Building Integrated Ontological Knowledge Structures with Efficient Approximation Algorithms.
The integration of ontologies builds knowledge structures which brings new understanding on existing terminologies and their associations. With the steady increase in the number of ontologies, automatic integration of ontologies is preferable over manual solutions in many applications. However, avai...
| Publicado en: | BioMed Research International Vol. 2015; pp. 1 - 15 |
|---|---|
| Autores principales: | , |
| Formato: | algorithm equations & formulas research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
10/13/2015
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=110561840&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 110561840 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 10/13/2015 vid: 2015 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 110561840 110561840 110561840 10.1155/2015/501528 110561840 ppf: 1 ppct: 14 formats: fmt: @attributes: type: P tig: atl: Building Integrated Ontological Knowledge Structures with Efficient Approximation Algorithms. aug: au: Xiang, Yang Janga, Sarath Chandra affil: Department of Biomedical Informatics, The Ohio State University, Columbus, OH 43210, USA sug: subj: Algorithms Ontologies Knowledge Management Genes Classification Descriptive Statistics ab: The integration of ontologies builds knowledge structures which brings new understanding on existing terminologies and their associations. With the steady increase in the number of ontologies, automatic integration of ontologies is preferable over manual solutions in many applications. However, available works on ontology integration are largely heuristic without guarantees on the quality of the integration results. In this work, we focus on the integration of ontologies with hierarchical structures. We identified optimal structures in this problem and proposed optimal and efficient approximation algorithms for integrating a pair of ontologies. Furthermore, we extend the basic problem to address the integration of a large number of ontologies, and correspondingly we proposed an efficient approximation algorithm for integrating multiple ontologies. The empirical study on both real ontologies and synthetic data demonstrates the effectiveness of our proposed approaches. In addition, the results of integration between gene ontology and National Drug File Reference Terminology suggest that our method provides a novel way to perform association studies between biomedical terms. pubtype: Academic Journal doctype: algorithm equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|