tamas doszkocs, ph.d. computer scientist [email protected] meta searching and clustering
TRANSCRIPT
What has been will be again, what has been done will be done again,
there is nothing new under the sun. (Ecclesiastes 1:9-14 NIV)
Meta Searching and Clustering
• A Brief History• Clustering• MetaSearching• Metadata and
Semantics• Clustering Examples
• Meta-Search and Clustering Engines
• A Clustering GYM• AllPlus• Web X.Y• Trends
Related Topics:( that we won’t talk about ):
Clustering
– "Finding a name for something is a way of conjuring its existence, of making it possible for people to see a pattern where they didn't see anything before“ Howard Rheingold
– Purpose: order out of chaos
– Indexes and Table of Contents are as old as human records
– Luhn, H. P. (1959). Keyword-in-Context Index for Technical Literature (KWIC Index). Yorktown Heights, N. Y.: IBM.
– Automatic Information Organization and Retrieval.G Salton - 1968 - McGraw Hill
– An Associative Interactive Dictionary - Doszkocs - 1978
– Dialog RANK command 1993
– Northern Light clustering, or "embedded folders", 1999
Meta-Searching
• Purpose: distributed and enhanced search to find more relevant items
• AID, 1978, MEDLINE, TOXLINE, Hepatitis Databank– Doszkocs, Tamas E. “AID, an Associative Interactive Dictionary for Online Searching” On-Line Review, v2 n2 p163-73 Jun
1978
• Chemical Substances Information Network, 1978-198– Information Retrieval in Toxicology, H.M. Kissman, • Annual Review of Pharmacology and Toxicology, April 1980,
Vol. 20, Pages 285-305
• CITE, 1979– T. E. Doszkocs and B. A. Rapp. Searching MEDLINE in English: A prototype user interface with natural language query,
ranked output, and relevance feedback. In Proceedings of the American Society for Information Science, pages 131--139, White Plains, NY, 1979. Knowledge Industry Publications, Inc
• Dialog OneSearch, 1987• Associative Concept Navigation in MEDLINE and other NLM Databases via a Mosaic - Forms - WWW
Interface Combining Natural Language Processing, Expert Systems and (un)Conventional Information Retrieval Techniques. In Second International World Wide Web Conference, Chicago, Illinois, USA , October 1994. http://www.ncsa.uiuc.edu/SDG/IT94/Proceedings/Searching/doszkocs/doszkocs.html
• The Open Web and the Hidden Web
Metadata and SemanticsWilf Lancaster, Vocabulary Control for Information Retrieval, 1972
– Dublin Core
• http://www.dublincore.org/
– Federated Searching Interface Techniques for Heterogeneous OAI Repositories
• http://jodi.ecs.soton.ac.uk/Articles/v02/i04/Liu/
– eXchangeable Faceted Metadata Language
• http://purl.oclc.org/NET/xfml/core/
– SIMILE (Semantic Interoperability of Metadata and Information in unLike Environments)
• http://simile.mit.edu/
– Folksonomies
• http://flickr.com
– Semantic Web
• http://www.few.vu.nl/~frankh/
• https://scholarsbank.uoregon.edu/dspace/bitstream/1794/3269/1/ccq_sem_web.pdf
– Ontology Lookup Service
• http://www.ebi.ac.uk/ontology-lookup/
– Web Services for Controlled Vocabularies
• http://www.asis.org/Bulletin/Jun-06/vizine-goetz_houghton_childress.html
Examples of Search Result Clustering
• Jerry’s Guide to the Web, 1994• Jerry Yang and David Filo’s Yahoo! 1995
– a directory of web sites, organized in a hierarchy of subject descriptors
– Librarians at Yahoo• Surfing is to Yahoo! what the Dewey Decimal System is to libraries. In other words, Surfing is the categorization of
websites. It also happens to be how Yahoo! began. Today our Surfing team continues its passion for finding, evaluating, and organizing information on the Internet. They have a voracious appetite for learning about new topics. They are curious individuals who are skilled at intuitively and efficiently analyzing and classifying diverse, unstructured pieces of information across the Yahoo! network. Surfers are critical to the relevance and intuitive nature of information presented on Yahoo!.
• http://careers.yahoo.com/job_descriptions.html
• Google vs. Yahoo automatic vs. controlled indexing
Open Directory Project
PubMed Related Articles
Folksonomy and Tagging in Flickr
Query Refinement with Subject Headings
Clustering with Multiple Criteria
Multi-faceted Clustering in an OPAC
Analyzing Search Results
Examples of Meta Search EnginesThe NLM ToxSeek System
Clustering of Search Results with Phrases
PolyMeta Clustering
Visualizing Topical Clusters
Multi-faceted Visualization
Clustering in A GYMAsk Google Yahoo MSN
Yahoo health
Google Health Searches
Microsoft Search Result Clustering
Clustering Sophistication: or the lack of it
AllPlus Clustering: the WHO
Clustering and Search Refinement with Natural Language and Controlled Vocabularies
The NLM AllPlus Search Demo
Web 2.0 Content Mashups in AllPlus
HyperGraph Cluster Visualization in AllPlus
The All in AllPlus
• Discovery– Meta-Searching
– Clustering
– Meaning
• Morphology
• Syntax
• Semantics
– Metadata
– Thesauri +
– Visualization
– Web X.Y
Trends
– Web x.0
• Content mashups
• Improved UI
• Social Search and Knowledge Organization
• Query Understanding
– Meaning
– User intent
– Multi-faceted clustering
– Multi-dimensional Information Spaces
• Google http://searchmash.com
– Digital Libraries
– Data Mining and Analysis
– Information Visualization
– Semantic Web
