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ABSTRACT: Within open, distributed and dynamic environments, agents frequently encounter and communicate with new agents and services
that were previously unknown. However, to overcome the ontological heterogeneitywhich may exist within such environments,
agents first need to reach agreement over the vocabulary and underlying conceptualisation of the shared domain, that will
be used to support their subsequent communication.Whilst there are many existing mechanisms for matching the agents’ individual
ontologies, some are better suited to certain ontologies or tasks than others, and many are unsuited for use in a real-time,
autonomous environment. Agents have to agree on which correspondences between their ontologies are mutually acceptable by
both agents. As the rationale behind the preferences of each agent may well be private, one cannot always expect agents to
disclose their strategy or rationale for communicating. This prevents the use of a centralised mediator or facilitator which
could reconcile the ontological differences. The use of argumentation allows two agents to iteratively explore candidate correspondences
within a matching process, through a series of proposals and counter proposals, i.e., arguments. Thus, two agents can reason over the acceptability of these correspondences without explicitly disclosing the rationale
for preferring one type of correspondences over another. In this chapter we present an overview of the approaches for alignment
agreement based on argumentation.
03/2011: pages 89-111;
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8th International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS 2009), Budapest, Hungary, May 10-15, 2009, Volume 2; 01/2009
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Proceedings of the Poster and Demonstration Session at the 7th International Semantic Web Conference (ISWC2008), Karlsruhe, Germany, October 28, 2008; 01/2008
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Foundations of Intelligent Systems, 17th International Symposium, ISMIS 2008, Toronto, Canada, May 20-23, 2008, Proceedings; 01/2008
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Proceedings of the 1st International Workshop on Ontology Matching (OM-2006) Collocated with the 5th International Semantic Web Conference (ISWC-2006), Athens, Georgia, USA, November 5, 2006; 01/2006
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ABSTRACT: The issue of knowledge sharing has permeated the field of distributed AI and in particular, its successor, multiagent systems. Through the years, many research and engineering efforts have tackled the problem of encoding and sharing knowledge without the need for a single, centralized knowledge base. However, the emergence of modern computing paradigms such as distributed, open systems have highlighted the importance of sharing distributed and heterogeneous knowledge at a larger scale—possibly at the scale of the Internet. The very characteristics that define the Semantic Web—that is, dynamic, distributed, incomplete, and uncertain knowledge—suggest the need for autonomy in distributed software systems. Semantic Web research promises more than mere management of ontologies and data through the definition of machine-understandable languages. The openness and decentralization introduced by multiagent systems and service-oriented architectures give rise to new knowledge management models, for which we can’t make a priori assumptions about the type of interaction an agent or a service may be engaged in, and likewise about the message protocols and vocabulary used. We therefore discuss the problem of knowledge management for open multi-agent systems, and highlight a number of challenges relating to the exchange and evolution of knowledge in open environments, which pertinent to both the Semantic Web and Multi Agent System communities alike.
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ABSTRACT: Efficient agent communication in open and dynamic environments relies on the agents ability to reach a mutual understanding over message exchanges. Such environments are characterized by the existence of different agents, which may commit to different ontologies, with no prior assumptions regarding the use of shared vocabularies. Various approaches have therefore considered how mutually acceptable mappings may be determined dynamically between agents through negotiation. However, this process can be highly complex, reaching Π(p)2 -complete. Whilst it is non-trivial to reduce this complexity, we have explored the use of ontology modularization as a means of reducing the space of possible concepts over which the agents have to negotiate. In this paper, we propose an approach that combines modularization with argumentation to generate focused domains of discourse to facilitate communication. We empirically demonstrate that we can not only reduce the number of alignments required to reach consensus by an average of 75%, but that in 41% of cases, we can identify those agents that would not be able to fully satisfy the request, without the need for negotiation.
