Part4 chapter1

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Capturing Emergent Semantics from Social Tagging Systems
Authors Dominik BENZ and Andreas HOTHO
Part Lexical Learning
Topics Tagging
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Capturing Emergent Semantics from Social Tagging Systems

The chapter "Capturing Emergent Semantics from Social Tagging Systems" was written by Dominik BENZ and Andreas HOTHO.

Abstract

With the advent of the so-called “Web 2.0”, the participatory nature of many of its applications has nurtured the vision of many researchers to finally overcome the knowledge acquisition bottleneck inherent to many Semantic Web applications. Despite the uncontrolledness and openness of platforms like collaborative tagging systems, blogs, social networks or wikis, evidence for the presence of emergent semantics within the resulting large bodies of human-annotated content was found. The strengths and weaknesses of traditional top-down approaches (like expert-defined ontologies) and this “wisdom of the crowds” approaches are obviously inverse. Therefore the idea to bridge the gap between the social and the semantic web has motivated a large number of approaches coming from different research areas. In this chapter, we will give a systematic overview of the recent trends and developments within the research branch targeted towards capturing emergent semantics from social tagging systems. Starting with an introduction to the data characteristics, we give an overview of approaches which analyze evidences and factors of emergent semantics. The following main part of this chapter is concerned with various methods geared towards making the implicit semantics within social tagging data explicit. The presentation is structured along a set of comparison dimensions, including the different levels of the ontology learning layer cake. We conclude the chapter by giving an outlook on promising future research directions.

Topics / Key Words

Tagging


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AbstractWith the advent of the so-called “Web 2.0” With the advent of the so-called “Web 2.0”, the participatory nature of many of its applications has nurtured the vision of many researchers to finally overcome

the knowledge acquisition bottleneck inherent to many Semantic Web applications. Despite the uncontrolledness and openness of platforms like collaborative tagging systems, blogs, social networks or wikis, evidence for the presence of emergent semantics within the resulting large bodies of human-annotated content was found. The strengths and weaknesses of traditional top-down approaches (like expert-defined ontologies) and this “wisdom of the crowds” approaches are obviously inverse. Therefore the idea to bridge the gap between the social and the semantic web has motivated a large number of approaches coming from different research areas. In this chapter, we will give a systematic overview of the recent trends and developments within the research branch targeted towards capturing emergent semantics from social tagging systems. Starting with an introduction to the data characteristics, we give an overview of approaches which analyze evidences and factors of emergent semantics. The following main part of this chapter is concerned with various methods geared towards making the implicit semantics within social tagging data explicit. The presentation is structured along a set of comparison dimensions, including the different levels of the ontology learning layer cake. We conclude the

chapter by giving an outlook on promising future research directions.
k on promising future research directions.
AuthorDominik BENZ and Andreas HOTHO  +
Book part titleLexical Learning  +
Chapter titleCapturing Emergent Semantics from Social Tagging Systems  +
TopicsTagging  +
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