Miklos Nagy

Multiagent Ontology Mapping Framework for the Semantic Web

Ontology mapping is a prerequisite for achieving heterogeneous data integration on the Semantic Web. The vision of the Semantic Web implies that a large number of ontologies present on the web need to be aligned before one can make use of them, for example, a question answering on the Semantic Web. At the same time, these ontologies can be used as domain-specific background knowledge by the ontology mapping systems to increase the mapping precision. However, these ontologies can differ in representation, quality, and size that pose different challenges to ontology mapping. In this paper, we analyze these challenges and introduce a multiagent ontology mapping framework that has been designed to operate effectively in the Semantic Web environment.

Multi agent ontology mapping framework in the AQUA question answering system

This paper describes an ontology-mapping framework in the context of query answering (QA). In order to incorporate uncertainty inherent to the mapping process, the system uses the Dempster-Shafer model for dealing with incomplete and uncertain information produced during the mapping. A novel approach is presented how specialized agents with partial local knowledge of the particular domain achieve ontology mapping without creating global or reference ontology. Our approach is particularly fit for a query-answering scenario, where an answer needs to be created in real time that satisfies the query posed by the user.

Managing Conflicting Beliefs with Fuzzy Trust on the Semantic Web

Automated ontology mapping approaches often combine similarity measures in order to increase the quality of the proposed mappings. When the mapping process of human experts is modeled with software agents that assess similarities, it can lead to situations where the beliefs in the assessed similarities becomes contradicting. The combination of these contradicting beliefs can easily worsen the mapping precision and recall, which leads to poor performance of any ontology mapping algorithm. Typically mapping algorithms, which use different similarities and combine them into a more reliable and coherent view can easily become unreliable when these contradictions are not managed effectively between the different sources. In this paper we propose a solution based on the fuzzy voting model for managing such situations by introducing trust and voting between software agents that resolve contradicting beliefs in the assessed similarities.

Multi-Agent Ontology Mapping with Uncertainty on the Semantic Web

The increasing number of ontologies of the semantic web poses new challenges for ontology mapping. Ontology mapping in the context of question answering can provide more correct results if the mapping process can deal with uncertainty effectively that is caused by the incomplete and inconsistent information used and produced by the mapping process. We present a novel approach of how Dempster-Shafer belief functions can be used to represent uncertain similarities created by both syntactic and semantic similarity algorithms. For ontology mapping in the context of question answering on the semantic web we propose a multi agent framework where agents create dynamic ontology mappings in order to integrate information and provide precise answers for the users query. We also discuss the problems which can be encountered if we have conflicting beliefs between agents in a particular mapping.

Establishing agent trust for contradictory evidence by means of fuzzy voting model: An ontology mapping case study

This paper introduces a novel trust assessment formalism for contradicting evidence in the context of multi-agent ontology mapping. Evidence combination using the Dempster rule tend to ignore contradictory evidence and the contemporary approaches for managing these conflicts introduce additional computation complexity i.e. increased response time of the system. On the Semantic Web, ontology mapping systems that need to interact with end users in real time cannot afford prolonged computation. In this work, we have made a step towards the formalisation of eliminating contradicting evidence, to utilise the original Dempsters combination rule without introducing additional complexity. Our proposed solution incorporates the fuzzy voting model to the Dempster-Shafer theory. Finally, we present a case study where we show how our approach improves the ontology mapping problem.

Reaching consensus over contradictory interpretation of Semantic Web data for ontology mapping

Software agents that need to interpret the possible meaning of Semantic Web data should be able to deal with scenarios where the different agents belief becomes contradicting. This is especially true for ontology mapping where different agents using different similarity measures create beliefs in the assessed similarities and this needs to be combined into a more coherent state. The combination of these contradicting beliefs can easily worsen the mapping precision and recall, which leads to poor performance of any ontology mapping algorithm. Typically mapping algorithms, which use different similarities and combine them into a more reliable and coherent view can easily become unreliable when these contradictions are not managed effectively between the different sources. In this paper we propose a solution based on the fuzzy voting model for managing such situations by introducing trust and voting between software agents that resolve contradicting beliefs in the assessed similarities.

State of the Art on Ontology Alignment

Ontology mapping as a semantic data integration approach has evolved from traditional data integration solutions. The core problems and open issues related to early data integration approaches are also applicable to ontology mapping on the Semantic Web community. Therefore, in this review the authors present the related literature, starting from the traditional data integration approaches, in order to highlight the evolution of data integration from the early approaches. Once the roots of semantic data integration have been presented, the authors proceed to introduce the state-of-the-art of the ontology mappings systems including the early approaches and the systems that can be compared through the Ontology Alignment Initiative OAEI.

Dealing with Contradictory Evidence Using Fuzzy Trust in Semantic Web Data

Term similarity assessment usually leads to situations where contradictory evidence support has different views concerning the meaning of a concept and how similar it is to other concepts. Human experts can resolve their differences through discussion, whereas ontology mapping systems need to be able to eliminate contradictions before similarity combination can achieve high quality results. In these situations, different similarities represent conflicting ideas about the interpreted meaning of the concepts. Such contradictions can contribute to unreliable mappings, which in turn worsen both the mapping precision and recall. In order to avoid including contradictory beliefs in similarities during the combination process, trust in the beliefs needs to be established and untrusted beliefs should be excluded from the combination. In this chapter, we propose a solution for establishing fuzzy trust to manage belief conflicts using a fuzzy voting model.

Experimental Evaluation of Multi-Agent Ontology Mapping Framework

Ontology mapping is a prerequisite for achieving heterogeneous data integration on the Semantic Web. The vision of the Semantic Web implies that a large number of ontologies are present on the Web that needs to be aligned before one can make use of them e.g. question answering on the Semantic Web. During the recent years a number of mapping algorithms, frameworks and tools have been proposed to address the problem of ontology mapping. Unfortunately comparing and evaluating these tools is not a straightforward task as these solutions are mainly designed for different domains. In this paper we introduce our ontology mapping framework called “DSSim” and present an experimental evaluation based on the tracks of the Ontology Alignment Evaluation Initiative (OAEI 2008).

Multi agent trust for belief combination on the Semantic Web

Software agents that assess similarities between concepts on the semantic Web has to deal with scenarios where the beliefs in the assessed similarities becomes contradicting. The combination of these contradicting beliefs can easily worsen the mapping precision and recall, which leads to poor performance of any ontology mapping algorithm. Typically mapping algorithms, which use different similarities and combine them into a more reliable and coherent view can easily become unreliable when these contradictions are not managed effectively between the different sources. In this paper we propose a solution based on the fuzzy voting model for managing such situations by introducing trust and voting between software agents that resolve contradicting beliefs in the assessed similarities.