Enterprise Ontology Merging for the Semantic Web

Abstract Semantic Web applications are dependent on the successful merging of a number of weakly-structured, disparate data sources. The process of integrating data can be very time consuming and difficult. Due to this level of complexity, soft computing methods have been frequently employed to assist in this process. This paper defines a novel approach of applying clustering techniques to assist in the merging of ontologies. For support of enterprise processes we provide consideration to terminological components as well as instance relationships. Here, instance-rich ontologies are likened to applications of web document clustering for the purpose of Semantic Web-based definition. Our methodology applies clustering to each ontology at both the concept and attribute levels to support the integration process. We view the consideration of both terms and instance data together as integral components for scaling up applications to support the Semantic Web. 1. Introduction The Semantic Web will only reach its highest potential after every associated data source is presented within a single common language. This common language is dependent on the integration of ontologies. By definition, ontologies are regarded as an abstraction of a conceptualization containing explicitly defined concepts and associated relations (Gruber 1992). By the merging of the concepts, we intend to support Semantic Web applications through reduction of redundancy. Since scaling such applications to industrial levels is a substantial task, it will be necessary to establish both methods and associated metrics in order to determine similarity in our data source. Our primary focus is to utilize Semantic Web techniques to support the integration of data. We view enterprise integration as the study of an organization and relating each of them to another via a focus on the organizational process, application, data and networks (Maule and Gallup 2007). Ontologies are applied to the enterprise, by creating a single layer of data which resides on top of established (legacy) data sources with existing vocabularies. On top of this newly integrated layer lies potential development of more powerful applications than previously supported. Such applications will be able to explore the breadth of company data that was not previously available. In large scale legacy applications, there are substantial amounts of data associated with each individual concept. This data can present a substantial opportunity in terms of assisting in the merging process. In development of this layer of data, an associated methodology is presented with our example in order to assist in the complete translation of data. Here, we apply our decision process to drive the development of rules which will support the translations of our potential ontologies (Ostrowski 2007). We view our process and methodology to be a semi-automatic approach to the merging process. In our approach, metrics are established for the knowledge at both the conceptual and attribute level. Making comparisons between enterprise instance data and web based content, we apply clustering for the support of the analysis process for the eventual translation of ontologies. Through the evaluation of similarities in our data, we hope to determine a common reference point for knowledge-based inference applications. We derive this approach at the conceptual level as related to instance based data. In this paper, we present the application of text clustering to merging ontologies. Our approach assists in integration of legacy based applications within the enterprise environment. In Section Two, we present background information concerning the merging of ontologies. In Section Three we define our process and methodology. In Section Four, we present a paired-down example of the three separate ontologies to the application of our technique. In Section Five, conclusions are provided.