�ݺ�ߣshows by User: dbenz

�ݺ�ߣshows by User: dbenz / http://www.slideshare.net/images/logo.gif �ݺ�ߣshows by User: dbenz / Sun, 23 Oct 2011 04:42:25 GMT �ݺ�ߣShare feed for �ݺ�ߣshows by User: dbenz Towards Mining Semantic Maturity in Social Bookmarking Systems /slideshow/towards-mining-semantic-maturity-in-social-bookmarking-systems/9841732 sdow2011slideshare-111023044228-phpapp01
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]]> Sun, 23 Oct 2011 04:42:25 GMT /slideshow/towards-mining-semantic-maturity-in-social-bookmarking-systems/9841732 dbenz@slideshare.net(dbenz) Towards Mining Semantic Maturity in Social Bookmarking Systems dbenz <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/sdow2011slideshare-111023044228-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds" /><br>

Towards Mining Semantic Maturity in Social Bookmarking Systems from Inovex GmbH

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0 One Tag to bind them all: Measuring Term abstractness in Social Metadata /slideshow/one-tag-to-bind-them-all-measuring-term-abstractness-in-social-metadata/8171298 eswc2011dbenz-110601041110-phpapp01
Talk at ESWC 2011, Heraklion, Crete]]>
Talk at ESWC 2011, Heraklion, Crete]]> Wed, 01 Jun 2011 04:11:04 GMT /slideshow/one-tag-to-bind-them-all-measuring-term-abstractness-in-social-metadata/8171298 dbenz@slideshare.net(dbenz) One Tag to bind them all: Measuring Term abstractness in Social Metadata dbenz Talk at ESWC 2011, Heraklion, Crete <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/eswc2011dbenz-110601041110-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds" /><br> Talk at ESWC 2011, Heraklion, Crete

One Tag to bind them all: Measuring Term abstractness in Social Metadata from Inovex GmbH

