Paper Info
Title
Adapting Gloss Vector Semantic Relatedness Measure for Semantic Similarity Estimation: An Evaluation in the Biomedical Domain.
Abstract
Automatic methods of ontology alignment are essential for establishing interoperability across web services. These methods are needed to measure semantic similarity between two ontologies' entities to discover reliable correspondences. While existing similarity measures suffer from some difficulties, semantic relatedness measures tend to yield better results; even though they are not completely appropriate for the 'equivalence' relationship (e.g. "blood" and "bleeding" related but not similar). We attempt to adapt Gloss Vector relatedness measure for similarity estimation. Generally, Gloss Vector uses angles between entities' gloss vectors for relatedness calculation. After employing Pearson's chi-squared test for statistical elimination of insignificant features to optimize entities' gloss vectors, by considering concepts' taxonomy, we enrich them for better similarity measurement. Discussed measures get evaluated in the biomedical domain using MeSH, MEDLINE and dataset of 301 concept pairs. We conclude Adapted Gloss Vector similarity results are more correlated with human judgment of similarity compared to other measures.
Year
DOI
Venue
2013
10.1007/978-3-319-06826-8_11
Lecture Notes in Computer Science
Keywords
Field
DocType
Semantic web,Similarity measure,Relatedness measure,UMLS,MEDLINE,MeSH,Bioinformatics,Pearson's Chi-squared test,Text mining,Ontology alignment,Computational linguistics
Semantic similarity,Ontology (information science),Ontology alignment,Information retrieval,Similarity measure,Computer science,Computational linguistics,Semantic Web,Equivalence (measure theory),Unified Medical Language System
Conference
Volume
ISSN
Citations 
8388
0302-9743
3
PageRank 
References 
Authors
0.39
14
3
Name
Order
Citations
PageRank
Ahmad Pesaranghader1284.20
Azadeh Rezaei281.49
Ali Pesaranghader3293.16