Ontology has been a subject of many studies carried out in artificial intelligence (AI) and information system communities. Ontology has become an important component of the semantic web, covering a variety of knowledge domains. Although building domain ontologies still remains a big challenge with regard to its designing and implementation, there are still many areas that need to create ontologies. Information Science (IS) is one of these areas that need a unified ontology model to facilitate information access among the heterogeneous data resources and share a common understanding of the domain knowledge. The objective of this study is to develop a generic model of ontology that serves as a foundation of knowledge modelling for applications and aggregation with other ontologies to facilitate information exchanging between different systems. This model will be a metadata for a knowledge base system to be used in different purposes of interest, such as education applications to support educational needs for teachers and students and information system developers, and enhancing the index tool in libraries to facilitate access to information collections. The findings of the research revealed that overall feedback from the IS community has been positive and that the model met the ontology quality criteria. It was appropriate to provide consistency and clear understanding of the subject area. OIS ontology unifies information science, which is composed of library science, computer science and archival science, by creating the theoretical base useful for further practical systems. Developing ontology of information science (OIS) is not an easy task, due to the complex nature of the field. It needs to be integrated with other ontologies such as social science, cognitive science, philosophy, law management and mathematics, to provide a basic knowledge for the semantic web and also to leverage information retrieval.
TopIntroduction
Recently, the development of domain ontologies has become increasingly important for knowledge level interoperation and information integration. They provide functional features for AI and knowledge representation. Domain Ontology is a central foundation of growth for the semantic web that provides a general knowledge for correspondence and communication among heterogeneous systems. Particularly with a rise of ontology in the artificial intelligence (AI) domain, it can be seen as an almost inevitable development in computer science and AI in general.
Ontologies are useful for different applications to be able to share information between heterogeneous data resources. They are also essential for enabling knowledge-level interoperation of agents, when these agents are interacting to share a common interpretation of the vocabulary. Moreover, it is useful for human understanding and interaction to reach a consensus amongst a professional community.
Although there are a range of domain ontologies on the semantic web such as Gene Ontology (GeneOntology, 2009), Biological science ontology (Sabou 2005), CIDOC-CRM ontology of culture heritage documentation, FRBR in Bibliographic and NCI cancer ontology (Golbeck et al., 2008), there still exists a lack of domain ontologies, which has led to the loss of knowledge in specific domains. This is a significant problem for scholars and researchers who need to be able to access information within their interest area.
Ontology provides a vocabulary for metadata description with machine understandable terminology. Ontology provides a format for explaining and understanding terminology and the knowledge contained in a software system. By using shared concepts and terms in accordance with a specific approach, a lot of information remains in people’s heads. It is discussed in chapter 2.
However, information science (IS) is a fast paced discipline and communication technology is rapidly increasing, so it is imperative to take advantage of this development. IS is a multidisciplinary field and it has gained the fundamental root of its theory from different related fields. The analysis includes the three branches of the field, which are; Library Science, Archival Science and Computer Science. Meanwhile it overlaps with other sciences, as stated in chapter 2, e.g., communication, cognitive science, philosophical science, management, social science and marketing. More precisely, the relationships between information and marketing can be subdivided into marketing information, marketing information services, marketing of library services. These kinds of relationships need logical ontology to clarify their relations and the science boundaries, amongst others. Therefore, Information Science still needs identity.
However, there is a lack of IS ontology representing the unified model that combines all concepts and their relationships. Moreover, IS as any domains which use the natural language. It contains a lot of jargon which needs to be in a formal language for programming or logic. Alternatively, integration of the computer with the internet has led to the emergence of new concepts in the field of IS such as, Electronic Library, Virtual Library, Library Without Walls, Digital Library and Information Management, as well as Nerve Centres. Even the information concept itself has strong and complex relations with other concepts, for example some people have defined it as fact, energy, data, and symbols. Also, it can be composed with other words such as; information age, information revaluation, information crisis, information explosion. However, there are 400 definitions for information in the literature (Yuexiao, 1988). It is hard to differentiate between these concepts. Even within the same field, there is still confusion over defining information - everyone defines it based on his background, for example librarians know it in term of facts, and data can be in containers such as journals, books and documents. The computer scientists conceive it as small units such as bits and bytes.
Consequently, modelling the IS domain necessarily assumes the need to represent the correct picture of the whole domain, and any changes in the domain will have to be added to keep the model up to date (Mommers, 2010, Yuexiao, 1988).