Biological Data Extraction and Integration A Research Area Background Study

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Biological Data Extraction and Integration A
Research Area Background Study

• Cui Tao
• Department of Computer Science
• Brigham Young University

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

• Information extraction systems process text
  documents and locate a specific set of relevant
  items. Califf99

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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

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Information Extraction

• Traditional information extraction
• Hidden web crawling
• Biological data extraction

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Traditional Information Extraction

• Different groups of IE tools Laender02
• Wrapper generation tools
• NLP-based and learning-based tools
• Ontology-based tools

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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

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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

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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

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Hidden Web Crawling

• Traditional IE tools publicly indexable web
  pages
• Hidden web crawling
• Crawl the hidden web according to a users query
• HiWE (Hidden Web Exposer) Raghavan01
• Source form representation ?? task-specific DB
  concepts
• Fill out and submit forms
• Retrieve information hidden behind the form

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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

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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

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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)
• DAMLOIL (DARPA Agent Markup Language OIL)
• OWL (Ontology Web Language)
• Semantic Annotation
• Old indexing of publications in libraries
• New information extraction

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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

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Schema Matching

• LSD (Learning Source Description) Doan01
• Semi-automatic
• Learning-based
• Both schema-level and instance-Level
• Only 1-1 mappings
• GLUE CGLUE DMD03
• Ontology alignment
• CGLUE Complex (non-1-1) mappings

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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

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Schema Matching

• COMA (COmbing MAtch) Do02
• Combines different matchers
• Interactive with users
• Also an evaluation platform for different matchers

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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

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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

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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

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Bioinformatics

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

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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

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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

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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

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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