The presence of emergent semantics in social annotation systems has been reported in numerous studies. Two important problems in this context are the induction of semantic relations among tags and the discovery of different senses of a given tag. While a number of approaches for discovering tag senses exist, little is known about which

@incollection{niebler2013tagging, author = {Niebler, Thomas and Singer, Philipp and Benz, Dominik and Körner, Christian and Strohmaier, Markus and Hotho, Andreas}, title = {How Tagging Pragmatics Influence Tag Sense Discovery in Social Annotation Systems}, editor = {Serdyukov, Pavel and Braslavski, Pavel and Kuznetsov, SergeiO. and Kamps, Jaap and Rüger, Stefan and Agichtein, Eugene and Segalovich, Ilya and Yilmaz, Emine}, booktitle = {Advances in Information Retrieval}, series = {Lecture Notes in Computer Science}, publisher = {Springer Berlin Heidelberg}, year = {2013}, volume = {7814}, pages = {86-97}, url = {http://dx.doi.org/10.1007/978-3-642-36973-5_8}, doi = {10.1007/978-3-642-36973-5_8}, isbn = {978-3-642-36972-8}, keywords = {pragmtics, tagging, itegpub, ecir13, discovery, l3s, 2013, iteg, sense}, abstract = {The presence of emergent semantics in social annotation systems has been reported in numerous studies. Two important problems in this context are the induction of semantic relations among tags and the discovery of different senses of a given tag. While a number of approaches for discovering tag senses exist, little is known about which } }

%0 = incollection %A = Niebler, Thomas and Singer, Philipp and Benz, Dominik and Körner, Christian and Strohmaier, Markus and Hotho, Andreas %B = Advances in Information Retrieval %D = 2013 %I = Springer Berlin Heidelberg %T = How Tagging Pragmatics Influence Tag Sense Discovery in Social Annotation Systems %U = http://dx.doi.org/10.1007/978-3-642-36973-5_8

The presence of emergent semantics in social annotation systems has been reported in numerous studies. Two important problems in this context are the induction of semantic relations among tags and the discovery of different senses of a given tag. While a number of approaches for discovering tag senses exist, little is known about which

@incollection{niebler2013tagging, author = {Niebler, Thomas and Singer, Philipp and Benz, Dominik and Körner, Christian and Strohmaier, Markus and Hotho, Andreas}, title = {How Tagging Pragmatics Influence Tag Sense Discovery in Social Annotation Systems}, editor = {Serdyukov, Pavel and Braslavski, Pavel and Kuznetsov, SergeiO. and Kamps, Jaap and Rüger, Stefan and Agichtein, Eugene and Segalovich, Ilya and Yilmaz, Emine}, booktitle = {Advances in Information Retrieval}, series = {Lecture Notes in Computer Science}, publisher = {Springer Berlin Heidelberg}, year = {2013}, volume = {7814}, pages = {86-97}, url = {http://dx.doi.org/10.1007/978-3-642-36973-5_8}, doi = {10.1007/978-3-642-36973-5_8}, isbn = {978-3-642-36972-8}, keywords = {pragmtics, tagging, ecir13, discovery, 2013, myown, sense}, abstract = {The presence of emergent semantics in social annotation systems has been reported in numerous studies. Two important problems in this context are the induction of semantic relations among tags and the discovery of different senses of a given tag. While a number of approaches for discovering tag senses exist, little is known about which } }

%0 = incollection %A = Niebler, Thomas and Singer, Philipp and Benz, Dominik and Körner, Christian and Strohmaier, Markus and Hotho, Andreas %B = Advances in Information Retrieval %D = 2013 %I = Springer Berlin Heidelberg %T = How Tagging Pragmatics Influence Tag Sense Discovery in Social Annotation Systems %U = http://dx.doi.org/10.1007/978-3-642-36973-5_8

@article{strohmaier2011evaluation, author = {Strohmaier, Markus and Helic, Denis and Benz, Dominik and Körner, Christian and Kern, Roman}, title = {Evaluation of Folksonomy Induction Algorithms}, journal = {Transactions on Intelligent Systems and Technology}, year = {2012}, url = {http://tist.acm.org/index.html}, keywords = {itegpub, folksonomies, ontology_learning, evaluation} }

%0 = article %A = Strohmaier, Markus and Helic, Denis and Benz, Dominik and Körner, Christian and Kern, Roman %D = 2012 %T = Evaluation of Folksonomy Induction Algorithms %U = http://tist.acm.org/index.html

