Social Semantics the Search for Meaning on the Web Reihe: Semantic Web and Beyond

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springer.com Harry Halpin Social Semantics The Search for Meaning on the Web Reihe: Semantic Web and Beyond 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 an impact on industry Social Semantics: The Search for Meaning on the 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. It has a robust empirical side which has an impact on industry. Social Semantics: The Search for Meaning on the Web discusses how the largest problem facing the Semantic Web is 2013, XVI, 220 p. 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, Gedrucktes Buch 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. Social Semantics: The Search Hardcover for Meaning on the Web targets practitioners working in the related fields of the semantic web, 84,95 € | £74.99 | $109.99 search engines, information retrieval, philosophers of language and more. Advanced-level [1]90,90 € (D) | 93,45 € (A) | CHF 100,50 students and researchers focusing on computer science will also find this book valuable as a secondary text or reference book. Softcover 84,95 € | £76.50 | $109.00 [1]90,90 € (D) | 93,45 € (A) | CHF 113,61 eBook 71,68 € | £60.99 | $84.99 [2]71,68 € (D) | 71,68 € (A) | CHF 90,50 Erhältlich bei Ihrer Bibliothek oder springer.com/shop MyCopy [3] Printed eBook for just € | $ 24.99 springer.com/mycopy Erhältlich bei Ihrem Buchhändler oder – Springer Nature Customer Service Center GmbH, Haberstrasse 7, 69126 Heidelberg, Germany / Call: + 49 (0) 6221-345-4301 / Fax: +49 (0)6221-345-4229 / Email: [email protected] / Web: springer.com [1] € (D) sind gebundene Ladenpreise in Deutschland und enthalten 7% MwSt; € (A) sind gebundene Ladenpreise in Österreich und enthalten 10% MwSt. CHF und die mit [2] gekennzeichneten Preise für elektronische Produkte sind unverbindliche Preisempfehlungen und enthalten die landesübliche MwSt. Programm- und Preisänderungen (auch bei Irrtümern) vorbehalten. Es gelten unsere Allgemeinen Liefer- und Zahlungsbedingungen. Springer-Verlag GmbH, Handelsregistersitz: Berlin-Charlottenburg, HR B 91022. Geschäftsführung: Haank, Mos, Hendriks Part of .

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Short-Text Clustering Using Statistical Semantics
Short-Text Clustering using Statistical Semantics Sepideh Seifzadeh Ahmed K. Farahat Mohamed S. Kamel University of Waterloo University of Waterloo University of Waterloo Waterloo, Ontario, Canada. Waterloo, Ontario, Canada. Waterloo, Ontario, Canada. N2L 3G1 N2L 3G1 N2L 3G1 [email protected] [email protected] [email protected] Fakhri Karray University of Waterloo Waterloo, Ontario, Canada. N2L 3G1 [email protected] ABSTRACT 1. INTRODUCTION Short documents are typically represented by very sparse In social media, users usually post short texts. Twitter vectors, in the space of terms. In this case, traditional limits the length of each Tweet to 140 characters; therefore, techniques for calculating text similarity results in measures developing data mining techniques to handle the large vol- which are very close to zero, since documents even the very ume of short texts has become an important goal [1]. Text similar ones have a very few or mostly no terms in common. document clustering has been widely used to organize doc- In order to alleviate this limitation, the representation of ument databases and discover similarity and topics among short-text segments should be enriched by incorporating in- documents. Short text clustering is more challenging than formation about correlation between terms. In other words, regular text clustering; due to the sparsity and noise, they if two short segments do not have any common words, but provide very few contextual clues for applying traditional terms from the ﬁrst segment appear frequently with terms data mining techniques [2]; therefore, short documents re- from the second segment in other documents, this means quire diﬀerent or more adapted approaches.
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