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0 Stop thinking, start tagging - Tag Semantics emerge from Collaborative Verbosity /dbenz/stop-thinking-start-tagging-tag-semantics-emerge-from-collaborative-verbosity www2010slidesnobackup-100503035051-phpapp01
Recent research provides evidence for the presence of emergent semantics in collaborative tagging systems. While several methods have been proposed, little is known about the factors that influence the evolution of semantic structures in these systems. A natural hypothesis is that the quality of the emergent semantics depends on the pragmatics of tagging: Users with certain usage patterns might contribute more to the resulting semantics than others. In this work, we propose several measures which enable a pragmatic differentiation of taggers by their degree of contribution to emerging semantic structures. We distinguish between categorizers, who typically use a small set of tags as a replacement for hierarchical classification schemes, and describers, who are annotating resources with a wealth of freely associated, descriptive keywords. To study our hypothesis, we apply semantic similarity measures to 64 different partitions of real-world and large-scale folksonomy containing different ratios of categorizers and describers. Our results not only show that ‘verbose’ taggers are most useful for the emergence of tag semantics, but also that a subset containing only 40% of the most ‘verbose’ taggers can produce results that match and even outperform the semantic precision obtained from the whole dataset. Moreover, the results suggest that there exists a causal link between the pragmatics of tagging and resulting emergent semantics. This work is relevant for designers and analysts of tagging systems interested (i) in fostering the semantic development of their platforms, (ii) in identifying users introducing “semantic noise”, and (iii) in learning ontologies.]]>
Recent research provides evidence for the presence of emergent semantics in collaborative tagging systems. While several methods have been proposed, little is known about the factors that influence the evolution of semantic structures in these systems. A natural hypothesis is that the quality of the emergent semantics depends on the pragmatics of tagging: Users with certain usage patterns might contribute more to the resulting semantics than others. In this work, we propose several measures which enable a pragmatic differentiation of taggers by their degree of contribution to emerging semantic structures. We distinguish between categorizers, who typically use a small set of tags as a replacement for hierarchical classification schemes, and describers, who are annotating resources with a wealth of freely associated, descriptive keywords. To study our hypothesis, we apply semantic similarity measures to 64 different partitions of real-world and large-scale folksonomy containing different ratios of categorizers and describers. Our results not only show that ‘verbose’ taggers are most useful for the emergence of tag semantics, but also that a subset containing only 40% of the most ‘verbose’ taggers can produce results that match and even outperform the semantic precision obtained from the whole dataset. Moreover, the results suggest that there exists a causal link between the pragmatics of tagging and resulting emergent semantics. This work is relevant for designers and analysts of tagging systems interested (i) in fostering the semantic development of their platforms, (ii) in identifying users introducing “semantic noise”, and (iii) in learning ontologies.]]> Mon, 03 May 2010 03:50:41 GMT /dbenz/stop-thinking-start-tagging-tag-semantics-emerge-from-collaborative-verbosity dbenz@slideshare.net(dbenz) Stop thinking, start tagging - Tag Semantics emerge from Collaborative Verbosity dbenz Recent research provides evidence for the presence of emergent semantics in collaborative tagging systems. While several methods have been proposed, little is known about the factors that influence the evolution of semantic structures in these systems. A natural hypothesis is that the quality of the emergent semantics depends on the pragmatics of tagging: Users with certain usage patterns might contribute more to the resulting semantics than others. In this work, we propose several measures which enable a pragmatic differentiation of taggers by their degree of contribution to emerging semantic structures. We distinguish between categorizers, who typically use a small set of tags as a replacement for hierarchical classification schemes, and describers, who are annotating resources with a wealth of freely associated, descriptive keywords. To study our hypothesis, we apply semantic similarity measures to 64 different partitions of real-world and large-scale folksonomy containing different ratios of categorizers and describers. Our results not only show that ‘verbose’ taggers are most useful for the emergence of tag semantics, but also that a subset containing only 40% of the most ‘verbose’ taggers can produce results that match and even outperform the semantic precision obtained from the whole dataset. Moreover, the results suggest that there exists a causal link between the pragmatics of tagging and resulting emergent semantics. This work is relevant for designers and analysts of tagging systems interested (i) in fostering the semantic development of their platforms, (ii) in identifying users introducing “semantic noise”, and (iii) in learning ontologies. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/www2010slidesnobackup-100503035051-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds" /><br> Recent research provides evidence for the presence of emergent semantics in collaborative tagging systems. While several methods have been proposed, little is known about the factors that influence the evolution of semantic structures in these systems. A natural hypothesis is that the quality of the emergent semantics depends on the pragmatics of tagging: Users with certain usage patterns might contribute more to the resulting semantics than others. In this work, we propose several measures which enable a pragmatic differentiation of taggers by their degree of contribution to emerging semantic structures. We distinguish between categorizers, who typically use a small set of tags as a replacement for hierarchical classification schemes, and describers, who are annotating resources with a wealth of freely associated, descriptive keywords. To study our hypothesis, we apply semantic similarity measures to 64 different partitions of real-world and large-scale folksonomy containing different ratios of categorizers and describers. Our results not only show that ‘verbose’ taggers are most useful for the emergence of tag semantics, but also that a subset containing only 40% of the most ‘verbose’ taggers can produce results that match and even outperform the semantic precision obtained from the whole dataset. Moreover, the results suggest that there exists a causal link between the pragmatics of tagging and resulting emergent semantics. This work is relevant for designers and analysts of tagging systems interested (i) in fostering the semantic development of their platforms, (ii) in identifying users introducing “semantic noise”, and (iii) in learning ontologies.

Stop thinking, start tagging - Tag Semantics emerge from Collaborative Verbosity from Inovex GmbH

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0 https://cdn.slidesharecdn.com/profile-photo-dbenz-48x48.jpg?cb=1623918055 http://www.kde.cs.uni-kassel.de/benz https://cdn.slidesharecdn.com/ss_thumbnails/sdow2011slideshare-111023044228-phpapp01-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/towards-mining-semantic-maturity-in-social-bookmarking-systems/9841732 Towards Mining Semanti... https://cdn.slidesharecdn.com/ss_thumbnails/eswc2011dbenz-110601041110-phpapp01-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/one-tag-to-bind-them-all-measuring-term-abstractness-in-social-metadata/8171298 One Tag to bind them a... https://cdn.slidesharecdn.com/ss_thumbnails/www2010slidesnobackup-100503035051-phpapp01-thumbnail.jpg?width=320&height=320&fit=bounds dbenz/stop-thinking-start-tagging-tag-semantics-emerge-from-collaborative-verbosity Stop thinking, start t...