| OWL: Capturing Semantic Information using a Standardized Web Ontology Language, Multilingual Computing Magazine Vol. 15, issue 7 (2008) | |||||||||||||||
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| The Semantic Web is an extension of the current World Wide Web. The hypertext pages that present information to humans remain, but a new layer of machine understandable data is added to allow computers to participate on the Web in new ways. Using standardized languages such as RDF and OWL, semantic web data can precisely describe the knowledge content underlying HTML pages, specify the implicit information contained in media like images and videos, or be a publicly accessible and usable representation of an otherwise inaccessible database. An integral component of the Semantic Web is the notion of an ontology. Ontologies are also extensively used in natural language processing (NLP) systems. However, the lack of a standardized ontology language has made it difficult to share and reuse ontological information across interrelated systems. The Semantic Web provides such a standard – the Web Ontology Language (OWL)- which can be used to overcome the semantic interoperability problem, in addition to supporting a wide variety of intelligent web-based applications. Ontologies in Natural Language Processing Ontology has been an important concept in Philosophy, and later Library Sciences, for a long time before it became relevant to the Computer Science and in particular, the Artificial Intelligence (AI) community. In AI, an ontology is used to formally specify the concepts and relationships that characterize a certain body of knowledge (domain). The formal nature of ontologies makes them amenable to machine-readability and provides a well-defined semantics for the defined terms. This allows computer programs to manipulate, transform and draw inferences from information represented using the ontology. Ontologies have been widely used in a variety of natural language applications including building a corpus of term definitions as a reference dictionary or thesauri (e.g. text classification systems), providing a systematic framework for complex language processing (e.g. word disambiguation based on context) and directly capturing rich linguistic knowledge (e.g. machine translation). A few examples of the kinds of the kinds of applications that contain ontological components include 1: | |||||||||||||||
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