Ontology Evolution: A Case Study on Semantic Technology in the Media Domain

Introduction

Semantic technology is based mostly on automatic building of ontologies which is often referred to by the term “ontology learning”, originally introduced by Mädche, A. & Staab, S. (2001). It is normally described as the process of (semi-) automatically constructing ontologies on the basis of in-domain texts where a domain can represent a small part of a world-knowledge such as finance, media, or technical or cultural knowledge. The text data for learning is then taken out of this section of world-knowledge. The assumption is that the domain texts reflect the terminology that should go into the ontology, and that appropriate linguistic and statistical methods should be able to extract the appropriate concept candidates and their relationships from these texts. Semantic technology is an application-driven technology. In one of the most recent studies (Mills, D. 2008), more than 100 application categories have been examined, where semantic related technologies can be applied to. Generally speaking, semantics as a leading technology in the evolution of the Internet and the information society, should help to understand and manage the opinions freely expressed by people and make them not only understandable to humans but also to computers. This is the most essential focus in many applications, as machine-to-machine communication will play an increasingly important role in helping people to search, find and evaluate desired information. Most of the applications can be divided into two domains, the enterprise (B2B) domain and the private end consumer (B2C, or C2C) domain. In the enterprise domain, Enterprise Resource Planning (ERP), Enterprise Content Management (ECM), Supply Chain Management (SCM), Finance and Accounting (FA) as well as Project Lifecycle Management (PLM) are among others the applications deployed in mid-sized and large companies. In the consumer domain, topics like customer self-service, customer service automation, media entertainment, directories and information/product portals as well as mobile search profit strongly from semantic search technologies.

Semantically enabled search and management technologies have still been characterized as early market. The majority of current investments is rooted in R&D activities and rarely reaches operational deployment. More and more companies, however, are seriously considering the gradual introduction of the new technologies. Similar to classic motivations for investment, there are three basic elements for measuring the business value of semantic technologies:

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  Cost saving: This is to raise the efficiency. The purpose is to do the same job faster, cheaper and with fewer resources than previously.

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  Return on assets: This is to increase the effectiveness, doing a better job than you did before, and improving the productivity and performance.

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  Return on investment: This is to create new and /or value-added services by changing some existing business aspects and/or adding new strategic advantages.

Table 1 shows that semantic technologies can significantly reduce production and operating cost improve service quality and create new business.