72002128e9e4/html5/thumbnails/76.jpg)
Introduction Ontologies in PortaGe OLE Preliminary Results Future Directions
OLITE Work Flow
Resource is a structured (XML, HTML) or unstructured(plain text) file.
Preprocessor incorporates generic NLP tasks such astokenization, POS tagging and chunking.
Extraction plug-ins provide submodules implementingvarious extraction techniques.
Miniontology covers the concepts and their relationsidentified in the respective resource.
![Page 77: Ontology Acquisition for Automatic Building of Scientific ...Ontology Acquisition for Automatic Building of Scientific Portals Pavel Smrˇz1 V´ıt Nov´aˇcek2 1Faculty of Information](https://reader033.vdocument.in/reader033/viewer/2022041712/5e488cc38cbe72002128e9e4/html5/thumbnails/77.jpg)
Introduction Ontologies in PortaGe OLE Preliminary Results Future Directions
OLITE Functional Components
Tools supporting cross-language applicability:
resource reader interfacetagger trainechunker trainer
Preprocessor
Language-specific analysis support
Extraction core with modular plug-in interface
Plug-ins of particular extraction methods
![Page 78: Ontology Acquisition for Automatic Building of Scientific ...Ontology Acquisition for Automatic Building of Scientific Portals Pavel Smrˇz1 V´ıt Nov´aˇcek2 1Faculty of Information](https://reader033.vdocument.in/reader033/viewer/2022041712/5e488cc38cbe72002128e9e4/html5/thumbnails/78.jpg)
Introduction Ontologies in PortaGe OLE Preliminary Results Future Directions
OLITE Functional Components
Tools supporting cross-language applicability:
resource reader interfacetagger trainechunker trainer
Preprocessor
Language-specific analysis support
Extraction core with modular plug-in interface
Plug-ins of particular extraction methods
![Page 79: Ontology Acquisition for Automatic Building of Scientific ...Ontology Acquisition for Automatic Building of Scientific Portals Pavel Smrˇz1 V´ıt Nov´aˇcek2 1Faculty of Information](https://reader033.vdocument.in/reader033/viewer/2022041712/5e488cc38cbe72002128e9e4/html5/thumbnails/79.jpg)
Introduction Ontologies in PortaGe OLE Preliminary Results Future Directions
OLITE Functional Components
Tools supporting cross-language applicability:
resource reader interfacetagger trainechunker trainer
Preprocessor
Language-specific analysis support
Extraction core with modular plug-in interface
Plug-ins of particular extraction methods
![Page 80: Ontology Acquisition for Automatic Building of Scientific ...Ontology Acquisition for Automatic Building of Scientific Portals Pavel Smrˇz1 V´ıt Nov´aˇcek2 1Faculty of Information](https://reader033.vdocument.in/reader033/viewer/2022041712/5e488cc38cbe72002128e9e4/html5/thumbnails/80.jpg)
Introduction Ontologies in PortaGe OLE Preliminary Results Future Directions
OLITE Functional Components
Tools supporting cross-language applicability:
resource reader interfacetagger trainechunker trainer
Preprocessor
Language-specific analysis support
Extraction core with modular plug-in interface
Plug-ins of particular extraction methods
![Page 81: Ontology Acquisition for Automatic Building of Scientific ...Ontology Acquisition for Automatic Building of Scientific Portals Pavel Smrˇz1 V´ıt Nov´aˇcek2 1Faculty of Information](https://reader033.vdocument.in/reader033/viewer/2022041712/5e488cc38cbe72002128e9e4/html5/thumbnails/81.jpg)
Introduction Ontologies in PortaGe OLE Preliminary Results Future Directions
OLITE Functional Components
Tools supporting cross-language applicability:
resource reader interfacetagger trainechunker trainer
Preprocessor
Language-specific analysis support
Extraction core with modular plug-in interface
Plug-ins of particular extraction methods
![Page 82: Ontology Acquisition for Automatic Building of Scientific ...Ontology Acquisition for Automatic Building of Scientific Portals Pavel Smrˇz1 V´ıt Nov´aˇcek2 1Faculty of Information](https://reader033.vdocument.in/reader033/viewer/2022041712/5e488cc38cbe72002128e9e4/html5/thumbnails/82.jpg)
Introduction Ontologies in PortaGe OLE Preliminary Results Future Directions
OLITE Functional Components
Tools supporting cross-language applicability:
resource reader interfacetagger trainechunker trainer
Preprocessor
Language-specific analysis support
Extraction core with modular plug-in interface
Plug-ins of particular extraction methods
![Page 83: Ontology Acquisition for Automatic Building of Scientific ...Ontology Acquisition for Automatic Building of Scientific Portals Pavel Smrˇz1 V´ıt Nov´aˇcek2 1Faculty of Information](https://reader033.vdocument.in/reader033/viewer/2022041712/5e488cc38cbe72002128e9e4/html5/thumbnails/83.jpg)
Introduction Ontologies in PortaGe OLE Preliminary Results Future Directions
Cross-Language Applicability Tools
Resource reader interface implements a set of transformationsto convert the resource to the internal format.
