Natural Language Processing as a Foundation of the Semantic Web
By Yorick Wilks, University of Oxford, UK | Christopher Brewster, Aston University, UK, c.a.brewster@aston.ac.uk
Abstract
The main argument of this paper is that Natural Language Processing (NLP) does, and will continue to, underlie the Semantic Web (SW), including its initial construction from unstructured sources like the World Wide Web (WWW), whether its advocates realise this or not. Chiefly, we argue, such NLP activity is the only way up to a defensible notion of meaning at conceptual levels (in the original SW diagram) based on lower level empirical computations over usage. Our aim is definitely not to claim logic-bad, NLP-good in any simple-minded way, but to argue that the SW will be a fascinating interaction of these two methodologies, again like the WWW (which has been basically a field for statistical NLP research) but with deeper content. Only NLP technologies (and chiefly information extraction) will be able to provide the requisite RDF knowledge stores for the SW from existing unstructured text databases in the WWW, and in the vast quantities needed. There is no alternative at this point, since a wholly or mostly hand-crafted SW is also unthinkable, as is a SW built from scratch and without reference to the WWW. We also assume that, whatever the limitations on current SW representational power we have drawn attention to here, the SW will continue to grow in a distributed manner so as to serve the needs of scientists, even if it is not perfect. The WWW has already shown how an imperfect artefact can become indispensable.
Natural Language Processing as a Foundation of the Semantic Web
Natural Language Processing as a Foundation of the Semantic Web argues that Natural Language Processing (NLP) does, and will continue to, underlie the Semantic Web (SW), including its initial construction from unstructured sources like the World Wide Web, in several different ways, and whether its advocates realise this or not. Chiefly, it argues, such NLP activity is the only way up to a defensible notion of meaning at conceptual levels based on lower level empirical computations over usage. The claim being made is definitely not logic-bad, NLP-good in any simple-minded way, but that the SW will be a fascinating interaction of these two methodologies, like the WWW (which, as the authors explain, has been a fruitful field for statistical NLP research) but with deeper content. Only NLP technologies (and chiefly information extraction) will be able to provide the requisite resource description framework (RDF) knowledge stores for the SW from existing WWW (unstructured) text databases, and in the vast quantities needed. There is no alternative at this point, since a wholly or mostly hand-crafted SW is also unthinkable, as is a SW built from scratch and without reference to the WWW. It is also assumed here that, whatever the limitations on current SW representational power drawn attention to here, the SW will continue to grow in a distributed manner so as to serve the needs of scientists, even if it is not perfect. The WWW has already shown how an imperfect artefact can become indispensable. Natural Language Processing as a Foundation of the Semantic Web will appeal to researchers, practitioners and anyone with an interest in NLP, the philosophy of language, cognitive science, the Semantic Web and Web Science generally, as well as providing a magisterial and controversial overview of the history of artificial intelligence
