Paper Info
Title
Learning Reference-Enriched Approach Towards Large Scale Active Ontology Alignment And Integration
Abstract
With the increasing number of ontologies being designed to represent and manage knowledge in all sorts of sectors, ontology alignment and integration become more and more important in aggregating intelligent efforts on homogenous and heterogeneous data. From the computational perspective, it is challenging due to the ubiquitous existence of diverse classifications of same data. In this paper, we propose an active ontology integration and alignment system, which plugs in expandable learning reference context pool. In the reference context pool, we have integrated WordNet, MeSH, and external curated mapping sources (ICD9 to SNOMEDCT) with an extension to injecting UMLS. The active ontology integration and alignment system takes account of not only subsumption tree but also directed acyclic graph underlying ontologies. It allows 1) finding exact one-to-one matching terms of pairwise ontologies, 2) finding inexact one-to-one term mappings, where two terms have at least a concept in common on basis of the lexical context, and 3) finding one-to-many concept mappings, where one concept can be lexically mapped to the combination of multiple exclusive concepts.
Year
DOI
Venue
2017
10.1109/BIBM.2017.8217908
2017 IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE (BIBM)
Keywords
DocType
ISSN
ontology, ontology alignment, ontology integration, bioassay data
Conference
2156-1125
Citations 
PageRank 
References 
0
0.34
0
Authors
4
Name
Order
Citations
PageRank
Qiong Cheng100.68
Oleg Ursu272.88
Tudor I Oprea335946.89
Stephan C Schürer413114.74