Biomedical word sense disambiguation with ontologies and metadata: automation meets accuracy.

ABSTRACT:

Background

Ontology term labels can be ambiguous and have multiple senses. While this is no problem for human annotators, it is a challenge to automated methods, which identify ontology terms in text. Classical approaches to word sense disambiguation use co-occurring words or terms. However, most treat ontologies as simple terminologies, without making use of the ontology structure or the semantic similarity between terms. Another useful source of information for disambiguation are metadata. Here, we systematically compare three approaches to word sense disambiguation, which use ontologies and metadata, respectively.

Results

The 'Closest Sense' method assumes that the ontology defines multiple senses of the term. It computes the shortest path of co-occurring terms in the doc

SUBMITTER: Alexopoulou D 

PROVIDER: S-EPMC2663782 | biostudies-literature | 2009 Jan

REPOSITORIES: biostudies-literature