Syed Farrukh Mehdi
Reza Fathzadeh
S. M. Faisal Abbas (Presenter)
{fmehdi,reza,fabbas}@cs.dal.ca
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Ontology
◦ Machine readable information
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Text
◦ Human readable information, most of the current
information is text.
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Ontology Learning
◦ (Semi) automatic extraction of relevant concept and
relations
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Medical Domain
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Syntax based concept learning augmented
with domain specific subject corpora
Syntax Based
Extraction
Domain
Specific
Knowledge
base
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Medical Domain Terminology
◦ OpenGalen project
 GALEN Terminology Server
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For Other domains, domain specific
terminology corpus should be used.
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Paul Buitelaar
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Parsing
◦ Linguistic Method
 Using Production Rules specified by linguists
◦ Statistical Method
 Using statistical models derived from written text.
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We used Stanford NLP Parser which is a
statistical parser
Dependency Trees instead of Parse Trees
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Domain Specific Terminology Corpus
Language corpus for general concepts
◦ GRAIL Terminology Server for Medical Domain
◦ WordNet for English Language
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Intension
◦ Formal and information definition of terms
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Extension
◦ Deriving concepts
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Linguistic Realization
◦ Concept coverage
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Terminal Concept
◦ Nouns, Noun Phrases
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Compound Concepts
◦ Defined Rules
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Concepts are related
Defined Rules
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IN subordinating conjunction (FUNC_WORD)
or preposition (PREP)
◦ “of”
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Candidate for Taxonomy
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CC coordinating conjunction
◦ “and”, “or” etc
◦ Compound concepts, broken into terminal concepts
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RB adverb and adverbial phrase
DT determiner/demonstrative pronoun
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Ignored in our work so far
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Verb is used as a relation between subject
and object
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JJ adjective
NN common noun
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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
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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
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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/lexparser.shtml
[WordNet] http://wordnet.princeton.edu/
[OpenGALEN] http://www.opengalen.org/
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Please provide us
Comments and Directions
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