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 logicbad, 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 handcrafted 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.
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The SW Underpinned by Natural Language
Abraxas AI/NLP algorithms annotation applied approach argue basic claim classic computational concepts conﬁdence level Connectionism core corpora databases deﬁned deﬁnition derived diﬀerent diﬃculty discussed documents domain eﬀective eﬀort English entities evaluation example explicit extraction patterns fact ﬁeld ﬁlled ﬁnd ﬁrst formal GOFAI hierarchical human identiﬁed inﬂuence input interlingua interpretation ISA links issue iterations Jelinek Jones’s knowledge representation knowledge triples language processing lexical lexicons linguistic logical machine learning meaning methodology methods natural language Natural Language Processing NLP techniques notion objects oﬀer Ontoclean ontological relation ontological relationship ontology learning paper part-of-speech tagging predicate problem queries reﬂect relevant resource retrieval scientiﬁc seed corpus semantic Semantic Web signiﬁcant skipgrams Sp¨arck Jones speciﬁc statistical structures suﬃcient symbolic SYSTRAN task templates thesaurus tion traditional TREC trigrams Web Science Wilks word sense Wordnet words