Statistical Semantics for the Semantic Web provides a unique
introduction to identity and reference theories of the World Wide Web,
through the academic lens of philosophy of language and data-driven
statistical models. The Semantic Web is a natural evolution of the Web,
and this book covers the URL-based Web architecture and Semantic Web in
detail.
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.