Tagger trainer employs a tagged corpus to create a respectivePOS tagger.
Chunker trainer employs a treebank-like corpus to learn howto chunk the input tagged sentences.
![Page 84: Ontology Acquisition for Automatic Building of Scientific ...Ontology Acquisition for Automatic Building of Scientific Portals Pavel Smrˇz1 V´ıt Nov´aˇcek2 1Faculty of Information](https://reader033.vdocument.in/reader033/viewer/2022041712/5e488cc38cbe72002128e9e4/html5/thumbnails/84.jpg)
Introduction Ontologies in PortaGe OLE Preliminary Results Future Directions
Cross-Language Applicability Tools
Resource reader interface implements a set of transformationsto convert the resource to the internal format.
Tagger trainer employs a tagged corpus to create a respectivePOS tagger.
Chunker trainer employs a treebank-like corpus to learn howto chunk the input tagged sentences.
![Page 85: Ontology Acquisition for Automatic Building of Scientific ...Ontology Acquisition for Automatic Building of Scientific Portals Pavel Smrˇz1 V´ıt Nov´aˇcek2 1Faculty of Information](https://reader033.vdocument.in/reader033/viewer/2022041712/5e488cc38cbe72002128e9e4/html5/thumbnails/85.jpg)
Introduction Ontologies in PortaGe OLE Preliminary Results Future Directions
Cross-Language Applicability Tools
Resource reader interface implements a set of transformationsto convert the resource to the internal format.
Tagger trainer employs a tagged corpus to create a respectivePOS tagger.
Chunker trainer employs a treebank-like corpus to learn howto chunk the input tagged sentences.
![Page 86: Ontology Acquisition for Automatic Building of Scientific ...Ontology Acquisition for Automatic Building of Scientific Portals Pavel Smrˇz1 V´ıt Nov´aˇcek2 1Faculty of Information](https://reader033.vdocument.in/reader033/viewer/2022041712/5e488cc38cbe72002128e9e4/html5/thumbnails/86.jpg)
Introduction Ontologies in PortaGe OLE Preliminary Results Future Directions
Cross-Language Applicability Tools
Resource reader interface implements a set of transformationsto convert the resource to the internal format.
Tagger trainer employs a tagged corpus to create a respectivePOS tagger.
Chunker trainer employs a treebank-like corpus to learn howto chunk the input tagged sentences.
![Page 87: Ontology Acquisition for Automatic Building of Scientific ...Ontology Acquisition for Automatic Building of Scientific Portals Pavel Smrˇz1 V´ıt Nov´aˇcek2 1Faculty of Information](https://reader033.vdocument.in/reader033/viewer/2022041712/5e488cc38cbe72002128e9e4/html5/thumbnails/87.jpg)
Introduction Ontologies in PortaGe OLE Preliminary Results Future Directions
Preprocessing and Language-Specific Analysis Support
The preprocessing of the input goes through several phases:
1 splitting the raw text into sentences and elimination ofirrelevant ones;
2 text tokenization;
3 POS tagging (Brill, stochastic, rule-based + unknown words);
4 chunking, esp. noun phrases (rule-based).
