These are exciting times in the fields of Fuzzy Logic and the Semantic Web, and this book will add to the excitement, as it is the first volume to focus on the growing connections between these two fields. This book is expected to be a valuable aid to anyone considering the application of Fuzzy Logic to the Semantic Web, because it contains a number of detailed accounts of these combined fields, written by leading authors in several countries. The Fuzzy Logic field has been maturing for forty years. These years have witnessed a tremendous growth in the number and variety of applications, with a real-world impact across a wide variety of domains with humanlike behavior and reasoning. And we believe that in the coming years, the Semantic Web will be major field of applications of Fuzzy Logic.
This book, the first in the new series Capturing Intelligence, shows the positive role Fuzzy Logic, and more generally Soft Computing, can play in the development of the Semantic Web, filling a gap and facing a new challenge. It covers concepts, tools, techniques and applications exhibiting the usefulness, and the necessity, for using Fuzzy Logic in the Semantic Web. It finally opens the road to new systems with a high Web IQ.
Most of today's Web content is suitable for human consumption. The Semantic Web is presented as an extension of the current web in which information is given well-defined meaning, better enabling computers and people to work in cooperation. For example, within the Semantic Web, computers will understand the meaning of semantic data on a web page by following links to specified ontologies. But while the Semantic Web vision and research attracts attention, as long as it will be used two-valued-based logical methods no progress will be expected in handling ill-structured, uncertain or imprecise information encountered in real world knowledge. Fuzzy Logic and associated concepts and techniques (more generally, Soft Computing), has certainly a positive role to play in the development of the Semantic Web. Fuzzy Logic will not supposed to be the basis for the Semantic Web but its related concepts and techniques will certainly reinforce the systems classically developed within W3C.
In fact, Fuzzy Logic cannot be ignored in order to bridge the gap between human-understandable soft logic and machine-readable hard logic. None of the usual logical requirements can be guaranteed: there is no centrally defined format for data, no guarantee of truth for assertions made, no guarantee of consistency. To support these arguments, this book shows how components of the Semantic Web (like XML, RDF, Description Logics, Conceptual Graphs, Ontologies) can be covered, with in each case a Fuzzy Logic focus.
- First volume to focus on the growing connections between Fuzzy Logic and the Semantic Web
- Keynote chapter by Lotfi Zadeh
- The Semantic Web is presently expected to be a major field of applications of Fuzzy Logic
- It fills a gap and faces a new challenge in the development of the Semantic Web
- It opens the road to new systems with a high Web IQ
- Contributed chapters by Fuzzy Logic leading experts
Preface (Frank Van Harmelen).
Foreword (Elie Sanchez).
From Search Engines to Question Answering Systems-The Problems of World Knowledge, Relevance, Deduction and Precisiation (Lotfi A. Zadeh).
A perception-based search with fuzzy semantic (Chris Tseng and Toan Vu).
On the Expressiveness of the Languages for the Semantic Web
Making a Case for 'A Little More'(Christopher Thomas, Amit Sheth).
Fuzzy Data Mining for the Semantic Web : Building XML Mediator Schemas (A. Laurent, P. Poncelet, M. Teisseire).
Capturing basic semantics exploiting RDF-oriented classification (Vincenzo Loia and Sabrina Senatore).
Approximate Knowledge Graph retrieval: Measures and Realization (T.H. Cao, Dat T. Huynh).
Processing Fuzzy Information in Semantic Web Applications (Sebastian Kloeckner, Klaus Turowski, Uwe Weng).
Using Knowledge Trees for Semantic Web Querying (Ronald R. Yager).
Fuzzy Logic Aggregation for Semantic Web Search for the Best Answer (Peter Vojtas).
Evolving Ontologies for Intelligent Decision Support (Paulo Gottgtroy, Nikola Kasabov, Stephen MacDonell).
Soft Integration of information with Semantic Gaps (Trevor Martin).
Fuzzy ontologies for information retrieval on the WWW (David Parry).
Bottom-up Extraction and Maintenance of Ontology-based Metadata (Paolo Ceravolo, Angelo Corallo, Ernesto Damiani, Gianluca Elia, Marco Viviani, Antonio Zilli).
A fuzzy logic approach to information retrieval using an ontology-based representation of documents (Mustapha Baziz, Mohand Boughanem, Gabriella Pasi, Henri Prade).
Fuzzy Relational Oncological Model in Information Search Systems (Rachel Pereira, Ivan Ricarte, Fernando Gomide).
Towards a Semantic Portal for Oncology using a Description Logic with Fuzzy Concrete Domains (Mathieu d'Aquin, Jean Lieber, Amedeo Napoli).
A Fuzzy Description Logic for the Semantic Web (Umberto Straccia).
Possibilistic uncertainty and fuzzy features in description logic. A preliminary discussion (Didier Dubois, Jérôme Mengin, Henri Prade).
What does mathematical fuzzy logic offer to description logic? (Petr Hajek).
Uncertainty and Description Logic Programs over Lattices (Umberto Straccia).
Fuzzy Quantification in Fuzzy Description Logics (Daniel Sanchez, Andrea G.B. Tettamanzi).
Enhancing the Power of the Internet using Fuzzy Logic-Based Web Intelligence : Beyond the Semantic Web (Masoud Nikravesh).