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MedOnto: Medical Ontology Learning System

(Work in Progress)

Syed Farrukh MehdiReza Fathzadeh

S. M. Faisal Abbas (Presenter){fmehdi,reza,fabbas}@cs.dal.ca

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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

Introduction:

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Syntax based concept learning augmented with domain specific subject corpora

Methodology

Domain Specific

Knowledge base

Syntax Based Extraction

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Medical Domain Terminology◦ OpenGalen project

GALEN Terminology Server

For Other domains, domain specific terminology corpus should be used.

Domain Specific Corpus

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Syntax Based Extraction Levels

Paul Buitelaar

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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

Term Extraction

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Domain Specific Terminology Corpus Language corpus for general concepts

◦ GRAIL Terminology Server for Medical Domain◦ WordNet for English Language

Synonym Extraction

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Intension◦ Formal and information definition of terms

Extension◦ Deriving concepts

Linguistic Realization◦ Concept coverage

Concept Extraction

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Terminal Concept◦ Nouns, Noun Phrases

Compound Concepts◦ Defined Rules

Terminal and Compound Concepts

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Concepts are related Defined Rules

Relation Extraction

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IN subordinating conjunction (FUNC_WORD) or preposition (PREP) ◦ “of”

Candidate for Taxonomy

Rules (IN)

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CC coordinating conjunction ◦ “and”, “or” etc

◦ Compound concepts, broken into terminal concepts

Rules (CC)

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RB adverb and adverbial phrase DT determiner/demonstrative pronoun

Ignored in our work so far

Rules (RB, DT, PDT)

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Verb is used as a relation between subject and object

Rule (VB)

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JJ adjective NN common noun

Rule (JJ+NN -> NP)

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Recursive, until dependency tree is exhausted

Create compound concepts and relate them with the rule and then apply the rules on the sub phrases

Algorithm

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Framework Institution Reference

ASIUM INRIA, Jouy--‐en--‐Josas Faure and Nedellec 1999

TextToOnto AIFB, University of Karlsruhe Madche and Volz 2001

HASTI Amir Kabir University, Teheran Shamsfard,Barforoush2004

OntoLT DFKI, Saarbrucken Buitelaar et al. 2004

DOODLE Shizuoka University Morita et al.2004

Text2Onto AIFB, University of Karlsruhe Cimiano and Volker 2005

OntoLearn University of Rome Velardi et al. 2005

OLE Brno University of Technology Novacek and Smrz 2005

OntoGen Institute Jozef Stefan, Ljubljana Fortuna et al., 2007

GALeOn Technical University of Madrid Manzano-Macho et al. 2008

DINO DERI, Galway Novacek et al.2008

OntoLancs Lancester University Gacitua et al. 2008

RELExO AIFB, University of Karlsruhe Volker and Rudolph 2008

OntoComp University of Dresden Sertkaya 2008

Other Work

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[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. Heiner Stuckenschmidt, 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/

References

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