Language and Domain-Specific Analysis Support:
additional regular expressions for chunk parsing – keywordidentification
terminological dictionaries
WSD resources
![Page 88: Ontology Acquisition for Automatic Building of Scientific ...Ontology Acquisition for Automatic Building of Scientific Portals Pavel Smrˇz1 V´ıt Nov´aˇcek2 1Faculty of Information](https://reader033.vdocument.in/reader033/viewer/2022041712/5e488cc38cbe72002128e9e4/html5/thumbnails/88.jpg)
Introduction Ontologies in PortaGe OLE Preliminary Results Future Directions
Preprocessing and Language-Specific Analysis Support
The preprocessing of the input goes through several phases:
1 splitting the raw text into sentences and elimination ofirrelevant ones;
2 text tokenization;
3 POS tagging (Brill, stochastic, rule-based + unknown words);
4 chunking, esp. noun phrases (rule-based).
Language and Domain-Specific Analysis Support:
additional regular expressions for chunk parsing – keywordidentification
terminological dictionaries
WSD resources
![Page 89: Ontology Acquisition for Automatic Building of Scientific ...Ontology Acquisition for Automatic Building of Scientific Portals Pavel Smrˇz1 V´ıt Nov´aˇcek2 1Faculty of Information](https://reader033.vdocument.in/reader033/viewer/2022041712/5e488cc38cbe72002128e9e4/html5/thumbnails/89.jpg)
Introduction Ontologies in PortaGe OLE Preliminary Results Future Directions
Preprocessing and Language-Specific Analysis Support
The preprocessing of the input goes through several phases:
1 splitting the raw text into sentences and elimination ofirrelevant ones;
2 text tokenization;
3 POS tagging (Brill, stochastic, rule-based + unknown words);
4 chunking, esp. noun phrases (rule-based).
Language and Domain-Specific Analysis Support:
additional regular expressions for chunk parsing – keywordidentification
terminological dictionaries
WSD resources
![Page 90: Ontology Acquisition for Automatic Building of Scientific ...Ontology Acquisition for Automatic Building of Scientific Portals Pavel Smrˇz1 V´ıt Nov´aˇcek2 1Faculty of Information](https://reader033.vdocument.in/reader033/viewer/2022041712/5e488cc38cbe72002128e9e4/html5/thumbnails/90.jpg)
Introduction Ontologies in PortaGe OLE Preliminary Results Future Directions
Preprocessing and Language-Specific Analysis Support
The preprocessing of the input goes through several phases:
1 splitting the raw text into sentences and elimination ofirrelevant ones;
2 text tokenization;
3 POS tagging (Brill, stochastic, rule-based + unknown words);
4 chunking, esp. noun phrases (rule-based).
Language and Domain-Specific Analysis Support:
additional regular expressions for chunk parsing – keywordidentification
terminological dictionaries
WSD resources
![Page 91: Ontology Acquisition for Automatic Building of Scientific ...Ontology Acquisition for Automatic Building of Scientific Portals Pavel Smrˇz1 V´ıt Nov´aˇcek2 1Faculty of Information](https://reader033.vdocument.in/reader033/viewer/2022041712/5e488cc38cbe72002128e9e4/html5/thumbnails/91.jpg)
Introduction Ontologies in PortaGe OLE Preliminary Results Future Directions
Preprocessing and Language-Specific Analysis Support
The preprocessing of the input goes through several phases:
1 splitting the raw text into sentences and elimination ofirrelevant ones;
2 text tokenization;
3 POS tagging (Brill, stochastic, rule-based + unknown words);
4 chunking, esp. noun phrases (rule-based).
Language and Domain-Specific Analysis Support:
additional regular expressions for chunk parsing – keywordidentification
terminological dictionaries
WSD resources
![Page 92: Ontology Acquisition for Automatic Building of Scientific ...Ontology Acquisition for Automatic Building of Scientific Portals Pavel Smrˇz1 V´ıt Nov´aˇcek2 1Faculty of Information](https://reader033.vdocument.in/reader033/viewer/2022041712/5e488cc38cbe72002128e9e4/html5/thumbnails/92.jpg)
Introduction Ontologies in PortaGe OLE Preliminary Results Future Directions
Preprocessing and Language-Specific Analysis Support
The preprocessing of the input goes through several phases:
1 splitting the raw text into sentences and elimination ofirrelevant ones;
2 text tokenization;
3 POS tagging (Brill, stochastic, rule-based + unknown words);
4 chunking, esp. noun phrases (rule-based).
