Enhancing Internet Search Engines to Achieve Concept- based Retrieval F. Lu, T. Johnsten, V. Raghavan, and D. Traylor InForum ‘99 May 5 -6, 1999.

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Presentation transcript:

Enhancing Internet Search Engines to Achieve Concept- based Retrieval F. Lu, T. Johnsten, V. Raghavan, and D. Traylor InForum ‘99 May 5 -6, 1999

Agenda Information on the Internet. Boolean Retrieval Model and the Internet. Concept-Based Retrieval (RUBRIC / CS 3 ). CS 3 and Boolean Search Engines. Future Work.

Information on the Internet Large volume. Rapid growth rate. Wide variations in quality and type.

Boolean Retrieval Model and the Internet Most Internet search engines are based on the Boolean Retrieval Model. Boolean Retrieval Model is relatively easy to implement. Limitations: –Inability to assign weights to query or document terms. –Inability to rank retrieved documents. – Naïve users have difficulty in using

Concept-Based Retrieval Address shortcomings of Boolean Retrieval Model. Search Requests specified in terms of concepts structured as rule-base trees.

Development of Rule-Base Trees (General) Top-down refinement strategy. Support for AND / OR relationships. Support for user-defined weights.

Development of Rule-Base Trees (CS 3 ) Concept-Set Structuring System (CS 3 ) CS 3 supports the creation, storage and modification of user-defined concepts Post-processing of results of sub-queries CS 3 user-interface.

CS3 User Interface

Evaluation of Rule-Base Trees (RUBRIC) Run-time, bottom-up analysis. Propagation of weight values (MIN / MAX). Disadvantage of run-time analysis.

Evaluation of Rule-Base Trees (CS 3 ) Static, bottom-up analysis. Construct Minimal Term Set (MTS). Propagation of terms. CS 3 user-interface.

MTS-Minimal Term Set lA MTS for a topic is a set of terms such that if each term in the set appears in the document, the document would get a RSV larger than 0. If not, the RSV would be 0. lA topic could have more than one MTSs. lA user can choose from those MTSs to perform a search to his needs.

Concept-Based Retrieval and Boolean Search Engines CS 3 is designed to interface with existing Boolean search engines. U.S. Department of Energy’s “Information- Bridge” search engine. U.S. Department of Transportation’s “National Transportation Library” search engine.

System Architecture Client (Java/ Applet ) CORBACGI Server (JAVA)Server (JAVA/C++) JDBC ORACLE DOE InfoBridge … etc.

Information-Bridge and CS 3 Search request: Boolean Vs. Concept Output: Non-Ranked Vs. Ranked. Calculation of RSV: –Given a document D and a set S of MTS expressions satisfied by D, the RSV of D is equal to the sum of all the weights of S plus the maximum weight in S.

Information-Bridge and CS 3 (Example) Boolean search request (“Environmental Science Network” Form): –(“Hydrogeology” OR “Dnapl” OR (“Colloid*” AND “Environmental Transport”)). Concept (CS 3 ): –“Hydrogeology”. –Rule-Base Tree.

CS3 Hydrogeology Rule Base

CS3 search results

Current and Future Work Conduct experiments to evaluate effectiveness (future). Investigate alternative methods to compute RSVs [KADR00, KDR01*]. Learning edge weights through relevanace feedback [KR00]. Thesaurii based rulebase generation [KLR00].

Relevant URLs [LJRT99*] RaghavanHome  Publications since 1991