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MedOnto : Medical Ontology Learning System ( Work in Progress)

MedOnto : Medical Ontology Learning System ( Work in Progress). Syed Farrukh Mehdi Reza Fathzadeh S. M. Faisal Abbas (Presenter) { fmehdi,reza,fabbas }@ cs.dal.ca. Introduction :. Ontology Machine readable information Text

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MedOnto : Medical Ontology Learning System ( Work in Progress)

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  1. MedOnto: Medical Ontology Learning System(Work in Progress) SyedFarrukhMehdi Reza Fathzadeh S. M. Faisal Abbas (Presenter) {fmehdi,reza,fabbas}@cs.dal.ca

  2. Introduction: • Ontology • Machine readable information • Text • Human readable information, most of the current information is text. • Ontology Learning • (Semi) automatic extraction of relevant concept and relations • Medical Domain

  3. Methodology • Syntax based concept learning augmented with domain specific subject corpora Domain Specific Knowledge base Syntax Based Extraction

  4. Domain Specific Corpus • Medical Domain Terminology • OpenGalenproject • GALEN Terminology Server • For Other domains, domain specific terminology corpus should be used.

  5. Syntax Based Extraction Levels Paul Buitelaar

  6. Term Extraction • Parsing • Linguistic Method • Using Production Rules specified by linguists • Statistical Method • Using statistical models derived from written text. • We used Stanford NLP Parser which is a statistical parser • Dependency Trees instead of Parse Trees

  7. Synonym Extraction • Domain Specific Terminology Corpus • Language corpus for general concepts • GRAIL Terminology Server for Medical Domain • WordNet for English Language

  8. Concept Extraction • Intension • Formal and information definition of terms • Extension • Deriving concepts • Linguistic Realization • Concept coverage

  9. Terminal and Compound Concepts • Terminal Concept • Nouns, Noun Phrases • Compound Concepts • Defined Rules

  10. Relation Extraction • Concepts are related • Defined Rules

  11. Rules (IN) • IN subordinating conjunction (FUNC_WORD) or preposition (PREP) • “of” • Candidate for Taxonomy

  12. Rules (CC) • CC coordinating conjunction • “and”, “or” etc • Compound concepts, broken into terminal concepts

  13. Rules (RB, DT, PDT) • RB adverb and adverbial phrase • DT determiner/demonstrative pronoun • Ignored in our work so far

  14. Rule (VB) • Verb is used as a relation between subject and object

  15. Rule (JJ+NN -> NP) • JJ adjective • NN common noun

  16. Algorithm • Recursive, until dependency tree is exhausted • Create compound concepts and relate them with the rule and then apply the rules on the sub phrases

  17. Other Work

  18. References • [Buitelaar05] Paul Buitelaar, etal. Ontology Learning from Text, October 3 rd , 2005 • [Kim09] Jin-Dong Kim et al., Overview of BioNLP’09 Shared Task On Event Extraction   • [Stuck] Semantic Technologies, Ontology Learning, Prof. Dr. HeinerStuckenschmidt, Dr. Johanna Völker • [Biemann] Chris Biemann: Ontology Learning from Text: A Survey of Methods • [StanParser] http://nlp.stanford.edu/software/lex-parser.shtml • [WordNet] http://wordnet.princeton.edu/ • [OpenGALEN] http://www.opengalen.org/

  19. Thank you. Please provide us Comments and Directions

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