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.

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.

In the last decade, ontologies have received much attention within computer science and related disciplines, most often as the semantic web. Ontology Learning and Population from Text: Algorithms, Evaluation and Applications discusses ontologies for the semantic web, as well as knowledge management, information retrieval, text clustering and classification, as well as natural language processing. Ontology Learning and Population from Text: Algorithms, Evaluation and Applications is structured for research scientists and practitioners in industry. This book is also suitable for graduate-level students in computer science.

Perspectives on Ontology Learning brings together researchers and practitioners from different communities − natural language processing, machine learning, and the semantic web − in order to give an interdisciplinary overview of recent advances in ontology learning. Starting with a comprehensive introduction to the theoretical foundations of ontology learning methods, the edited volume presents the state-of-the-start in automated knowledge acquisition and maintenance. It outlines future challenges in this area with a special focus on technologies suitable for pushing the boundaries beyond the creation of simple taxonomical structures, as well as on problems specifically related to knowledge modeling and representation using the Web Ontology Language. Perspectives on Ontology Learning is designed for researchers in the field of semantic technologies and developers of knowledge-based applications. It covers various aspects of ontology learning including ontology quality, user interaction, scalability, knowledge acquisition from heterogeneous sources, as well as the integration with ontology engineering methodologies.

With the current changes driven by the expansion of the World Wide Web, this book uses a different approach from other books on the market: it applies ontologies to electronically available information to improve the quality of knowledge management in large and distributed organizations. Ontologies are formal theories supporting knowledge sharing and reuse. They can be used to explicitly represent semantics of semi-structured information. These enable sophisticated automatic support for acquiring, maintaining and accessing information. Methodology and tools are developed for intelligent access to large volumes of semi-structured and textual information sources in intra- and extra-, and internet-based environments to employ the full power of ontologies in supporting knowledge management from the information client perspective and the information provider. The aim of the book is to support efficient and effective knowledge management and focuses on weakly-structured online information sources. It is aimed primarily at researchers in the area of knowledge management and information retrieval and will also be a useful reference for students in computer science at the postgraduate level and for business managers who are aiming to increase the corporations' information infrastructure. The Semantic Web is a very important initiative affecting the future of the WWW that is currently generating huge interest. The book covers several highly significant contributions to the semantic web research effort, including a new language for defining ontologies, several novel software tools and a coherent methodology for the application of the tools for business advantage. It also provides 3 case studies which give examples of the real benefits to be derived from the adoption of semantic-web based ontologies in "real world" situations. As such, the book is an excellent mixture of theory, tools and applications in an important area of WWW research. * Provides guidelines for introducing knowledge management concepts and tools into enterprises, to help knowledge providers present their knowledge efficiently and effectively. * Introduces an intelligent search tool that supports users in accessing information and a tool environment for maintenance, conversion and acquisition of information sources. * Discusses three large case studies which will help to develop the technology according to the actual needs of large and or virtual organisations and will provide a testbed for evaluating tools and methods. The book is aimed at people with at least a good understanding of existing WWW technology and some level of technical understanding of the underpinning technologies (XML/RDF). It will be of interest to graduate students, academic and industrial researchers in the field, and the many industrial personnel who are tracking WWW technology developments in order to understand the business implications. It could also be used to support undergraduate courses in the area but is not itself an introductory text.

A new edition of the widely used guide to the key ideas, languages, and technologies of the Semantic Web The development of the Semantic Web, with machine-readable content, has the potential to revolutionize the World Wide Web and its uses. A Semantic Web Primer provides an introduction and guide to this continuously evolving field, describing its key ideas, languages, and technologies. Suitable for use as a textbook or for independent study by professionals, it concentrates on undergraduate-level fundamental concepts and techniques that will enable readers to proceed with building applications on their own and includes exercises, project descriptions, and annotated references to relevant online materials. The third edition of this widely used text has been thoroughly updated, with significant new material that reflects a rapidly developing field. Treatment of the different languages (OWL2, rules) expands the coverage of RDF and OWL, defining the data model independently of XML and including coverage of N3/Turtle and RDFa. A chapter is devoted to OWL2, the new W3C standard. This edition also features additional coverage of the query language SPARQL, the rule language RIF and the possibility of interaction between rules and ontology languages and applications. The chapter on Semantic Web applications reflects the rapid developments of the past few years. A new chapter offers ideas for term projects. Additional material, including updates on the technological trends and research directions, can be found at http://www.semanticwebprimer.org.

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