Ontology-based search and knowledge sharing using domain ontologies

1. Ontology-based search and knowledge sharing using domain ontologies Sine Zambach, PhD student, Roskilde University GERPS ‘08

2. Outline 1. Why Domain Ontologies? 2. Ontology-based search 3. Domain analysis: Relations in ontologies 4. How does this gain value for the organisation?

3. Why Domain Ontologies? • Knowledge sharing for common understanding in e.g. software development and translations • Background for domain specific information retrieval

4. Example of a domain ontology

5. Ongoing example substance process isa isa Glucose uptake Insulin activates

6. 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

7. Ongoing example substance process isa isa activate Glucose uptake = Glycose transport Insulin = INS activate New unknown substance

8. 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

9. The SIABO-project Computational Linguists (CL) Knowledge Engineers (K) Computer Scientists (CS) Terminologists (T) Domain experts (D) Ontology based search application Knowledge extraction Search implementation Text pattern rule development on NP’s (CL, KI, D) Interface (CS) Search functions (CS, K, D) Similarity measures (CS) Text preprossesing Domain ontology modelling Grammatical parsing/ POS-tagging/ (CL) Grabbing/ontological tagging fragments using ontotypes (K) Mapping into ontology (CS) Indexing (CS) Start from UMLS (T,D) Modeller in a suitable tool (T,D) Put into relational database (CS)

10. 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

11. Relations as semantic ”glue” • Insulin activates glucose uptake • Pancreas activates organ (odd) • Substance activatessubstance • Substance activatesprocess

12. Domain specific relations

13. OBO-ontologi Table 3 Some properties of the relations in the OBO Relation Ontology RelationTransitive Symmetric Reflexive Antisymmetric is_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

14. 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

15. Example: positive relation –> transitivity? A activates B B activates C -> A activates C A B C A B C A B C

16. Example: inhibits and stimulate -> complex property A inhibits B B inhibits C -> A activates C A B C A B C A B C

17. Verb frequences in the 4 corpora:

18. Background

19. Relations in an enterprise ontology • Discovering of weird words = domain specific concepts and relations • Similarity measure in information retrieval • Information fishing of new concepts

20. Ongoing example substance process isa isa activate Glucose uptake = Glycose transport Insulin = INS activate New unknown substance