Tamas Doszkocs, Ph.D. Computer Scientist Meta Searching and Clustering

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 McGraw Hill –An Associative Interactive Dictionary - Doszkocs –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 p Jun 1978 Chemical Substances Information Network, –Information Retrieval in Toxicology, H.M. Kissman, Annual Review of Pharmacology and Toxicology, April 1980, Vol. 20, Pages 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 , White Plains, NY, 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 The Open Web and the Hidden Web

Metadata and Semantics Wilf Lancaster, Vocabulary Control for Information Retrieval, 1972 –Dublin Core –Federated Searching Interface Techniques for Heterogeneous OAI Repositories /v02/i04/Liu/ /v02/i04/Liu/ –eXchangeable Faceted Metadata Language / / –SIMILE (Semantic Interoperability of Metadata and Information in unLike Environments) –Folksonomies –Semantic Web / / edu/dspace/bitstream/1794/32 69/1/ccq_sem_web.pdfhttps://scholarsbank.uoregon. edu/dspace/bitstream/1794/32 69/1/ccq_sem_web.pdf –Ontology Lookup Service y-lookup/ y-lookup/ –Web Services for Controlled Vocabularies un-06/vizine- goetz_houghton_childress.ht mlhttp:// un-06/vizine- goetz_houghton_childress.ht ml

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!. Google vs. Yahoo automatic vs. controlled indexing

The Remains of the Yahoo Directory

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 Engines The NLM ToxSeek System

Clustering of Search Results with Phrases

PolyMeta Clustering

Visualizing Topical Clusters

Multi-faceted Visualization

Clustering in A GYM Ask 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 –Digital Libraries –Data Mining and Analysis –Information Visualization –Semantic Web

Tamas Doszkocs, Ph.D. Computer Scientist Meta Searching and Clustering