Language and Domain-Specific Analysis Support:
additional regular expressions for chunk parsing – keywordidentification
terminological dictionaries
WSD resources
![Page 93: Ontology Acquisition for Automatic Building of Scientific ...Ontology Acquisition for Automatic Building of Scientific Portals Pavel Smrˇz1 V´ıt Nov´aˇcek2 1Faculty of Information](https://reader033.vdocument.in/reader033/viewer/2022041712/5e488cc38cbe72002128e9e4/html5/thumbnails/93.jpg)
Introduction Ontologies in PortaGe OLE Preliminary Results Future Directions
Preprocessing and Language-Specific Analysis Support
The preprocessing of the input goes through several phases:
1 splitting the raw text into sentences and elimination ofirrelevant ones;
2 text tokenization;
3 POS tagging (Brill, stochastic, rule-based + unknown words);
4 chunking, esp. noun phrases (rule-based).
Language and Domain-Specific Analysis Support:
additional regular expressions for chunk parsing – keywordidentification
terminological dictionaries
WSD resources
![Page 94: Ontology Acquisition for Automatic Building of Scientific ...Ontology Acquisition for Automatic Building of Scientific Portals Pavel Smrˇz1 V´ıt Nov´aˇcek2 1Faculty of Information](https://reader033.vdocument.in/reader033/viewer/2022041712/5e488cc38cbe72002128e9e4/html5/thumbnails/94.jpg)
Introduction Ontologies in PortaGe OLE Preliminary Results Future Directions
Preprocessing and Language-Specific Analysis Support
The preprocessing of the input goes through several phases:
1 splitting the raw text into sentences and elimination ofirrelevant ones;
2 text tokenization;
3 POS tagging (Brill, stochastic, rule-based + unknown words);
4 chunking, esp. noun phrases (rule-based).
Language and Domain-Specific Analysis Support:
additional regular expressions for chunk parsing – keywordidentification
terminological dictionaries
WSD resources
![Page 95: Ontology Acquisition for Automatic Building of Scientific ...Ontology Acquisition for Automatic Building of Scientific Portals Pavel Smrˇz1 V´ıt Nov´aˇcek2 1Faculty of Information](https://reader033.vdocument.in/reader033/viewer/2022041712/5e488cc38cbe72002128e9e4/html5/thumbnails/95.jpg)
Introduction Ontologies in PortaGe OLE Preliminary Results Future Directions
Preprocessing and Language-Specific Analysis Support
The preprocessing of the input goes through several phases:
1 splitting the raw text into sentences and elimination ofirrelevant ones;
2 text tokenization;
3 POS tagging (Brill, stochastic, rule-based + unknown words);
4 chunking, esp. noun phrases (rule-based).
Language and Domain-Specific Analysis Support:
additional regular expressions for chunk parsing – keywordidentification
terminological dictionaries
WSD resources
![Page 96: Ontology Acquisition for Automatic Building of Scientific ...Ontology Acquisition for Automatic Building of Scientific Portals Pavel Smrˇz1 V´ıt Nov´aˇcek2 1Faculty of Information](https://reader033.vdocument.in/reader033/viewer/2022041712/5e488cc38cbe72002128e9e4/html5/thumbnails/96.jpg)
Introduction Ontologies in PortaGe OLE Preliminary Results Future Directions
Extraction Core
1 Generic wrapper for chunked sentences
chunk splitting/mergingnamed-entity extractionextraction of adjectival modifierspredicate structures identification
2 Interface for extraction plug-ins takes advantage of thewrapper methods and stores the extracted data in an internalontology-representation format
3 Transformation layer provides transformational rules forimmediate miniontology output in various formats (such asOWL or our fuzzy OWL extension – (F)OWL); it also passesthe unmodified extracted miniontology further to theintegration module OLEMAN
![Page 97: Ontology Acquisition for Automatic Building of Scientific ...Ontology Acquisition for Automatic Building of Scientific Portals Pavel Smrˇz1 V´ıt Nov´aˇcek2 1Faculty of Information](https://reader033.vdocument.in/reader033/viewer/2022041712/5e488cc38cbe72002128e9e4/html5/thumbnails/97.jpg)
Introduction Ontologies in PortaGe OLE Preliminary Results Future Directions
Extraction Core
1 Generic wrapper for chunked sentences
chunk splitting/mergingnamed-entity extractionextraction of adjectival modifierspredicate structures identification
2 Interface for extraction plug-ins takes advantage of thewrapper methods and stores the extracted data in an internalontology-representation format
