A Cooperative Approach for Composite Ontology Matching

Ontologies have proven to be an essential element in a range of applications in which knowl-edge plays a key role. Resolving the semantic heterogeneity problem is crucial to allow the interoperability between ontology-based systems. This makes automatic ontology matching, as an anticipated solution to semantic heterogeneity, an important, research issue. Many dif-ferent approaches to the matching problem have emerged from the literature. An important issue of ontology matching is to find effective ways of choosing among many techniques and their variations, and then combining their results. An innovative and promising option is to formalize the combination of matching techniques using agent-based approaches, such as cooperative negotiation and argumentation. In this thesis, the formalization of the on-tology matching problem following an agent-based approach is proposed. Such proposal is evaluated using state-of-the-art data sets. The results show that the consensus obtained by negotiation and argumentation represent intermediary values which are closer to the best matcher. As the best matcher may vary depending on specific differences of multiple data sets, cooperative approaches are an advantage. *** RESUMO - Ontologias são elementos essenciais em sistemas baseados em conhecimento. Resolver o problema de heterogeneidade semântica é fundamental para permitira interoperabilidade entre sistemas baseados em ontologias. Mapeamento automático de ontologias pode ser visto como uma solução para esse problema. Diferentes e complementares abordagens para o problema são propostas na literatura. Um aspecto importante em mapeamento consiste em selecionar o conjunto adequado de abordagens e suas variações, e então combinar seus resultados. Uma opção promissora envolve formalizara combinação de técnicas de ma-peamento usando abordagens baseadas em agentes cooperativos, tais como negociação e argumentação. Nesta tese, a formalização do problema de combinação de técnicas de ma-peamento usando tais abordagens é proposta e avaliada. A avaliação, que envolve conjuntos de testes sugeridos pela comunidade científica, permite concluir que o consenso obtido pela negociação e pela argumentação não é exatamente a melhoria de todos os resultados individuais, mas representa os valores intermediários que são próximo da melhor técnica. Considerando que a melhor técnica pode variar dependendo de diferencas específicas de múltiplas bases de dados, abordagens cooperativas são uma vantagem

Ontology Mapping for a Legal Question Answering System

Legal information retrieval systems, such question answering, use legal ontologies to represent semantic objects, to associate them with legal documents and to make inferences about them. The ontology mapping process can help users to reuse and compare information from different ontologies. In this paper we present a review on legal ontologies and present an approach to ontology mapping based on argumentation. Individual mappings are computed by specialized agents using different mapping approaches. Next, these agents use argumentation to exchange their local results, in order to agree on the obtained mappings. To each argument is associated a strength, representing how confident an agent is in the similarity of two ontology terms. Based on their preferences and confidence of the arguments, the agents compute their preferred mapping sets. The arguments in such preferred sets are viewed as the set of globally acceptable arguments. This work is part of a question answering system for the legal domain

An Argumentation Framework based on strength for Ontology Mapping

In the field of ontology mapping, using argumentation to combine different mapping approaches is an innovative research area. We had extended the Value-based Argumentation Framework (VAF) in order to represent arguments with confidence degrees, according to the similarity degree between the terms being mapped. The mappings are computed by agents using different mapping approaches. Based on their preferences and confidences, the agents compute their preferred mapping sets. The arguments in such preferred sets are viewed as the set of globally acceptable arguments. In previous work we had used discrete classes to represent the confidence degrees (certainty and uncertainty). In this paper, we propose to use continuous values from the interval [0,1]. Here, confidence is treated as strength. Using a threshold for the strength we can reduce the set of mappings and adjust the values of precision. We evaluate the use of strength against the previous confidence as discrete classes. The results are promising, especially what concerns precision

A Framework for Multilingual Ontology Mapping

This paper proposes a framework for mapping multilingual Description Logics ontologies. First, the DL source ontology is translated to the target ontology language, using a lexical database or a dictionary, generating a translated ontology. The target and the translated ontologies are then used as input for the mapping process. A DL mapping ontology is generated as result of this process

Matching Law Ontologies using an Extended Argumentation Framework based on Confidence Degrees

