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Text Analytics on UIMA and UIMA Semantic Search Engine ISM209 David Lewis Student Project Presentation 2006-12-05.

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Presentation on theme: "Text Analytics on UIMA and UIMA Semantic Search Engine ISM209 David Lewis Student Project Presentation 2006-12-05."— Presentation transcript:

1 Text Analytics on UIMA and UIMA Semantic Search Engine ISM209 David Lewis Student Project Presentation 2006-12-05

2 What Learn about UIMA –UIMA Origins and Applications –UIMA Architecture and Components Juru extended For XML Document Search Demonstration

3 UIMA Origins and Goals Developed by IBM Research over 4 years Offered by IBM as open source EOY05 –DeveloperWorks –WebSphere production –AlphaWorks – Early adopters –Source Forge – Handoff In Process “Bridge from the unstructured word to the structured world” “UIMA SDK supports development, discovery, composition and deployment of multi-modal analytics for the analysis of unstructured information”

4 UIMA Applications WebSphere Information Integrator OmniFind Edition (search engine) Lotus Notes search DARPA UIMA Working Group (WWW mining) Unstructured Information Management (UIM) Research and Instruction –CMU, Stanford, UMass Amherst Others –SAIC, BBN, Mayo Clinic, MITRE Corp –“14 Software Vendors” (press in open source announcement

5 Architecture and Components UIMA Framework - run-time environment UIMA SDK – all Java implementation of framework with Eclipse IDE integration

6 Components UIMA Framework Core Externalized Framework Plug-ins –Common Annotation Structure (CAS) –Type System (Person, Organization, Bank, etc) –Document Annotator, Analysis Engines –Collection Processing Engine –CAS Sources and Sinks –Resource and Configuration Manager, Logger, etc

7 Processing Engine Configurator

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9 Aggregate Analysis Engines Analysis engines may be composed into aggregate engines Analysis Engine Assembler Distributed execution support

10 UIMA Tools and Utilities CAS Save/Restore Configuration Editors Annotation Viewer CAS Visual Debugger Document Analyzer –Graphical tool for applying analysis engines and viewing results Juru-based Semantic Search Engine

11 Exploiting Analysis Results Semantic Search –Contribute analysis results (CASs) to “Juru” XML search engine indexer –Typed-entity recognizers (e.g., name-entity) –XML Fragments query language Database Insert/Update Stream –Contribute analysis results to database

12 Juru Search Engine Extensions for XML Extended Vector Space Model –Compound index items: ( context, word ) –Cosine distance with context Relaxed match on context (context resemblance measure)

13 Demonstrations Running an Analysis Engine Building Collection Processing Engine Running Semantic Search

14 References UIMA SDK Users Guide Reference –http://dl.alphaworks.ibm.com/technologies/uima/UIMA_SDK_Users_Gui de_Reference.pdf An Extension of the Vector Space Model for Querying XML Documents via XML Fragment –http://xml.coverpages.org/CarmelFragments.pdf


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