3 Transformation layer provides transformational rules forimmediate miniontology output in various formats (such asOWL or our fuzzy OWL extension – (F)OWL); it also passesthe unmodified extracted miniontology further to theintegration module OLEMAN
![Page 98: Ontology Acquisition for Automatic Building of Scientific ...Ontology Acquisition for Automatic Building of Scientific Portals Pavel Smrˇz1 V´ıt Nov´aˇcek2 1Faculty of Information](https://reader033.vdocument.in/reader033/viewer/2022041712/5e488cc38cbe72002128e9e4/html5/thumbnails/98.jpg)
Introduction Ontologies in PortaGe OLE Preliminary Results Future Directions
Extraction Core
1 Generic wrapper for chunked sentences
chunk splitting/mergingnamed-entity extractionextraction of adjectival modifierspredicate structures identification
2 Interface for extraction plug-ins takes advantage of thewrapper methods and stores the extracted data in an internalontology-representation format
3 Transformation layer provides transformational rules forimmediate miniontology output in various formats (such asOWL or our fuzzy OWL extension – (F)OWL); it also passesthe unmodified extracted miniontology further to theintegration module OLEMAN
![Page 99: Ontology Acquisition for Automatic Building of Scientific ...Ontology Acquisition for Automatic Building of Scientific Portals Pavel Smrˇz1 V´ıt Nov´aˇcek2 1Faculty of Information](https://reader033.vdocument.in/reader033/viewer/2022041712/5e488cc38cbe72002128e9e4/html5/thumbnails/99.jpg)
Introduction Ontologies in PortaGe OLE Preliminary Results Future Directions
Extraction Core
1 Generic wrapper for chunked sentences
chunk splitting/mergingnamed-entity extractionextraction of adjectival modifierspredicate structures identification
2 Interface for extraction plug-ins takes advantage of thewrapper methods and stores the extracted data in an internalontology-representation format
3 Transformation layer provides transformational rules forimmediate miniontology output in various formats (such asOWL or our fuzzy OWL extension – (F)OWL); it also passesthe unmodified extracted miniontology further to theintegration module OLEMAN
![Page 100: Ontology Acquisition for Automatic Building of Scientific ...Ontology Acquisition for Automatic Building of Scientific Portals Pavel Smrˇz1 V´ıt Nov´aˇcek2 1Faculty of Information](https://reader033.vdocument.in/reader033/viewer/2022041712/5e488cc38cbe72002128e9e4/html5/thumbnails/100.jpg)
Introduction Ontologies in PortaGe OLE Preliminary Results Future Directions
Possible Extraction Methods
pattern-driven extraction of semantic relations – well known and easy toimplement method coined by Marti Hearst; utilizes matching of givenpatterns that are significant for particular semantic relations; mostlyeffective for the is-a relation but applicable for other semantic or ad hocrelations (such as method-of or described-in relations that are usefulwhen analyzing scientific materials)
lexico-syntactic co-occurrence methods for clustering words, accompaniedby identifying the classes using the knowledge already contained in ourdomain specific ontology (or external sources like WordNet, Roget’sthesaurus, word sketch engines etc.)
various other kinds of semantic clustering or (F)FCA methods can beeasily plugged in
![Page 101: Ontology Acquisition for Automatic Building of Scientific ...Ontology Acquisition for Automatic Building of Scientific Portals Pavel Smrˇz1 V´ıt Nov´aˇcek2 1Faculty of Information](https://reader033.vdocument.in/reader033/viewer/2022041712/5e488cc38cbe72002128e9e4/html5/thumbnails/101.jpg)
Introduction Ontologies in PortaGe OLE Preliminary Results Future Directions
Possible Extraction Methods
pattern-driven extraction of semantic relations – well known and easy toimplement method coined by Marti Hearst; utilizes matching of givenpatterns that are significant for particular semantic relations; mostlyeffective for the is-a relation but applicable for other semantic or ad hocrelations (such as method-of or described-in relations that are usefulwhen analyzing scientific materials)
lexico-syntactic co-occurrence methods for clustering words, accompaniedby identifying the classes using the knowledge already contained in ourdomain specific ontology (or external sources like WordNet, Roget’sthesaurus, word sketch engines etc.)
