1. 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.

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
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.

4. Example of a domain ontology

5. Ongoing example substance process Glucose uptake Insulin isa isa
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 Glucose uptake = Glycose transport
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 activates substance
Substance activates process

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

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 :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
Fig 1_ equilibrium 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
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)

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