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ABSTRACT: Changes in an ontology may have a disruptive impact on any system using it. This impact may depend on structural changes such as name changes or relations between concepts, or it may be related to a change in the expected performance of the reasoning tasks. As the number of systems using ontologies is expected to increase this problem is likely to occur more frequently, and, given the open nature of the Semantic Web, new ontologies and modifications to existing ones are to be expected. Dynamically handling these changes, without requiring human intervention, is a key requirement for successful applications. This paper presents a system that isolates groups of related axioms in an OWL ontology, so that a change in one or more axioms can be automatically localised to a part of the ontology. In addition, we report the results on evaluating the effectiveness of our approach on large ontologies.
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ABSTRACT: Effective communication in open environments relies on the ability of agents to reach a mutual understanding of the exchanged message by reconciling the vocabulary (ontology) used. Various approaches have considered how mutually acceptable mappings between corresponding concepts in the agents' own ontologies may be determined dynamically through argumentation-based negotiation (such as Meaning-based Argumentation). However, the complexity of this process is high, approaching $\Pi_{2}^{(p)}$-complete in some cases. As reducing this complexity is non-trivial, we propose the use of ontology modularization as a means of reducing the space over which possible concepts are negotiated. The suitability of different modularization approaches as filtering mechanisms for reducing the negotiation search space is investigated, and a framework that integrates modularization with Meaning-based Argumentation is proposed. We empirically demonstrate that some modularization approaches not only reduce the number of alignments required to reach consensus, but also predict those cases where a service provider is unable to fully satisfy a request, without the need for negotiation.
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ABSTRACT: This paper presents an overview on the role of agents in the Semantic Web, that was the topic
of the AgentLink Technical Forum Group on “Semantic Web Agents”, aimed at fostering closer
collaboration between the European communities working in these areas. The paper is structured
in three main sections. In the first one, we argue how agents are an essential component of the
Semantic Web, then we provide a brief history, by no mean comprehensive, of how the Semantic
Web vision – that includes agents – has evolved in the past fifteen years. We then conclude
reporting on the topics presented and discussed during the Technical Forum.
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[show abstract]
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ABSTRACT: Effective communication in open environments relies on the ability of agents to reach a mutual understanding of the exchanged message by reconciling the vocabulary (ontology) used. Various approaches have considered how mutually acceptable mappings between corresponding concepts in the agents’ own ontologies may be determined dynami- cally through argumentation-based negotiation (such as Meaning-based Argumentation, MbA). In this paper we present a novel approach to the dynamic determination of mutually acceptable mappings, that allows agents to express a private acceptability threshold over the types of mappings they prefer. We empirically compare this approach with the Meaning-based Argumentation and demonstrate that the proposed approach produces larger agreed alignments thus better enabling agent communication. Furthermore, we compare and evaluate the fitness for purpose of the generated alignments, and we empirically demonstrate that the proposed approach has comparable performance to the MbA approach.
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[show abstract]
[hide abstract]
ABSTRACT: Efficient agent communication in open and dynamic environments relies on the agents ability to reach a mutual understanding over message exchanges. Such environments are characterized by the existence of heterogeneous agents that commit to different ontologies, with no prior assumptions regarding the use of shared vocabularies. Various approaches have therefore considered how mutually acceptable mappings may be determined dynamically between agents through negotiation. In particular, this paper focusses on the meaning based negotiation approach, proposed by Laera et al [1], that makes use of argumentation in order to select a set of mappings that is deemed acceptable by both agents. However, this process can be highly complex, reaching Π(p)2 complete. Whilst it is non-trivial to reduce this complexity, we have explored the use of ontology modularization as a means of reducing the space of possible concepts over which the agents have to negotiate. In this paper, we propose an approach that combines modularization with argumentation to generate focused domains of discourse to facilitate communication. We empirically demonstrate that we can not only reduce the number of alignments required to reach consensus by an average of 75%, but that in 41% of cases, we can identify those agents that would not be able to fully satisfy the request, without the need for negotiation.
