Learning reference-enriched approach towards large scale active ontology alignment and integration

Qiong Cheng, Oleg Ursu, Tudor I. Oprea, Stephan C Schuerer

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

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.

Original languageEnglish (US)
Title of host publicationProceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1658-1663
Number of pages6
Volume2017-January
ISBN (Electronic)9781509030491
DOIs
StatePublished - Dec 15 2017
Event2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017 - Kansas City, United States
Duration: Nov 13 2017Nov 16 2017

Other

Other2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017
CountryUnited States
CityKansas City
Period11/13/1711/16/17

Fingerprint

Systems Integration
Ontology
Unified Medical Language System
Learning

Keywords

  • bioassay data
  • ontology
  • ontology alignment
  • ontology integration

ASJC Scopus subject areas

  • Biomedical Engineering
  • Health Informatics

Cite this

Cheng, Q., Ursu, O., Oprea, T. I., & Schuerer, S. C. (2017). Learning reference-enriched approach towards large scale active ontology alignment and integration. In Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017 (Vol. 2017-January, pp. 1658-1663). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BIBM.2017.8217908

Learning reference-enriched approach towards large scale active ontology alignment and integration. / Cheng, Qiong; Ursu, Oleg; Oprea, Tudor I.; Schuerer, Stephan C.

Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017. Vol. 2017-January Institute of Electrical and Electronics Engineers Inc., 2017. p. 1658-1663.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Cheng, Q, Ursu, O, Oprea, TI & Schuerer, SC 2017, Learning reference-enriched approach towards large scale active ontology alignment and integration. in Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017. vol. 2017-January, Institute of Electrical and Electronics Engineers Inc., pp. 1658-1663, 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017, Kansas City, United States, 11/13/17. https://doi.org/10.1109/BIBM.2017.8217908
Cheng Q, Ursu O, Oprea TI, Schuerer SC. Learning reference-enriched approach towards large scale active ontology alignment and integration. In Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017. Vol. 2017-January. Institute of Electrical and Electronics Engineers Inc. 2017. p. 1658-1663 https://doi.org/10.1109/BIBM.2017.8217908
Cheng, Qiong ; Ursu, Oleg ; Oprea, Tudor I. ; Schuerer, Stephan C. / Learning reference-enriched approach towards large scale active ontology alignment and integration. Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017. Vol. 2017-January Institute of Electrical and Electronics Engineers Inc., 2017. pp. 1658-1663
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