Ontology-based search and knowledge sharing using domain ontologies Sine Zambach, PhD student, Roskilde University GERPS ‘08 Presentation of myself: bioinformatician with the ontology niche.

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

Why Domain Ontologies? Knowledge sharing for common understanding in e.g. software development and translations Background for domain specific information retrieval My part is actually a corner of the conference which is not explicitely ERP. However in next generation the tools I work with can actually be an integral part of these systems in the progress of development of common understanding (knowledge sharing) and domain specific information retrieval (eg through research notes in the company database). I am not a ERP-person which is why I hope to get your view (a top down) on our research.

Example of a domain ontology

Ongoing example substance process Glucose uptake Insulin isa isa activates

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

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

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

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)

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

Relations as semantic ”glue” Insulin activates glucose uptake Pancreas activates organ (odd) Substance activates substance Substance activates process

Domain specific relations To be og to have er ranket højt I alle tekster

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

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

Example: positive relation –> transitivity? A activates B B activates C -> A activates C Fig 1_ equilibrium A B C A B C A B C

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

Verb frequences in the 4 corpora:

Background

Relations in an enterprise ontology Discovering of weird words = domain specific concepts and relations Similarity measure in information retrieval Information fishing of new concepts 1) Domain specifik plug in Looking at the word frequences of the most common verbs in BNC (probably) shows no significant difference Domain specific verbs do show difference Problem: How do we ”discover” those domain specific relations? 2) Similarity measures A simple similarity measure is counting edges between concepts. The edges could have different weights according to how much properties each. Inhibit and activate both facilitates ”inheritance” for their concepts. Inheritance of properties shows similarity Used in e.g. business intelligence 3) Information fishing Using an ontology from the known area for Fishing Compounds (relations used as hooks)

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