Paper Info
Title
Hybrid pattern matching for complex ontology term recognition
Abstract
Ontology term recognition is a key task of ontology-based text mining. Previous approaches of statistical analysis and syntactic pattern matching have such limitations that they do not consider relations between words and that their handcrafted patterns are expensive and show low coverage, respectively. These limitations are critical especially when dealing with long and complex ontology terms. We propose a hybrid approach that combines the two approaches sequentially: It first uses syntactic pattern matching and, when its results are partial due to lack of required patterns, then completes them with supplementary evidence from a statistical method. Additionally, we present a novel method that automatically learns syntactic patterns from an annotated corpus. We tested the proposed approach for the tasks of recognizing Gene Ontology (GO) terms in text and also of associating the GO terms with proteins. When compared with existing systems of statistical analysis and syntactic pattern matching, it significantly improves 'relative' recall by 11%~13% and F-score by 7%.
Year
DOI
Venue
2012
10.1145/2382936.2382973
BCB
Keywords
Field
DocType
syntactic pattern matching,complex ontology term,hybrid approach,hybrid pattern,uses syntactic pattern matching,statistical analysis,approaches sequentially,required pattern,statistical method,handcrafted pattern,syntactic pattern,complex ontology term recognition,text mining
Ontology (information science),Ontology,Ontology-based data integration,Process ontology,Computer science,Artificial intelligence,Natural language processing,Suggested Upper Merged Ontology,Upper ontology,Syntax,Pattern matching
Conference
Citations 
PageRank 
References 
1
0.34
8
Authors
2
Name
Order
Citations
PageRank
Jung-Jae Kim134327.42
Anh Tuan Luu217711.34