@article{strohmaier2011evaluation, author = {Strohmaier, Markus and Helic, Denis and Benz, Dominik and Körner, Christian and Kern, Roman}, title = {Evaluation of Folksonomy Induction Algorithms}, journal = {Transactions on Intelligent Systems and Technology}, year = {2012}, url = {http://tist.acm.org/index.html}, keywords = {folksonomies, ontology_learning, myown, 2012, evaluation} }

%0 = article %A = Strohmaier, Markus and Helic, Denis and Benz, Dominik and Körner, Christian and Kern, Roman %D = 2012 %T = Evaluation of Folksonomy Induction Algorithms %U = http://tist.acm.org/index.html

While recent progress has been achieved in understanding the structure and dynamics of social tagging systems, we know little about the underlying user motivations for tagging, and how they influence resulting folksonomies and tags. This paper addresses three issues related to this question. (1) What distinctions of user motivations are identified by previous research, and in what ways are the motivations of users amenable to quantitative analysis? (2) To what extent does tagging motivation vary across different social tagging systems? (3) How does variability in user motivation influence resulting tags and folksonomies? In this paper, we present measures to detect whether a tagger is primarily motivated by categorizing or describing resources, and apply these measures to datasets from seven different tagging systems. Our results show that (a) users’ motivation for tagging varies not only across, but also within tagging systems, and that (b) tag agreement among users who are motivated by categorizing resources is significantly lower than among users who are motivated by describing resources. Our findings are relevant for (1) the development of tag-based user interfaces, (2) the analysis of tag semantics and (3) the design of search algorithms for social tagging systems.

@article{strohmaier2012understanding, author = {Strohmaier, Markus and Körner, Christian and Kern, Roman}, title = {Understanding why users tag: A survey of tagging motivation literature and results from an empirical study}, journal = {Web Semantics: Science, Services and Agents on the World Wide Web}, year = {2012}, volume = {17}, number = {0}, pages = {1 - 11}, url = {http://www.sciencedirect.com/science/article/pii/S1570826812000820}, doi = {10.1016/j.websem.2012.09.003}, issn = {1570-8268}, keywords = {tagging, survey, understanding, motivation, weblog, mining, users}, abstract = {While recent progress has been achieved in understanding the structure and dynamics of social tagging systems, we know little about the underlying user motivations for tagging, and how they influence resulting folksonomies and tags. This paper addresses three issues related to this question. (1) What distinctions of user motivations are identified by previous research, and in what ways are the motivations of users amenable to quantitative analysis? (2) To what extent does tagging motivation vary across different social tagging systems? (3) How does variability in user motivation influence resulting tags and folksonomies? In this paper, we present measures to detect whether a tagger is primarily motivated by categorizing or describing resources, and apply these measures to datasets from seven different tagging systems. Our results show that (a) users’ motivation for tagging varies not only across, but also within tagging systems, and that (b) tag agreement among users who are motivated by categorizing resources is significantly lower than among users who are motivated by describing resources. Our findings are relevant for (1) the development of tag-based user interfaces, (2) the analysis of tag semantics and (3) the design of search algorithms for social tagging systems.} }

%0 = article %A = Strohmaier, Markus and Körner, Christian and Kern, Roman %D = 2012 %T = Understanding why users tag: A survey of tagging motivation literature and results from an empirical study %U = http://www.sciencedirect.com/science/article/pii/S1570826812000820

Recent research has demonstrated how the widespread adoption of collaborative tagging systems yields emergent semantics. In recent years, much has been learned about how to harvest the data produced by taggers for engineering light-weight ontologies. For example, existing measures of tag similarity and tag relatedness have proven crucial step stones for making latent semantic relations in tagging systems explicit. However, little progress has been made on other issues, such as understanding the different levels of tag generality (or tag abstractness), which is essential for, among others, identifying hierarchical relationships between concepts. In this paper we aim to address this gap. Starting from a review of linguistic definitions of word abstractness, we first use several large-scale ontologies and taxonomies as grounded measures of word generality, including Yago, Wordnet, DMOZ and Wikitaxonomy. Then, we introduce and apply several folksonomy-based methods to measure the level of generality of given tags. We evaluate these methods by comparing them with the grounded measures. Our results suggest that the generality of tags in social tagging systems can be approximated with simple measures. Our work has implications for a number of problems related to social tagging systems, including search, tag recommendation, and the acquisition of light-weight ontologies from tagging data.

