Ontology-based search and knowledge sharing using domain ontologies
Sine Zambach, PhD student, Roskilde UniversityGERPS ‘08
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Outline
1. Why Domain Ontologies?2. Ontology-based search3. Domain analysis: Relations in
ontologies4. How does this gain value for the
organisation?
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Why Domain Ontologies?
Knowledge sharing for common understanding in e.g. software development and translations
Background for domain specific information retrieval
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Example of a domain ontology
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Ongoing example
Insulin Glucose uptake
processsubstance
isa
activates
isa
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Ontology-based search
Ontology background for information retrieval:
Broaden search wrt synonyms, ontological similarity, relations, etc.
Can potentially be used by organisations to search through all kinds of texts
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Ongoing example
Insulin = INS
Glucose uptake = Glycose transport
processsubstance
isa
activate
isa
activate New unknown substance
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Ontology based search in biomedical texts
Siabo project Computer scientists computational
linguists, domain experts, terminologists
Develops Background ontology Text preprocessing tools Knowledge extraction tools Implementation on the texts
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The SIABO-project
Ontology based search application
Text preprossesing
Domain ontology modelling
Search implementation
Start from UMLS (T,D)Modeller in a suitable tool (T,D)Put into relational database (CS)
Computational Linguists (CL)Knowledge Engineers (K)Computer Scientists (CS)Terminologists (T)Domain experts (D)
Interface (CS)Search functions (CS, K, D)Similarity measures (CS)
Grammatical parsing/ POS-tagging/ (CL)Grabbing/ontological tagging fragments using ontotypes (K)Mapping into ontology (CS)Indexing (CS)
Knowledge extraction
Text pattern rule development on NP’s (CL, KI, D)
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Relations
Semantic glue between concepts (the idea behind words)
General and domain specific relations
Represented by e.g. verbs and can be identified in various ways
Parallel to concepts that are represented by terms
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Relations as semantic ”glue”
Insulin activates glucose uptake Pancreas activates organ (odd) Substance activates substance Substance activates process
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Domain specific relations
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OBO-ontologi
Table 3Some properties of the relations in the OBO Relation OntologyRelationTransitive Symmetric Reflexive Antisymmetricis_a + - + +part_of + - + +located_in + - + -contained_in - - - -adjacent_to - - - -transformation_of + - - -derives_ from + - - -preceded_by + - - -has_participant- - - -has_agent - - - -Smith et al. Genome Biology 2005 6:R46
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Domain specific relations
Inhibition and activation Domain specific Bio-relations Has interesting properties through a
path of relations of that types. The relation of ”activation” is
transitive, where ”inhibition” is more complex and is dependent of the stimulation-relation
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Example: positive relation –> transitivity?
A activates B
B activates C
-> A activates C
A B C A B CA B C
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Example: inhibits and stimulate -> complex property
A inhibits B
B inhibits C
-> A activates C
A B C A B CA B C
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Verb frequences in the 4 corpora:
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Background
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Relations in an enterprise ontology
Discovering of weird words = domain specific concepts and relations
Similarity measure in information retrieval
Information fishing of new concepts
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Ongoing example
Insulin = INS
Glucose uptake = Glycose transport
processsubstance
isa
activate
isa
activate New unknown substance