various other kinds of semantic clustering or (F)FCA methods can beeasily plugged in
![Page 102: Ontology Acquisition for Automatic Building of Scientific ...Ontology Acquisition for Automatic Building of Scientific Portals Pavel Smrˇz1 V´ıt Nov´aˇcek2 1Faculty of Information](https://reader033.vdocument.in/reader033/viewer/2022041712/5e488cc38cbe72002128e9e4/html5/thumbnails/102.jpg)
Introduction Ontologies in PortaGe OLE Preliminary Results Future Directions
Possible Extraction Methods
pattern-driven extraction of semantic relations – well known and easy toimplement method coined by Marti Hearst; utilizes matching of givenpatterns that are significant for particular semantic relations; mostlyeffective for the is-a relation but applicable for other semantic or ad hocrelations (such as method-of or described-in relations that are usefulwhen analyzing scientific materials)
lexico-syntactic co-occurrence methods for clustering words, accompaniedby identifying the classes using the knowledge already contained in ourdomain specific ontology (or external sources like WordNet, Roget’sthesaurus, word sketch engines etc.)
various other kinds of semantic clustering or (F)FCA methods can beeasily plugged in
![Page 103: Ontology Acquisition for Automatic Building of Scientific ...Ontology Acquisition for Automatic Building of Scientific Portals Pavel Smrˇz1 V´ıt Nov´aˇcek2 1Faculty of Information](https://reader033.vdocument.in/reader033/viewer/2022041712/5e488cc38cbe72002128e9e4/html5/thumbnails/103.jpg)
Introduction Ontologies in PortaGe OLE Preliminary Results Future Directions
Preliminary Results
pattern-based acquisition of taxonomic relations was tested on anexperimental (computer science) corpus with the size of about 70 millionwords
speed of about 10, 000 words per second
no “gold standard” for the domain was available, so an orientationalsemi–automatic evaluation was performed on a random sample of10 miniontologies:
File File sz. No. of No. of Prec. (%) Rec. (%) I (%)(words) conc. rel.
1 3330 7 5 60.00 23.52 840.342 2606 9 5 80.00 5.21 1438.853 5387 33 24 62.50 5.88 4401.414 2274 16 11 63.63 3.31 2179.115 3936 25 14 71.43 7.51 4277.256 4943 27 18 61.11 5.84 3892.367 3937 22 15 46.67 4.27 3070.398 7438 25 16 68.75 7.37 3756.839 1826 10 5 60.00 6.19 1801.8010 5250 52 32 37.50 18.42 8333.33
average 4093 22.6 14.5 61.16 8.75 3399.17
![Page 104: Ontology Acquisition for Automatic Building of Scientific ...Ontology Acquisition for Automatic Building of Scientific Portals Pavel Smrˇz1 V´ıt Nov´aˇcek2 1Faculty of Information](https://reader033.vdocument.in/reader033/viewer/2022041712/5e488cc38cbe72002128e9e4/html5/thumbnails/104.jpg)
Introduction Ontologies in PortaGe OLE Preliminary Results Future Directions
Preliminary Results
pattern-based acquisition of taxonomic relations was tested on anexperimental (computer science) corpus with the size of about 70 millionwords
speed of about 10, 000 words per second
no “gold standard” for the domain was available, so an orientationalsemi–automatic evaluation was performed on a random sample of10 miniontologies:
File File sz. No. of No. of Prec. (%) Rec. (%) I (%)(words) conc. rel.