Law information retrieval systems use law ontologies to represent semantic objects, to associate them with law documents and to make inferences about them. A number of law ontologies have been proposed in the literature, what shows the variety of approaches pointing to the need of matching systems. We present a proposal based on argumentation to match law ontologies, as an approach to be considered for this problem. Argumentation is used to combine different techniques for ontology matching. Such approaches are encapsulated by agents that apply individual matching algorithms and cooperate in order to exchange their local results (arguments). Next, based on their preferences and confidence, the agents compute their preferred matching sets. The arguments in such preferred sets are viewed as the set of globally acceptable arguments. We show the applicability of our model matching two legal core ontologies: LKIF and CLO

An Argumentation Framework based on Confidence Degrees to Combine Ontology Mapping Approaches

Ontology mapping has a key importance for applications such as information retrieval, database integration, and agent-communication. This paper presents an Argumentation Framework, with confidence degrees associated to the arguments, to combine ontology mapping approaches. Our agents apply individual mapping algorithms and cooperate in order to exchange their local results (arguments). Based on their preferences and confidence of the arguments, the agents compute their preferred mapping sets. The arguments in such preferred sets are viewed as the set of globally acceptable arguments. The model is evaluated using a benchmark for ontology mapping. The results are promising especially what concerns precision

Using an Extended Argumentation Framework based on Confidence Degrees for Legal Core Ontology Mapping

Web legal information retrieval systems use legal ontologies to represent semantic objects, to associate them with legal documents and to make inferences about them. The quality of the output of these systems can be improved with the ontology completeness, which can be obtained by the ontology merging process. The first step in this process is the ontology mapping. This paper proposes to use abstract argumentation frameworks to combine ontology mapping approaches. We extend the Value-based Argumentation Framework (VAF)[1], in order to represent arguments with confidence degrees. Our agents apply individual mapping algorithms and cooperate in order to exchange their local results (arguments). Next, based on their preferences and confidence of the arguments, the agents compute their preferred mapping sets. The arguments in such preferred sets are viewed as the set of globally acceptable arguments. We applied our model to map two legal core ontologies, LRI-Core and DOLCE-Lite, and to map LRI-Core with SUMO generic core ontology

Using Quantitative Aspects of Alignment Generation for Argumentation on Mappings

State-of-the art mappers articulate several techniques using different sources of knowledge in an unified process. An important issue of ontology mapping is to find ways of choosing among many techniques and their variations, and then combining their results. For this, an innovative and promising option is to use frameworks dealing with arguments for or against correspondences. In this paper, we re-use an argumentation framework that considers the confidence levels of mapping arguments. We also propose new frameworks that use voting as a way to cope with various degrees of consensus among arguments. We compare these frameworks by evaluating their application to a range of individual mappers, in the context of a real-world library case

Automating OAEI campaigns (first report)

trojahn2010cInternational audienceThis paper reports the first effort into integrating OAEI and SEALS evaluation campaigns. The SEALS project aims at providing standardized resources (software components, data sets, etc.) for automatically executing evaluations of typical semantic web tools, including ontology matching tools. A first version of the software infrastructure is based on the use of a web service interface wrapping the functionality of a matching tool to be evaluated. In this setting, the evaluation results can visualized and manipulated immediately in a direct feedback cycle. We describe how parts of the OAEI 2010 evaluation campaign have been integrated into this software infrastructure. In particular, we discuss technical and organizational aspects related to the use of the new technology for both participants and organizers of the OAEI

Cooperative Approach for Composite Ontology Mapping

This paper proposes a cooperative approach for composite ontology mapping. We first present an extended classification of automated ontology matching and propose an automatic composite solution for the matching problem based on cooperation. In our proposal, agents apply individual mapping algorithms and cooperate in order to change their individual results. We assume that the approaches are complementary to each other and their combination produces better results than the individual ones. Next, we compare our model with three state of the art matching systems. The results are promising specially for what concerns precision and recall. Finally, we propose an argumentation formalism as an extension of our initial model. We compare our argumentation model with the matching systems, showing improvements on the results