automatic semantic interpretation of unstructured data for knowledge management
DESCRIPTION
The demo shows an automatic semantic analysis of Wikipedia articles about astronomy.TRANSCRIPT
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Topic Maps in the Industry
TMRA 2010
Demo of an automatic semantic interpretation of unstructured data for knowledge management
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Agenda
1. Demo
2. Knowledge Discovery
3. Technical Solution
Inverted approach of semantic it
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1.
The demo shows twofold results of an automatic semantic analysis of Wikipedia articles to demonstrate a new approach for knowledge discovery.
Demo
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1.
Analysis of Wikipedia articles about astronomy
Demo
Crawling all articles of a knowledge domain
Extracting the relevant text parts of Wikipedia pages
Extracting meta data of each Wikipedia article
Automatic semantic analysis of integrated data
on a term level to create a linked concept graph
on an object level linked data (object) graph
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1.
What the demo shows
Demo
Visualization of the linked concept graph (left)
Visualization of the linked data graph (right)
Knowledge discovery by a taxonomy and linked data
Accessing information by linked data
Accessing information by derived taxonomy
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2.
Isolated data becomes meaning by links to related data. Even unstructured information can be evaluated systematically by linked data and a derived taxonomy.
Knowledge Discovery
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2.
Use cases for an object graph
Knowledge Discovery
Information Logistics: Relevant information will be provided automatically in the process or activity context of a user.
Portal navigation: Users can navigate according to their personal focus of interest along the dynamic links to each selected context.
Knowledge discovery: Awareness of hidden knowledge such as project synergies, sales opportunities, relevant news.
Question answering: The identification of appropriate responses, related problems, or experts on the issue.
Business intelligence: Complex queries of the object graph for reports on customer behavior, staff profiles and project analysis.
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2.
Use cases for a concept graph
Knowledge discovery
Knowledge Representation: The concept graph gives an overview of key entities and facts in an unstructured data set.
Document and e-mail-clustering: Unstructured data will be grouped thematically or associated with each path in a taxonomy.
Moderated search: searches for the automatic extension of a keyword search for increased precision of the results.
Topic monitoring: Identifying new facts and new issues or topics in the news, or constellations of other publications
Taxonomy or ontology modeling and maintenance: Initial knowledge representation and identification of adaptation needs.
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3.
Knowledge discovery needs a real bottom-up-approach with no initial effort on modeling a knowledge domain. The result can be exported as topic maps or combined with formalized domain knowledge of existing topic maps.
Technical Solution
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3.
Implementing Content Provider
Bottom-up semantic data integration
Lean interfaces to connect any data format and source
Push and pull principle to monitor data sources
Optional bi-directional integration of data sources
Optional definition of actions for data objects in each source
Implicit data harmonization and derivation of a common model
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3.
Object graph (linked data graph)
Bottom-up semantic data analysis
All relations (quadruples) are
dynamically created and updated in real-time
described by the semantic reason
weighted regarding the relevance
All relations are created by
Key attributes (syntax analysis)
Text mining (pattern analysis)
User behavior (usage analysis)
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3.
Example of a graph fragment
Bottom-up semantic data analysis
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3.
Concept graph
Bottom-up semantic data integration
Extraction of concepts such as names and terms in texts
Calculation of significance of extracted concepts
Identification of the co-occurrences of significant concepts
Creating a graph with significance value for nodes and edges
Dynamically updated graph caused by new data
Calculation of a hierarchical structure for a taxonomy
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3.
iQser GIN Platform
ERPCRM WWW
Collaboration
Fila System
Custom Applications
Client Connector API
Content Provider API
Ana
lyze
r Tas
k A
PI
Even
t Li
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er A
PI
Security Layer
iQser Core
Custom Analytics / Ontologies
Custom Event Actions / Business
Logic
ESB / SOAWeb Rich-/Fat Client
Mobile
Analyzer Chain Event Processor
Objektgraph Konzeptgraph
Index
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www.iqser.com
+49 172 66 800 73
Dr. Jörg Wurzer Member of the board