Artificial Intelligence federates numerous scientific fields in the aim of developing machines able to assist human operators performing complex treatments---most of which demand high cognitive skills (e.g. learning or decision processes). Central to this quest is to give machines the ability to estimate the likeness or similarity between things in the way human beings estimate the similarity between stimuli. In this context, this book focuses on semantic measures: approaches designed for comparing semantic entities such as units of language, e.g. words, sentences, or concepts and instances defined into knowledge bases. The aim of these measures is to assess the similarity or relatedness of such semantic entities by taking into account their semantics, i.e. their meaning---intuitively, the words tea and coffee, which both refer to stimulating beverage, will be estimated to be more semantically similar than the words toffee (confection) and coffee, despite that the last pair has a higher syntactic similarity. The two state-of-the-art approaches for estimating and quantifying semantic similarities/relatedness of semantic entities are presented in detail: the first one relies on corpora analysis and is based on Natural Language Processing techniques and semantic models while the second is based on more or less formal, computer-readable and workable forms of knowledge such as semantic networks, thesauri or ontologies. Semantic measures are widely used today to compare units of language, concepts, instances or even resources indexed by them (e.g., documents, genes). They are central elements of a large variety of Natural Language Processing applications and knowledge-based treatments, and have therefore naturally been subject to intensive and interdisciplinary research efforts during last decades. Beyond a simple inventory and categorization of existing measures, the aim of this monograph is to convey novices as well as researchers of these domains toward a better understanding of semantic similarity estimation and more generally semantic measures. To this end, we propose an in-depth characterization of existing proposals by discussing their features, the assumptions on which they are based and empirical results regarding their performance in particular applications. By answering these questions and by providing a detailed discussion on the foundations of semantic measures, our aim is to give the reader key knowledge required to: (i) select the more relevant methods according to a particular usage context, (ii) understand the challenges offered to this field of study, (iii) distinguish room of improvements for state-of-the-art approaches and (iv) stimulate creativity toward the development of new approaches. In this aim, several definitions, theoretical and practical details, as well as concrete applications are presented.

For humans, understanding a natural language sentence or discourse is so effortless that we hardly ever think about it. For machines, however, the task of interpreting natural language, especially grasping meaning beyond the literal content, has proven extremely difficult and requires a large amount of background knowledge. This book focuses on the interpretation of natural language with respect to specific domain knowledge captured in ontologies. The main contribution is an approach that puts ontologies at the center of the interpretation process. This means that ontologies not only provide a formalization of domain knowledge necessary for interpretation but also support and guide the construction of meaning representations. We start with an introduction to ontologies and demonstrate how linguistic information can be attached to them by means of the ontology lexicon model lemon. These lexica then serve as basis for the automatic generation of grammars, which we use to compositionally construct meaning representations that conform with the vocabulary of an underlying ontology. As a result, the level of representational granularity is not driven by language but by the semantic distinctions made in the underlying ontology and thus by distinctions that are relevant in the context of a particular domain. We highlight some of the challenges involved in the construction of ontology-based meaning representations, and show how ontologies can be exploited for ambiguity resolution and the interpretation of temporal expressions. Finally, we present a question answering system that combines all tools and techniques introduced throughout the book in a real-world application, and sketch how the presented approach can scale to larger, multi-domain scenarios in the context of the Semantic Web. Table of Contents: List of Figures / Preface / Acknowledgments / Introduction / Ontologies / Linguistic Formalisms / Ontology Lexica / Grammar Generation / Putting Everything Together / Ontological Reasoning for Ambiguity Resolution / Temporal Interpretation / Ontology-Based Interpretation for Question Answering / Conclusion / Bibliography / Authors' Biographies

Now in paperback for the first time since its original publication, the material gathered here is perfect for anyone who needs a detailed and accessible introduction to the important semantic theories. Designed for a wide audience, it will be of great value to linguists, cognitive scientists, philosophers, and computer scientists working on natural language. The book covers theories of lexical semantics, cognitively oriented approaches to semantics, compositional theories of sentence semantics, and discourse semantics. This clear, elegant explanation of the key theories in semantics research is essential reading for anyone working in the area.

How should we think about the meaning of the words that make up our language? How does reference of these terms work, and what is their referent when these are connected to abstract objects rather than to concrete ones? Can logic help to address these questions? This collection of papers aims to unify the questions of syntax and semantics of language, which span across the fields of logic, philosophy and ontology of language. The leading motif of the presented selection is the differentiation between linguistic tokens (material, concrete objects) on the one hand and linguistic types (ideal, abstract objects) on the other. Through a promenade among articles that span over all of the Author’s career, this book addresses the complex philosophical question of the ontology of language by following the crystalline conceptual tools offered by logic. At the core of Wybraniec-Skardowska’s scholarship is the idea that language is an ontological being, characterized in compliance with the logical conception of language proposed by Ajdukiewicz. The application throughout the book of tools of classical logic and set theory results fosters the emergence of a general formal logical theory of syntax, semantics and of the pragmatics of language, which takes into account the duality token-type in the understanding of linguistic expressions. Via a functional approach to language itself, logic appears as ontologically neutral with respect to existential assumptions relating to the nature of linguistic expressions and their extra-linguistic counterparts. The book is addressed to readers both at the graduate and undergraduate level, but also to a more general audience interested in getting a firmer grip on the interplay between reality and the language we use to describe and understand it.

Ontology was once understood to be the philosophical inquiry into the structure of reality: the analysis and categorization of ‘what there is’. Recently, however, a field called ‘ontology’ has become part of the rapidly growing research industry in information technology. The two fields have more in common than just their name. Theory and Applications of Ontology is a two-volume anthology that aims to further an informed discussion about the relationship between ontology in philosophy and ontology in information technology. It fills an important lacuna in cutting-edge research on ontology in both fields, supplying stage-setting overview articles on history and method, presenting directions of current research in either field, and highlighting areas of productive interdisciplinary contact. Theory and Applications of Ontology: Computer Applications presents ontology in ways that philosophers are not likely to find elsewhere. The volume offers an overview of current research in ontology, distinguishing basic conceptual issues, domain applications, general frameworks, and mathematical formalisms. It introduces the reader to current research on frameworks and applications in information technology in ways that are sure to invite reflection and constructive responses from ontologists in philosophy.

Philosophical questions regarding the nature and methodology of philosophical inquiry have garnered much attention in recent years. Perhaps nowhere are these discussions more developed than in relation to the field of metaphysics. The Routledge Handbook of Metametaphysics is an outstanding reference source to this growing subject. It comprises thirty-eight chapters written by leading international contributors, and is arranged around five themes: • The history of metametaphysics • Neo-Quineanism (and its objectors) • Alternative conceptions of metaphysics • The epistemology of metaphysics • Science and metaphysics. Essential reading for students and researchers in metaphysics, philosophical methodology, and ontology, The Routledge Handbook of Metametaphysics will also be of interest to those in closely related subjects such as philosophy of language, logic, and philosophy of science.

Connects the semantics of databases to that of natural language, and links them through a common view of the semantics of time.

This book constitutes the refereed proceedings of the 11th International Conference on Applications of Natural Language to Information Systems, NLDB 2006, held in Klagenfurt, Austria in May/June 2006 as part of UNISCON 2006. The book presents 17 revised full papers and 5 revised short papers, organized in topical sections on concepts extraction and ontology, ontologies and task repository utilization, query processing, information retrieval and dialog processing, and NLP techniques.