• Aucun résultat trouvé

An Implementation of Agent-Based Ontology Alignment

N/A
N/A
Protected

Academic year: 2022

Partager "An Implementation of Agent-Based Ontology Alignment"

Copied!
1
0
0

Texte intégral

(1)

An Implementation of Agent-Based Ontology Alignment

1

Maxim Davidovsky

1

, Vadim Ermolayev

2

, and Vyacheslav Tolok

3

,

1Zaporozhye National University, Center of Information Technologies, Zhukovskogo st. 66, 69600 Zaporozhye, Ukraine

2Zaporozhye National University, Department of Information Technologies, Zhukovskogo st. 66, 69600 Zaporozhye, Ukraine

3Zaporozhye National University, Department of Mathematical Modelling, Zhukovskogo st. 66, 69600 Zaporozhye, Ukraine

m.davidovsky@gmail.com, vadim@ermolayev.com, vyacheslav-tolok@yandex.ru

Abstract. Various knowledge-based information systems contain distinct knowledge representations reflecting different domains of interest and different viewpoints across domains of discourse. For efficient use of knowledge-based systems it is necessary to know semantic relations or alignment between different knowledge representations. One of the promising approaches is the use of intelligent software agents where agents communicate in order to align respective knowledge representations. The paper presents an approach for ontology alignment based on implementation of meaning negotiation between intelligent agents. In the approach, negotiation leads in iterative way. On each step agents compare ontological contexts and use propositional substitutions in order to reduce semantic distance between the contexts. The focus of the paper is the implementation of agents’ negotiation strategy.

Keywords. Ontology, ontology alignment, intelligent agent, meaning negotiation, implementation.

Key Terms. KnowledgeRepresentation, MultiAgentSystem, Collaboration

1 This UNISCON 2012 paper has been invited for a presentation at ICTERI 2012. The paper will appear in full in the post-proceedings of UNISCON 2012.

Références

Documents relatifs

The Ontologies Handler is used for uploading source and target ontologies (returns Ontology IDs), retrieving ontological elements specified (based on Ontology ID) as

The general representation and reasoning framework that we propose includes: 1) a declarative language to specify networks of ontologies and alignments, with inde- pendent control

The approach presented in this paper, in turn, proposes a directly use of foundational ontologies to improve semantic integration by considering some alignment

The problem of alignment between the elements of the second type is therefore to extend the maximal ontology signature isomorphism with correspondences while

The input for the recommendation sessions consists of a database of algorithms for the preprocessing, matching, combination and filtering in the computation

• EA - Evaluation Agent: make a judgment as to the relatedness of available ontologies along some relevant dimension (e.g., domain relevance, semantic

A small particle needs less computation time than a big particle, therefore a worker with smaller particles will require less runtime per iteration.. The random initialisation

In this paper we have presented an extension of our recent work on BLOOMS [1] which allows users to plug-in auxiliary sources of their choice as oracles in the task of