1 3330 7 5 60.00 23.52 840.342 2606 9 5 80.00 5.21 1438.853 5387 33 24 62.50 5.88 4401.414 2274 16 11 63.63 3.31 2179.115 3936 25 14 71.43 7.51 4277.256 4943 27 18 61.11 5.84 3892.367 3937 22 15 46.67 4.27 3070.398 7438 25 16 68.75 7.37 3756.839 1826 10 5 60.00 6.19 1801.8010 5250 52 32 37.50 18.42 8333.33
average 4093 22.6 14.5 61.16 8.75 3399.17
![Page 105: Ontology Acquisition for Automatic Building of Scientific ...Ontology Acquisition for Automatic Building of Scientific Portals Pavel Smrˇz1 V´ıt Nov´aˇcek2 1Faculty of Information](https://reader033.vdocument.in/reader033/viewer/2022041712/5e488cc38cbe72002128e9e4/html5/thumbnails/105.jpg)
Introduction Ontologies in PortaGe OLE Preliminary Results Future Directions
Preliminary Results
pattern-based acquisition of taxonomic relations was tested on anexperimental (computer science) corpus with the size of about 70 millionwords
speed of about 10, 000 words per second
no “gold standard” for the domain was available, so an orientationalsemi–automatic evaluation was performed on a random sample of10 miniontologies:
File File sz. No. of No. of Prec. (%) Rec. (%) I (%)(words) conc. rel.
1 3330 7 5 60.00 23.52 840.342 2606 9 5 80.00 5.21 1438.853 5387 33 24 62.50 5.88 4401.414 2274 16 11 63.63 3.31 2179.115 3936 25 14 71.43 7.51 4277.256 4943 27 18 61.11 5.84 3892.367 3937 22 15 46.67 4.27 3070.398 7438 25 16 68.75 7.37 3756.839 1826 10 5 60.00 6.19 1801.8010 5250 52 32 37.50 18.42 8333.33
average 4093 22.6 14.5 61.16 8.75 3399.17
![Page 106: Ontology Acquisition for Automatic Building of Scientific ...Ontology Acquisition for Automatic Building of Scientific Portals Pavel Smrˇz1 V´ıt Nov´aˇcek2 1Faculty of Information](https://reader033.vdocument.in/reader033/viewer/2022041712/5e488cc38cbe72002128e9e4/html5/thumbnails/106.jpg)
Introduction Ontologies in PortaGe OLE Preliminary Results Future Directions
Sample Portion of an Ontology Gained by OLE
![Page 107: Ontology Acquisition for Automatic Building of Scientific ...Ontology Acquisition for Automatic Building of Scientific Portals Pavel Smrˇz1 V´ıt Nov´aˇcek2 1Faculty of Information](https://reader033.vdocument.in/reader033/viewer/2022041712/5e488cc38cbe72002128e9e4/html5/thumbnails/107.jpg)
Introduction Ontologies in PortaGe OLE Preliminary Results Future Directions
Conclusions and Future Directions
the flexible OLE system has been presented as a base forfuture purely–autonomous acquisition of ontologies forautomatic building of scientific portals
more extraction plug-ins to increase the coverage of theOLITE module
defeasible mechanisms for ontology merging and theircombination with fuzzy logic
development and integration of advanced reasoning engines
coin and apply a framework for proper evaluation
WordNet, SUMO, MILO to define the directions for kinds ofrelations
uncertainty via subjective language analysis
![Page 108: Ontology Acquisition for Automatic Building of Scientific ...Ontology Acquisition for Automatic Building of Scientific Portals Pavel Smrˇz1 V´ıt Nov´aˇcek2 1Faculty of Information](https://reader033.vdocument.in/reader033/viewer/2022041712/5e488cc38cbe72002128e9e4/html5/thumbnails/108.jpg)
Introduction Ontologies in PortaGe OLE Preliminary Results Future Directions
Conclusions and Future Directions
the flexible OLE system has been presented as a base forfuture purely–autonomous acquisition of ontologies forautomatic building of scientific portals
more extraction plug-ins to increase the coverage of theOLITE module
defeasible mechanisms for ontology merging and theircombination with fuzzy logic
development and integration of advanced reasoning engines
coin and apply a framework for proper evaluation
WordNet, SUMO, MILO to define the directions for kinds ofrelations
uncertainty via subjective language analysis
![Page 109: Ontology Acquisition for Automatic Building of Scientific ...Ontology Acquisition for Automatic Building of Scientific Portals Pavel Smrˇz1 V´ıt Nov´aˇcek2 1Faculty of Information](https://reader033.vdocument.in/reader033/viewer/2022041712/5e488cc38cbe72002128e9e4/html5/thumbnails/109.jpg)
Introduction Ontologies in PortaGe OLE Preliminary Results Future Directions
Conclusions and Future Directions
