Ontology learning with text mining: Two use cases in lipoprotein metabolism and toxicology

Abstract

Background: 
The engineering of ontologies, especially with a view to a text-mining use, is still a 
new research field. There does not yet exist a well-defined theory and technology for 
ontology construction. Many of the ontology design steps remain manual and are 
based on personal experience and intuition. However, there exist a few efforts on 
automatic construction of ontologies in the form of extracted lists of terms and 
relations between them. 

Results: 
We share experience acquired during the manual development of a lipoprotein 
metabolism ontology (LMO) to be used for text-mining. We compare the manually 
created ontology terms with the automatically derived terminology from four different 
automatic term recognition methods. The top 50 predicted terms contain up to 
89% relevant terms. For the top 1000 terms the best method still generates 51% 
relevant terms. In a corpus of 3066 documents 53% of LMO terms are contained and 
38% can be generated with one of the methods. 
Secondly we present a use case for ontology-based search for toxicological methods.

Conclusions: 
Given high precision, automatic methods can help decrease development time and 
provide significant support for the identification of domain-specific vocabulary. The 
coverage of the domain vocabulary depends strongly on the underlying documents. 
Ontology development for text mining should be performed in a semi-automatic way; 
taking automatic term recognition results as input. 

Availability: 
The automatic term recognition method is available as web service, described at 
http://gopubmed4.biotec.tu- 
dresden.de/IdavollWebService/services/CandidateTermGeneratorService?wsdl