Paper Unlock

Authors: André Ippolito and Jorge Rady de Almeida Júnior

Affiliation: Polytechnic School of University of São Paulo, Brazil

ISBN: 978-989-758-187-8

ISSN: 2184-4992

Keyword(s): Ontology Matching, Aspect, Consensus Clustering, Bayesian Cluster Ensembles, Community Detection.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Biomedical Engineering ; Cloud Computing ; Collaboration and e-Services ; Complex Systems Modeling and Simulation ; Coupling and Integrating Heterogeneous Data Sources ; Data Engineering ; Data Mining ; Databases and Information Systems Integration ; e-Business ; Enterprise Information Systems ; Health Information Systems ; Information Systems Analysis and Specification ; Integration/Interoperability ; Interoperability ; Knowledge Engineering and Ontology Development ; Knowledge Management ; Knowledge Management and Information Sharing ; Knowledge-Based Systems ; Ontologies and the Semantic Web ; Ontology Engineering ; Semantic Web Technologies ; Sensor Networks ; Services Science ; Signal Processing ; Simulation and Modeling ; Society, e-Business and e-Government ; Soft Computing ; Software Agents and Internet Computing ; Software and Architectures ; Symbolic Systems ; Web Information Systems and Technologies

Abstract: With the increase in the number of existing ontologies, ontology integration becomes a challenging task. A fundamental step in ontology integration is ontology matching, which is the process of finding correspondences between elements of different ontologies. For large-scale ontology matching, some authors developed a divide-and-conquer strategy, which partitions ontologies, clusters similar partitions and restricts the matching process to ontology elements of similar partitions. Works related to this strategy considered only a single ontology aspect for clustering. In this paper, we proposed a solution for ontology matching based on Bayesian Cluster Ensembles (BCE) of multiple aspects of ontology partitions. We partition ontologies applying Community Detection techniques. We believe that BCE of multiple aspects of ontology partitions can provide an ontology clustering that is more precise than the clustering of a single aspect. This can result in a more precise matching.

PDF ImageFull Text


Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Ippolito, A. and Júnior, J. (2016). Ontology Matching based on Multi-Aspect Consensus Clustering of Communities.In Proceedings of the 18th International Conference on Enterprise Information Systems - Volume 2: ICEIS, ISBN 978-989-758-187-8, ISSN 2184-4992, pages 321-326. DOI: 10.5220/0005893103210326

author={André Ippolito. and Jorge Rady de Almeida Júnior.},
title={Ontology Matching based on Multi-Aspect Consensus Clustering of Communities},
booktitle={Proceedings of the 18th International Conference on Enterprise Information Systems - Volume 2: ICEIS,},


JO - Proceedings of the 18th International Conference on Enterprise Information Systems - Volume 2: ICEIS,
TI - Ontology Matching based on Multi-Aspect Consensus Clustering of Communities
SN - 978-989-758-187-8
AU - Ippolito, A.
AU - Júnior, J.
PY - 2016
SP - 321
EP - 326
DO - 10.5220/0005893103210326

Login or register to post comments.

Comments on this Paper: Be the first to review this paper.