As the (in)famous definition states: "An ontology is an explicit specification of a conceptualization". However, an ontology is also a philosophical theory of existence, a knowledge management resource, a database schema, or a type of knowledge representation artefact on the semantic web. Over the years the term 'ontology' has been used in so many different ways that one can no longer be sure what is meant by it at any given occasion. This book clarifies the role ontologies play in knowledge representation; it discusses the distinctions with their use in philosophy, gives insight in the features, rationale and limitations of the OWL 2 web ontology language, and provides a critical review of methodologies and design principles advocated to improve the quality of ontologies. It covers both theory and practice of knowledge acquisition, representation and ontologies; it emphasises human understanding as knowledge structuring principle, and demonstrates this approach in the development of a core ontology of basic legal concepts (LKIF Core) and in the exploration of expressive ontology design patterns for the representation of social reality, change and causation, actions and transactions. In doing so it contributes to a better understanding of the representation of ontologies; or rather, what it means to do ontology representation.

Inhaltsangabe:Introduction: The management of large amounts of information and knowledge is of ever increasing importance in today s large organisations. With the ongoing ease of supplying information online, especially in corporate intranets and knowledge bases, finding the right information becomes an increasingly difficult task. Today s search tools perform rather poorly in the sense that information access is mostly based on keyword searching or even mere browsing of topic areas. This unfocused approach often leads to undesired results. The following example illustrates the problem more clearly: An agriculture scientist would like to find out which organisation established the Agreement on Agriculture. A simple search for establish Agreement on Agriculture might result in a huge list of documents containing these words, but actually none of them containing the desired result: WTO or World Trade Organisation. The problem becomes even worse if the result searched for only appears in a foreign language document. Semantically annotated documents, i.e. documents that are indexed with ontological terms and concepts instead of simple keywords, provide several advantages. First, the ontological abstraction provides robustness against changes in the document. In the above example, the document representation might change using the term Agricultural Agreement instead of Agreement on Agriculture . However, since the document has been annotated with the ontological semantics, this will not affect the search results. Second, since the ontology used for annotating the document in this example is domain-specific, the semantic meanings and interpretations of keywords are bound to that domain and therefore the retrieval is likely to be more efficient. A term can have several meanings in different domains. By first mapping the keyword to its semantic representation in a specific ontology and using the ontology s linked knowledge structure, a much more focused search approach can be taken. Third, document specific representations no longer affect the search. This is extremely important in the case of multilingual representations. Keywords of several languages are mapped to the same concept in an ontology and are therefore given the same meaning. Multilingual search portals can be established to produce the same results, no matter which language is used for retrieval. An important task in knowledge management facilitating above described search scenario id [...]

In order for information systems supporting two different organizations to interoperate, there must be an agreement as to what the words mean. There are many such agreements in place, supporting information systems interoperation in many different application areas. Most of these agreements have been created as part of diverse systems development processes, but since the advent of the Semantic Web in the late 1990s, they have been studied as a kind of software artifact in their own right, called an ontology, or description of a shared world. This book brings together developments from philosophy, artificial intelligence and information systems to formulate a collection of functional requirements for ontology development. Once the functional requirements are established, the book looks at several ontology representation languages: RDFS, OWL, Common Logic and Topic Maps, to show how these languages support the functional requirements, what deficiencies there are, and how the languages relate to each other. Besides a collection of running examples used throughout the book, the entire treatment is supported by an extended example of a hypothetical ontology for the Olympic Games presented first as a set of chapter-end exercises and then as a set of solutions which illustrate the various points made in the text in the context of a single coherent development.

Formal Ontology in Information Systems (FOIS) is the flagship conference of the International Association for Ontology and its Applications, a non-profit organization promoting interdisciplinary research and international collaboration at the intersection of philosophical ontology, linguistics, logic, cognitive science, and computer science. This book presents the 11 papers accepted for the 12th edition of FOIS. The conference was held from 13-17 September 2021 in Bozen-Bolzano, Italy, as a hybrid event with some participants attending on-site in Bolzano and others attending virtually online. The papers are divided into 3 sections and cover a wide range of topics: (1) Foundations, addressing fundamental issues; (2) Applications and Methods, presenting novel uses, systems, tools, and approaches; and (3) Domain Ontology, describing well-formed ontologies in particular subject areas.

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.

Defining a formal domain ontology is considered a useful, not to say necessary step in almost every software project. This is because software deals with ideas rather than with self-evident physical artefacts. However, this development step is hardly ever done, as ontologies rely on well-defined and semantically powerful AI concepts such as description logics or rule-based systems, and most software engineers are unfamiliar with these. This book fills this gap by covering the subject of MDA application for ontology development on the Semantic Web. The writing is technical yet clear, and is illustrated with examples. The book is supported by a website.

"The focus of this book is on information and communication sciences, computer science, and artificial intelligence and provides readers with access to the latest knowledge related to design, modeling and implementation of ontologies"--Provided by publisher.

"This book provides an opportunity for readers to clearly understand the notion of ontology engineering and the practical aspects of this approach in the domains of two interest areas: Knowledge Management Systems and Enterprise Systems"--

Ontology Learning for the Semantic Web explores techniques for applying knowledge discovery techniques to different web data sources (such as HTML documents, dictionaries, etc.), in order to support the task of engineering and maintaining ontologies. The approach of ontology learning proposed in Ontology Learning for the Semantic Web includes a number of complementary disciplines that feed in different types of unstructured and semi-structured data. This data is necessary in order to support a semi-automatic ontology engineering process. Ontology Learning for the Semantic Web is designed for researchers and developers of semantic web applications. It also serves as an excellent supplemental reference to advanced level courses in ontologies and the semantic web.

This book constitutes the post-proceedings of the International Workshop on Ontology, Conceptualization and Epistemology for Information Systems, Software Engineering and Service Sciences (ONTOSE 2010) , held at the CAiSE 2010 conference in Hammamet, Tunisia, June, 2010. The 10 papers presented in this volume were carefully revised and selected from 25 submissions. They are grouped in sections on enterprise and service architectures, ontology applications, ontology visualization and query expansion, and ontologies for services.