Commonsense Knowledge, Ontology and Ordinary Language

Int. J. Reasoning-based Intelligent Systems, Vol. n, No. m, 2008 43 Copyright © 2008 Inderscience Enterprises Ltd. Commonsense Knowledge, Ontology and Ordinary Language Walid S. Saba American Institutes for Research, 1000 Thomas Jefferson Street, NW, Washington, DC 20007 USA E-mail: wsaba@air.org Abstract: Over two decades ago a “quite revolution” overwhelmingly replaced knowledge- based approaches in natural language processing (NLP) by quantitative (e.g., statistical, corpus-based, machine learning) methods. Although it is our firm belief that purely quanti- tative approaches cannot be the only paradigm for NLP, dissatisfaction with purely engi- neering approaches to the construction of large knowledge bases for NLP are somewhat justified. In this paper we hope to demonstrate that both trends are partly misguided and that the time has come to enrich logical semantics with an ontological structure that reflects our commonsense view of the world and the way we talk about in ordinary language. In this paper it will be demonstrated that assuming such an ontological structure a number of challenges in the semantics of natural language (e.g., metonymy, intensionality, copredica- tion, nominal compounds, etc.) can be properly and uniformly addressed. Keywords: Ontology, compositional semantics, commonsense knowledge, reasoning. Reference to this paper should be made as follows: Saba, W. S. (2008) ‘Commonsense Knowledge, Ontology and Ordinary Language’, Int. Journal of Reasoning-based Intelligent Systems, Vol. n, No. n, pp.43–60. Biographical notes: W. Saba received his PhD in Computer Science from Carleton Uni- versity in 1999. He is currently a Principal Software Engineer at the American Institutes for Research in Washington, DC. Prior to this he was in academia where he taught computer science at the University of Windsor and the American University of Beirut (AUB). For over 9 years he was also a consulting software engineer where worked at such places as AT&T Bell Labs, MetLife and Cognos, Inc. His research interests are in natural language processing, ontology, the representation of and reasoning with commonsense knowledge, and intelligent e-commerce agents.

1 INTRODUCTION Over two decades ago a “quite revolution”, as Charniak (1995) once called it, overwhelmingly replaced knowledge- based approaches in natural language processing (NLP) by quantitative (e.g., statistical, corpus-based, machine learn- ing) methods. In recent years, however, the terms ontology, semantic web and semantic computing have been in vogue, and regardless of how these terms are being used (or mis- used) we believe that this ‘semantic counter revolution’ is a positive trend since corpus-based approaches to NLP, while useful in some language processing tasks – see (Ng and Zelle, 1997) for a good review – cannot account for compo- sitionality and productivity in natural language, not to men- tion the complex inferential patterns that occur in ordinary language use. The inferences we have in mind here can be illustrated by the following example:

(1) Pass that car will you.

a. He is really annoying me.

b. They are really annoying me. Clearly, speakers of ordinary language can easily infer that ‘he’ in (1a) refers to the person driving [that] car, while ‘they’ in (1b) is a reference to the people riding [that] car. Such inferences, we believe, cannot theoretically be learned (how many such examples will be needed?), and are thus beyond the capabilities of any quantitative approach. On the other hand, and although it is our firm belief that purely quantitative approaches cannot be the only paradigm for NLP, dissatisfaction with purely engineering approaches to the construction of large knowledge bases for NLP (e.g., Lenat and Ghua, 1990) are somewhat justified. While lan- guage ‘understanding’ is for the most part a commonsense ‘reasoning’ process at the pragmatic level, as example (1) illustrates, the knowledge structures that an NLP system must utilize should have sound linguistic and ontological underpinnings and must be formalized if we ever hope to build scalable systems (or as John McCarthy once said, if we ever hope to build systems that we can actually under- stand!). Thus, and as we have argued elsewhere (Saba, 2007), we believe that both trends are partly misguided and that the time has come to enrich logical semantics with an 44 W. S. SABA ontological structure that reflects our commonsense view of the world and the way we talk about in ordinary language. Specifically, we argue that very little progress within logical semantics have been made in the past several years due to the fact that these systems are, for the most part, mere sym- bol manipulation systems that are devoid of any content. In particular, in such systems where there is hardly any link between semantics and our commonsense view of the world, it is quite difficult to envision how one

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