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Biological Data Extraction and Integration A Research Area Background Study

Biological Data Extraction and Integration A Research Area Background Study. Cui Tao Department of Computer Science Brigham Young University. Research Field Overview. My research. Semantic Web. Data Integration. Schema Matching. Information Extraction. Bioinformatics.

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Biological Data Extraction and Integration A Research Area Background Study

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  1. Biological Data Extraction and Integration A Research Area Background Study Cui Tao Department of Computer Science Brigham Young University

  2. Research Field Overview My research Semantic Web Data Integration Schema Matching Information Extraction Bioinformatics

  3. Information Extraction • “Information extraction systems process text documents and locate a specific set of relevant items.” [Califf99]

  4. Information Extraction • “Information extraction systems process text documents and locate a specific set of relevant items.” [Califf99] • “Because the WWW consists primarily of text, information extraction is central to all effort that would use the web as a resource for knowledge discovery.” [Freitag98]

  5. Information Extraction • Traditional information extraction • Hidden web crawling • Biological data extraction

  6. Traditional Information Extraction • Different groups of IE tools: [Laender02] • Wrapper generation tools • NLP-based and learning-based tools • Ontology-based tools

  7. Traditional Information Extraction • Wrapper generation tools • Lixto [Baumgartner01] • Supervised wrapper generation • Semi-automatically • Not robust; Does not work well with unstructured data • ROADRUNNER [Crescenzi01] • Fully automatic wrapper generation • Does not generate robust and general wrappers • Only works for highly regular web pages

  8. Traditional Information Extraction • NLP-based and learning-based tools • SRV [Freitag98] • Top-down learner • Learns based on simple and relational features • Single slot filling • RAPIER [Califf99] • Bottom-up learner • Learns pre-filler, slot filler, and post-filler patterns • Only works for free text • Single slot filling

  9. Traditional Information Extraction • Ontology-based tools • BYU Ontos [Embley99] • Based on domain-specific extraction ontologies • Robust to changes • Multiple slot filling • Ontologies has to be built manually

  10. Hidden Web Crawling • Traditional IE tools: publicly indexable web pages • Hidden web crawling • Crawl the hidden web according to a user’s query • HiWE (Hidden Web Exposer) [Raghavan01] • Source form representation  task-specific DB concepts • Fill out and submit forms • Retrieve information hidden behind the form

  11. Biological Data Extraction • Mainly from plain text • Extract biological terms • Dictionary-based • Rule-based • Extract relationships between biological terms/elements • Example systems • BLAST-based name identifier [Krauthammer00] • PASTA (Protein Active Site Template Acquisition) [Gaizauskas03]

  12. The Semantic Web • Machine-understandable web • Gives information a well-defined meaning • Allows automation of tasks • Provides biologists • Intelligent information services • Personalized web resources • Semantically empowered search engines

  13. The Semantic Web • Semantic web languages • XOL (XML-based Ontology Exchange Language) • SHOE (Simple HTML Ontology Extension) • OML (Ontology Markup Language) • RDF(S) (Resource Description Framework (Schema)) • OIL (Ontology Interchange Language) • DAML+OIL (DARPA Agent Markup Language + OIL) • OWL (Ontology Web Language) • Semantic Annotation • Old: indexing of publications in libraries • New: information extraction

  14. Schema Matching • Previous methods [Raghavan01]: • Individual matchers vs. combining matchers • Schema-based matchers vs. instance-based matchers • Learning-based matchers vs. rule-based matchers • Element-level matchers vs. structure-level matchers

  15. Schema Matching • LSD (Learning Source Description) [Doan01] • Semi-automatic • Learning-based • Both schema-level and instance-Level • Only 1-1 mappings • GLUE & CGLUE [DMD+03] • Ontology alignment • CGLUE: Complex (non-1-1) mappings

  16. Schema Matching • Cupid [Madhavan01] • Rule-based matcher • Both element-level and structure-level • Schema-based • Works on hierarchical schemas with schema tree • Linguistic similarity & structure similarity • Matches tree elements by weighted similarities

  17. Schema Matching • COMA (COmbing MAtch) [Do02] • Combines different matchers • Interactive with users • Also an evaluation platform for different matchers

  18. Biological Data Integration • Challenge: • Huge amount, growing rapidly • Highly diverse in granularity and variety • Different terminologies, ID systems, units • Unstable and unpredictable • Different interface and querying capabilities

  19. Biological Data Integration • SRS (Sequence Retrieval System) [Etzold96] • Keyword-based retrieval system • Returns simple aggregation of matched records • Only works for relational databases • BioKleisli [Davidson97] • Integrated digital library in biomedical domain • No global schema or ontology • A mediator works on top of source-specific wrappers • Horizontal integration

  20. Biological Data Integration • DiscoveryLink [Haas01] • Mediator-based, wrapper-oriented • Provides virtual DB access from different sources • Cannot deal with complex source data • Hard to add new sources • Requires knowledge of specific query language • TAMBIS (Transparent Access to Multiple Bioinformatics Information Sources) [Stevens00] • Mediator-based • Uses global ontology and schema • Maps source and target concepts manually • Not robust to changes • Hard to add new sources

  21. Bioinformatics • Biological ontology • Bioinformatics data source discovery • Trustworthiness and provenance

  22. Bioinformatics • Biological ontology • GO (Gene Ontology) [Ashburner00] • Controlled vocabulary • Molecular Function (7278 terms) • Biological Process (8151 terms) • Cellular Component (1379 terms) • Is represent knowledge hierarchically

  23. Bioinformatics • Biology Ontology • LinKBase [Verschelde03] • Originally a biomedical ontology • Over 2,000,000 medical concepts • Over 5,300,000 instantiations • 543 relations • Expanded using GO • Only describes simple binary relationships

  24. Bioinformatics • Bioinformatics data source discovery • First step in integrating or answering queries • Example System: [Rocco03]: • Pre-defined classes with class descriptions • Tries to map a source with a class • Trustworthiness and provenance • Trustworthiness: • Consistency • Reliability • Competence • Honesty • Provenance • Record History • Transformations • Annotations • updates

  25. My research Semantic Web Schema Matching Information Extraction Bioinformatics Summary and Future Work • Overcome drawbacks of existing systems • Elaborate new algorithms to solve the problem of locating and extracting data from heterogeneous biological sources

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