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[show abstract]
[hide abstract]
ABSTRACT: Changes in an ontology may have a disruptive impact on any system using it. This impact may depend on structural changes such as introduction or removal of concept definitions, or it may be related to a change in the expected performance of the reasoning tasks. As the number of systems using ontologies is expected to increase, and given the open nature of the Semantic Web, introduction of new ontologies and modifications to existing ones are to be expected. Dynamically handling such changes, without requiring human intervention, becomes crucial. This paper presents a framework that isolates groups of related axioms in an OWL ontology, so that a change in one or more axioms can be automatically localised to a part of the ontology.
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[show abstract]
[hide abstract]
ABSTRACT: In order to support semantic interoperation in open environments, where agents can dynamically join or leave and no prior assumption can be made on the ontologies to align, the different agents involved need to agree on the semantics of the terms used during the interoperation. Reaching this agreement can only come through some sort of negotiation process. Indeed, agents will differ in the domain ontologies they commit to; and their perception of the world, and hence the choice of vocabulary used to represent concepts.
We propose an approach for supporting the creation and exchange of different arguments, that support or reject possible correspondences. Each agent can decide, according to its preferences, whether to accept or refuse a candidate correspondence. The proposed framework considers arguments and propositions that are specific to the matching task and are based on the ontology semantics. This argumentation framework relies on a formal argument manipulation schema and on an encoding of the agents’ preferences between particular kinds of arguments.
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ABSTRACT: In open and dynamic environments, agents will usually differ in the domain ontologies they commit to and their perception of the world. The availability of Alignment Services that are able to provide correspondences between two ontologies is only a partial solution to achieving interoperability between agents, because any given candidate set of alignments is only suitable in certain contexts. For a given context, different agents might have different and inconsistent perspectives that reflect their differing interests and preferences on the acceptability of candidate mappings, each of which may be rationally acceptable. In this paper we introduce an argumentation-based negotiation framework over the terminology they use in order to communicate. This argumentation framework relies on a formal argument manipulation schema and on an encoding of the agents preferences between particular kinds of arguments. The former does not vary between agents, whereas the latter depends on the interests of each agent. Thus, this approach distinguishes clearly between the alignment rationales valid for all agents and those specific to a particular agent.
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ABSTRACT: When agents communicate, they do not necessarily use the same vocabulary or ontology. For them to interact successfully, they must find correspondences (mappings) between the terms used in their respective ontologies. While many proposals for matching two agent ontologies have been presented in the literature, the resulting alignment may not be satisfactory to both agents, and thus may necessitate additional negotiation to identify a mutually agreeable set of alignments.
We propose an approach for supporting the creation and exchange of different arguments, that support or reject possible correspondences. Each agent can decide, according to its preferences, whether to accept or refuse a candidate correspondence. The proposed framework considers arguments and propositions that are specific to the matching task and are based on the ontology semantics. This argumentation framework relies on a formal argument manipulation schema and on an encoding of the agents’ preferences between particular kinds of arguments. Whilst the former does not vary between agents, the latter depends on the interests of each agent. Thus, this approach distinguishes clearly between alignment rationales which are valid for all agents and those specific to a particular agent.
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ABSTRACT: Ontology modularization has received growing interest from the research community lately, since it has been shown to support a variety of tasks for ontology engineers and users alike, such as ontology design, maintenance, reuse; knowledge selection and integration. Most of the research efforts have concentrated on approaches to extract modules, or generate partitions from an input ontology. However these approaches are influenced by different definitions of ontology modularization and thus tend to vary with respect to the concepts and properties in the ontology that should define the module, and on the characteristics that modules should exhibit, which often depend on the task for which the modularization process is performed. This diversity of approaches makes the comparative evaluation of the output of different modularization processes hard to perform. In this paper, we propose an entropy inspired measure for modularization, Integrated Ontology Entropy, that approximates the information content of modules, and hence provides a profile for the module generated. This measure is independent of the modularization technique used, and is calculated as a function of the number of edges connecting the named concepts in the ontology, when a graph representation of the ontology is utilized. In the paper we apply this measure to the modules generated by different modularization techniques and we empirically explore how the measure behaves given the different characteristics of the generated modules, such as the degree of redundancy and the level of connectedness.
