Dynamic Change Evaluation for Ontology Evolution in the Semantic Web

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

Using Ontology Modularization for Efficient Negotiation over Ontology Correspondences in MAS

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 ?(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

Dynamic selection of ontological alignments: a space reduction mechanism

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 Π2(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

Evaluating Ontology Modules Using an Entropy Inspired Metric

The focus of ontology modularization to date has largely been on the creation of techniques to carry out ontology modularization. This creates a problem in evaluating the results of the different techniques. Ontology modularization techniques cannot solely be evaluated by examining their logical properties. Certain applications of ontology modularization, such as ontology reuse, require a new objective way to evaluate the results. This paper motivates the use of an entropy inspired measure to evaluate ontology modules by arguing that current objective measures of evaluation do not reconcile with the subjective measures employed by Ontology Engineers. Experiments are conducted to show that an entropy based evaluation of ontology modules is beneficial to an Ontology Engineer evaluating the results of ontology module extraction techniques

Dynamic ontology evolution in open environments

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