Ontological Semantics

A comprehensive theory-based approach to the treatment of text meaning in natural language processing applications.

Lexical Ontological Semantics introduces ontological methods into lexical semantic studies with the aim of giving impetus to various fields of endeavours which envision and model the semantic network of a language. Lexical ontological semantics (LOS) provides a cognition-based computation-oriented framework in which nouns and predicates are described in terms of their semantic knowledge and models the mechanism in which the noun system is coupled with the predicate system. It expands the scope of lexical semantics, updates methodologies to semantic representation, guides the construction of semantic resources for natural language processing, and develops new theories for human-machine interactions and communications.

Semantic Web technologies enable people to create data stores on the Web, build vocabularies, and write rules for handling data. They have been in use for several years now, and knowledge extraction and knowledge discovery are two key aspects investigated in a number of research fields which can potentially benefit from the application of semantic web technologies, and specifically from the development and reuse of ontologies. This book, Applications and Practices in Ontology Design, Extraction, and Reasoning, has as its main goal the provision of an overview of application fields for semantic web technologies. In particular, it investigates how state-of-the-art formal languages, models, methods, and applications of semantic web technologies reframe research questions and approaches in a number of research fields. The book also aims to showcase practical tools and background knowledge for the building and querying of ontologies. The first part of the book presents the state-of-the-art of ontology design, applications and practices in a number of communities, and in doing so it provides an overview of the latest approaches and techniques for building and reusing ontologies according to domain-dependent and independent requirements. Once the data is represented according to ontologies, it is important to be able to query and reason about them, also in the presence of uncertainty, vagueness and probabilities. The second part of the book covers some of the latest advances in the fields of ontology, semantics and reasoning, without losing sight of the book’s practical goals.

Researchers in areas such as artificial intelligence, formal and computational linguistics, biomedical informatics, conceptual modeling, knowledge engineering and information retrieval have come to realise that a solid foundation for their research calls for serious work in ontology, understood as a general theory of the types of entities and relations that make up their respective domains of inquiry. In all these areas, attention is now being focused on the content of information rather than on just the formats and languages used to represent information. The clearest example of this development is provided by the many initiatives growing up around the project of the Semantic Web. And, as the need for integrating research in these different fields arises, so does the realisation that strong principles for building well-founded ontologies might provide significant advantages over ad hoc, case-based solutions. The tools of formal ontology address precisely these needs, but a real effort is required in order to apply such philosophical tools to the domain of information systems. Reciprocally, research in the information sciences raises specific ontological questions which call for further philosophical investigations. The purpose of FOIS is to provide a forum for genuine interdisciplinary exchange in the spirit of a unified effort towards solving the problems of ontology, with an eye to both theoretical issues and concrete applications. This book contains a wide range of areas, all of which are important to the development of formal ontologies.

A picture of the world as chiefly one of discrete objects, distributed in space and time, has sometimes seemed compelling. It is however one of two main targets of Henry Laycock's book; for it is seriously incomplete. The picture, he argues, leaves no space for stuff like air and water. With discrete objects, we may always ask 'how many?', but with stuff the question has to be 'how much?' Within philosophy, stuff of certain basic kinds is central to the ancient pre-Socraticworld-view; but it also constitutes the field of modern chemistry and is a major factor in ecology.Philosophers these days, in general, are unlikely to deny that stuff exists. But they are very likely to deny that it is ('ultimately') to be contrasted with things, and it is on this account that logic and semantics figure largely in the framework of the book. Elementary logic is a logic which takes values for its variables; and these values are precisely distinct individuals or things. Existence is then symbolized in just such terms; and this, it is proposed, creates a pressure for 'reducing'stuff to things. Non-singular expressions, which include words for stuff, 'mass' nouns, and also plural nouns, are 'explicated' as semantically singular.Here then is the second target of the book. The posit that both mass and plural nouns name special categories of objects (set-theoretical 'collections' of objects in the one case, mereological 'parcels' or 'portions' of stuff in the other) represents, so Laycock urges, the imposition of an alien logic upon both the many and the much.

Most widely available approaches to semantic integration provide ad-hoc, non-systematic, subjective manual mappings that lead to procrustean amalgamations to fit the target standard, an outcome that pleases no one. Written by experts in the field, Theories of Geographic Concepts: Ontological Approaches to Semantic Integration emphasizes the

The promise of the Semantic Web is that future web pages will be annotated not only with bright colors and fancy fonts as they are now, but with annotation extracted from large domain ontologies that specify, to a computer in a way that it can exploit, what information is contained on the given web page. The presence of this information will allow software agents to examine pages and to make decisions about content as humans are able to do now. The classic method of building an ontology is to gather a committee of experts in the domain to be modeled by the ontology, and to have this committee agree on which concepts cover the domain, on which terms describe which concepts, on what relations exist between each concept and what the possible attributes of each concept are. All ontology learning systems begin with an ontology structure, which may just be an empty logical structure, and a collection of texts in the domain to be modeled. An ontology learning system can be seen as an interplay between three things: an existing ontology, a collection of texts, and lexical syntactic patterns. The Semantic Web will only be a reality if we can create structured, unambiguous ontologies that model domain knowledge that computers can handle. The creation of vast arrays of such ontologies, to be used to mark-up web pages for the Semantic Web, can only be accomplished by computer tools that can extract and build large parts of these ontologies automatically. This book provides the state-of-art of many automatic extraction and modeling techniques for ontology building. The maturation of these techniques will lead to the creation of the Semantic Web.

Ontology has been a subject of many studies carried out in artificial intelligence (AI) and information system communities. Ontology has become an important component of the semantic web, covering a variety of knowledge domains. Although building domain ontologies still remains a big challenge with regard to its designing and implementation, there are still many areas that need to create ontologies. Information Science (IS) is one of these areas that need a unified ontology model to facilitate information access among the heterogeneous data resources and share a common understanding of the domain knowledge. Recently, the development of domain ontologies has become increasingly important for knowledge level interoperation and information integration. They provide functional features for AI and knowledge representation. Domain Ontology is a central foundation of growth for the semantic web that provides a general knowledge for correspondence and communication among heterogeneous systems. Particularly with a rise of ontology in the artificial intelligence (AI) domain, it can be seen as an almost inevitable development in computer science and AI in general.