Using ontologies for collaborative information management: some challenges and ideas

Authors:
Victor Guevara-Masis;Ozgul Unal;Ersin C. Kaletas;Hamideh Afsarmanesh;Louis O. Hertzberger
Affiliations:
Informatics Institute, University of Amsterdam, Amsterdam, The Netherlands;Informatics Institute, University of Amsterdam, Amsterdam, The Netherlands;Informatics Institute, University of Amsterdam, Amsterdam, The Netherlands;Informatics Institute, University of Amsterdam, Amsterdam, The Netherlands;Informatics Institute, University of Amsterdam, Amsterdam, The Netherlands
Venue:
ADVIS'04 Proceedings of the Third international conference on Advances in Information Systems
Year:
2004

Citing 7
Cited 0

InfoSleuth: agent-based semantic integration of information in open and dynamic environments

SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
OBSERVER: An Approach for Query Processing in Global Information Systems Based on Interoperation Across Pre-Existing Ontologies

Distributed and Parallel Databases
Ontologies for Agents

IEEE Internet Computing
Generic Schema Matching with Cupid

Proceedings of the 27th International Conference on Very Large Data Bases
A survey of approaches to automatic schema matching

The VLDB Journal — The International Journal on Very Large Data Bases
VLAM-G: A Grid-based virtual laboratory

Scientific Programming
COMA: a system for flexible combination of schema matching approaches

VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases

Quantified Score

Hi-index	0.01

Visualization

Abstract

Emerging collaborative networks (CNs) bring together the need for an effective collaborative information management (CIM) infrastructure. Achieving a common understanding of the concepts and entities in CNs, especially with the presence of different types of heterogeneity involved in such environments, represents a big challenge for the developers of the CIM infrastructure. This paper proposes some ontology-based solutions to this challenge in three different research domains. Among the described solutions, the first one addresses the challenges of data representation and implementation regarding the translation of semantic model into appropriate data structures, the second one suggests an ontology-based semi-automatic approach for the resolution of schematic and semantic heterogeneity, and the third one aims to assist scientists from different e-science domains when customizing their experimentation environments with user-defined experiments and data types.