A model to predict quality of a reduced ontology for Web service discovery on mobile devices

Abstract

AbstractAs Web Services and the Semantic Web become more important, enabling technologies such as Web service ontologies will grow larger. At the same time, use of mobile devices to access Web services has doubled in the last year. The ability of these resource-constrained devices to download and reason across ontologies to support service discovery are severely limited. Since concrete agents typically only needs a subset of what is described in a Web service ontology to complete their task, a reduced ontology can be created. Measuring the quality of a reduced ontology, in both knowledge content and performance, is a nontrivial task. Expert analysis of the ontologies is time-consuming and unreliable. We propose two measures of knowledge content and performance. Mean average recall (MAR) with respect to the original ontology compares the data returned from a series of queries related to a particular concept of interest. Mean average performance (MAP) compares the download and reasoning speedup of the reduced ontology with respect to the original ontology. Neither of these values can be computed easily, therefore we propose a set of ontology metrics to predict these values. In this paper, we develop two prediction models for MAR and MAP based on these metrics. These models are based on analysis of 23 ontologies from five domains. To compute MAR, a specific set of queries for each domain was applied to each candidate reduced ontology along with the original ontology. To compute MAP, a simulated mobile device will download and process of each ontology along with the original ontology. We believe this model allows a speedy selection of a reduced ontology that contains the knowledge content and performance speedup needed by a mobile device for service discovery.