Biomedical word sense disambiguation with ontologies and metadata: automation meets accuracy.
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 document to one of these senses. The 'Term Cooc' method defines a log-odds ratio for co-occurring terms including co-occurrences inferred from the ontology structure. The 'MetaData' approach trains a classifier on metadata. It does not require any ontology, but requires training data, which the other methods do not. To evaluate these approaches we defined a manually curated training corpus of 2600 documents for seven ambiguous terms from the Gene Ontology and MeSH. All approaches over all conditions achieve 80% success rate on average. The 'MetaData' approach performed best with 96%, when trained on high-quality data. Its performance deteriorates as quality of the training data decreases. The 'Term Cooc' approach performs better on Gene Ontology (92% success) than on MeSH (73% success) as MeSH is not a strict is-a/part-of, but rather a loose is-related-to hierarchy. The 'Closest Sense' approach achieves on average 80% success rate.


CONCLUSIONS
Metadata is valuable for disambiguation, but requires high quality training data. Closest Sense requires no training, but a large, consistently modelled ontology, which are two opposing conditions. Term Cooc achieves greater 90% success given a consistently modelled ontology. Overall, the results show that well structured ontologies can play a very important role to improve disambiguation.


BACKGROUND
The three benchmark datasets created for the purpose of disambiguation are available in Additional file 1.
Alexopoulou
Dimitra
D
Biotechnology Center (BIOTEC), Technische Universität Dresden, 01062, Dresden, Germany. dimitra.alexopoulou@boitec.tu-dresden.de
Andreopoulos
Bill
B
Dietze
Heiko
H
Doms
Andreas
A
Gandon
Fabien
F
Hakenberg
Jörg
J
Khelif
Khaled
K
Schroeder
Michael
M
Wächter
Thomas
T
D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D016247:Information Storage and Retrieval; D008490:Medical Informatics / Q000379:methods; D046650:Medical Subject Headings; D010363:Pattern Recognition, Automated; D017432:Unified Medical Language System; D018875:Vocabulary, Controlled