the flexible OLE system has been presented as a base forfuture purely–autonomous acquisition of ontologies forautomatic building of scientific portals
more extraction plug-ins to increase the coverage of theOLITE module
defeasible mechanisms for ontology merging and theircombination with fuzzy logic
development and integration of advanced reasoning engines
coin and apply a framework for proper evaluation
WordNet, SUMO, MILO to define the directions for kinds ofrelations
uncertainty via subjective language analysis
![Page 110: Ontology Acquisition for Automatic Building of Scientific ...Ontology Acquisition for Automatic Building of Scientific Portals Pavel Smrˇz1 V´ıt Nov´aˇcek2 1Faculty of Information](https://reader033.vdocument.in/reader033/viewer/2022041712/5e488cc38cbe72002128e9e4/html5/thumbnails/110.jpg)
Introduction Ontologies in PortaGe OLE Preliminary Results Future Directions
Conclusions and Future Directions
the flexible OLE system has been presented as a base forfuture purely–autonomous acquisition of ontologies forautomatic building of scientific portals
more extraction plug-ins to increase the coverage of theOLITE module
defeasible mechanisms for ontology merging and theircombination with fuzzy logic
development and integration of advanced reasoning engines
coin and apply a framework for proper evaluation
WordNet, SUMO, MILO to define the directions for kinds ofrelations
uncertainty via subjective language analysis
![Page 111: Ontology Acquisition for Automatic Building of Scientific ...Ontology Acquisition for Automatic Building of Scientific Portals Pavel Smrˇz1 V´ıt Nov´aˇcek2 1Faculty of Information](https://reader033.vdocument.in/reader033/viewer/2022041712/5e488cc38cbe72002128e9e4/html5/thumbnails/111.jpg)
Introduction Ontologies in PortaGe OLE Preliminary Results Future Directions
Conclusions and Future Directions
the flexible OLE system has been presented as a base forfuture purely–autonomous acquisition of ontologies forautomatic building of scientific portals
more extraction plug-ins to increase the coverage of theOLITE module
defeasible mechanisms for ontology merging and theircombination with fuzzy logic
development and integration of advanced reasoning engines
coin and apply a framework for proper evaluation
WordNet, SUMO, MILO to define the directions for kinds ofrelations
uncertainty via subjective language analysis
![Page 112: Ontology Acquisition for Automatic Building of Scientific ...Ontology Acquisition for Automatic Building of Scientific Portals Pavel Smrˇz1 V´ıt Nov´aˇcek2 1Faculty of Information](https://reader033.vdocument.in/reader033/viewer/2022041712/5e488cc38cbe72002128e9e4/html5/thumbnails/112.jpg)
Introduction Ontologies in PortaGe OLE Preliminary Results Future Directions
Conclusions and Future Directions
the flexible OLE system has been presented as a base forfuture purely–autonomous acquisition of ontologies forautomatic building of scientific portals
more extraction plug-ins to increase the coverage of theOLITE module
defeasible mechanisms for ontology merging and theircombination with fuzzy logic
development and integration of advanced reasoning engines
coin and apply a framework for proper evaluation
WordNet, SUMO, MILO to define the directions for kinds ofrelations
uncertainty via subjective language analysis
![Page 113: Ontology Acquisition for Automatic Building of Scientific ...Ontology Acquisition for Automatic Building of Scientific Portals Pavel Smrˇz1 V´ıt Nov´aˇcek2 1Faculty of Information](https://reader033.vdocument.in/reader033/viewer/2022041712/5e488cc38cbe72002128e9e4/html5/thumbnails/113.jpg)
Introduction Ontologies in PortaGe OLE Preliminary Results Future Directions
Conclusions and Future Directions
the flexible OLE system has been presented as a base forfuture purely–autonomous acquisition of ontologies forautomatic building of scientific portals
more extraction plug-ins to increase the coverage of theOLITE module
defeasible mechanisms for ontology merging and theircombination with fuzzy logic
development and integration of advanced reasoning engines
coin and apply a framework for proper evaluation
WordNet, SUMO, MILO to define the directions for kinds ofrelations
uncertainty via subjective language analysis