Results 261 to 270 of about 134,903 (295)

Large-Scale Ontology Matching

ACM Computing Surveys, 2018
Ontologies have become a popular means of knowledge sharing and reuse. This has motivated the development of large-sized independent ontologies within the same or different domains with some overlapping information among them. To integrate such large ontologies, automatic matchers become an inevitable solution.
Peter Ochieng, Swaib Kyanda
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Geographic Ontology Matching with iG-Match

2007
To achieve accurate results when matching geographic ontologies, it is important to have clear what has to be compared, and just then start comparing them. In this paper we define a geographic ontology reference model and, from it, the set of heterogeneities that may occur when comparing two geographic ontologies is elaborated, at both the concept and ...
Guillermo Nudelman Hess, Cirano Iochpe, Silvana Castano   +2 more
openaire   +1 more source

Secured Ontology Matching Using Graph Matching

2013
Today’s market evolution and high volatility of business requirements put an increasing emphasis on the ability for systems to accommodate the changes required by new organizational needs while maintaining security objectives satisfiability. This is all the more true in case of collaboration and interoperability between different organizations and thus
K. Manjula Shenoy, K. C. Shet, U. Dinesh Acharya   +2 more
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Ontology Matching across Domains

2012
Ontologies are often used to annotate information (metadata) that is passed between domains during negotiation. In that sense, Ontology matching is critical for the receiving domain to gather the correct meaning of the data, and hence critical for interoperability.
Renato Levy, Jakob Henriksson, Margaret Lyell, Xiong Liu, Michael J. Mayhew   +4 more
openaire   +1 more source

Dynamic Ontology Matching Challenge

2023
The labour market is currently heavily struggling with friction in demand and supply. Skills-based approaches deliver promising results. These approaches ask for a common and up-to-date skills language to achieve their full potential. Skills ontologies such as ESCO and O*NET exist, but tend to get outdated soon, as the labour market changes quickly and
Boer, M.H.T. De, Oosterheert, L., Bakker, R.M.   +2 more
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On the Ontology Instance Matching Problem

2008 19th International Conference on Database and Expert Systems Applications, 2008
Most of the existing work on ontology matching is focused at the schema level, while, at the time being, instance-level matching techniques are only marginally considered. In this paper, we address the problem of ontology instance matching by providing a reference definition and by discussing key requirements and possible application fields.
S. Castano, A. Ferrara, D. Lorusso, S. Montanelli   +3 more
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Supporting Database Evolution: Using Ontologies Matching

2003
In this paper, we propose a methodology for managing database evolutions by exploiting two ontologies. The first ontology describes the changes occurring in a database application. The second one aims at characterizing techniques and tools useful for database change management.
Lammari, Nadira, Akoka, Jacky, Comyn-Wattiau, Isabelle   +2 more
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IROM: Information Retrieval-Based Ontology Matching

2011
A crucial piece of semantic web development is the creation of viable ontology matching approaches to ensure interoperability in a wide range of applications such as information integration and semantic multimedia. In this paper, a new approach for ontology matching called IROM (Information Retrieval-based Ontology Matching) is presented. This approach
Mousselly-Sergieh, Hatem, Unland, Rainer
openaire   +2 more sources

Ontology Matching

2008
Underlying heterogeneous systems, multiple ontologies are created that describe same or overlapping domains. Software agents (or tools in general) need to learn how to translate between such related ontologies. Only then will they be able to exploit the information from various sources simultaneously.
openaire   +2 more sources

Matching weak informative ontologies

Science China Information Sciences, 2021
Peng Wang, Baowen Xu
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