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Statistical Semantics for the Semantic Web discusses how the largest problem facing the Semantic Web is the problem of identity and reference, and how these are the results of a larger general theory of meaning. This book hypothesizes that statistical semantics can solve these problems, illustrated by case studies ranging from a pioneering study of tagging systems to using the Semantic Web to boost the results of commercial search engines. The substance matter of this book is rooted in the most difficult theoretical issues facing the Semantic Web today while having a robustly empirical side with an impact on industry.
About this book:
- Presents case studies and experiments including the pioneering study of tagging systems.
- Covers performance of commercial search engines.
- Illustrates that the substance matter of this book has a robustly empirical side with impact on industry.
This book targets practitioners working in the related fields of the semantic web, search engines, information retrieval, philosophers of language and more. Advanced-level students and researchers focusing on computer science will also find this book valuable as a secondary text or reference book.