@inproceedings{benz2011measuring, author = {Benz, Dominik and Körner, Christian and Hotho, Andreas and Stumme, Gerd and Strohmaier, Markus}, title = {One Tag to Bind Them All : Measuring Term Abstractness in Social Metadata}, editor = {Antoniou, Grigoris and Grobelnik, Marko and Simperl, Elena and Parsia, Bijan and Plexousakis, Dimitris and Pan, Jeff and Leenheer, Pieter De}, booktitle = {Proceedings of the 8th Extended Semantic Web Conference (ESWC 2011)}, address = {Heraklion, Crete}, year = {2011}, url = {http://www.kde.cs.uni-kassel.de/pub/pdf/benz2011measuring.pdf}, keywords = {tagging, itegpub, abstractness, 2011, l3s, myown, seminar, summer}, abstract = {Recent research has demonstrated how the widespread adoption of collaborative tagging systems yields emergent semantics. In recent years, much has been learned about how to harvest the data produced by taggers for engineering light-weight ontologies. For example, existing measures of tag similarity and tag relatedness have proven crucial step stones for making latent semantic relations in tagging systems explicit. However, little progress has been made on other issues, such as understanding the different levels of tag generality (or tag abstractness), which is essential for, among others, identifying hierarchical relationships between concepts. In this paper we aim to address this gap. Starting from a review of linguistic definitions of word abstractness, we first use several large-scale ontologies and taxonomies as grounded measures of word generality, including Yago, Wordnet, DMOZ and Wikitaxonomy. Then, we introduce and apply several folksonomy-based methods to measure the level of generality of given tags. We evaluate these methods by comparing them with the grounded measures. Our results suggest that the generality of tags in social tagging systems can be approximated with simple measures. Our work has implications for a number of problems related to social tagging systems, including search, tag recommendation, and the acquisition of light-weight ontologies from tagging data.} }

%0 = inproceedings %A = Benz, Dominik and Körner, Christian and Hotho, Andreas and Stumme, Gerd and Strohmaier, Markus %B = Proceedings of the 8th Extended Semantic Web Conference (ESWC 2011) %C = Heraklion, Crete %D = 2011 %T = One Tag to Bind Them All : Measuring Term Abstractness in Social Metadata %U = http://www.kde.cs.uni-kassel.de/pub/pdf/benz2011measuring.pdf

Recent research has demonstrated how the widespread adoption of collaborative tagging systems yields emergent semantics. In recent years, much has been learned about how to harvest the data produced by taggers for engineering light-weight ontologies. For example, existing measures of tag similarity and tag relatedness have proven crucial step stones for making latent semantic relations in tagging systems explicit. However, little progress has been made on other issues, such as understanding the different levels of tag generality (or tag abstractness), which is essential for, among others, identifying hierarchical relationships between concepts. In this paper we aim to address this gap. Starting from a review of linguistic definitions of word abstractness, we first use several large-scale ontologies and taxonomies as grounded measures of word generality, including Yago, Wordnet, DMOZ and Wikitaxonomy. Then, we introduce and apply several folksonomy-based methods to measure the level of generality of given tags. We evaluate these methods by comparing them with the grounded measures. Our results suggest that the generality of tags in social tagging systems can be approximated with simple measures. Our work has implications for a number of problems related to social tagging systems, including search, tag recommendation, and the acquisition of light-weight ontologies from tagging data.

@inproceedings{benz2011measuring, author = {Benz, Dominik and Körner, Christian and Hotho, Andreas and Stumme, Gerd and Strohmaier, Markus}, title = {One Tag to Bind Them All : Measuring Term Abstractness in Social Metadata}, editor = {Antoniou, Grigoris and Grobelnik, Marko and Simperl, Elena and Parsia, Bijan and Plexousakis, Dimitris and Pan, Jeff and Leenheer, Pieter De}, booktitle = {Proceedings of the 8th Extended Semantic Web Conference (ESWC 2011)}, address = {Heraklion, Crete}, year = {2011}, url = {http://www.kde.cs.uni-kassel.de/pub/pdf/benz2011measuring.pdf}, keywords = {abstractness, 2011, social, myown, measuring, term}, abstract = {Recent research has demonstrated how the widespread adoption of collaborative tagging systems yields emergent semantics. In recent years, much has been learned about how to harvest the data produced by taggers for engineering light-weight ontologies. For example, existing measures of tag similarity and tag relatedness have proven crucial step stones for making latent semantic relations in tagging systems explicit. However, little progress has been made on other issues, such as understanding the different levels of tag generality (or tag abstractness), which is essential for, among others, identifying hierarchical relationships between concepts. In this paper we aim to address this gap. Starting from a review of linguistic definitions of word abstractness, we first use several large-scale ontologies and taxonomies as grounded measures of word generality, including Yago, Wordnet, DMOZ and Wikitaxonomy. Then, we introduce and apply several folksonomy-based methods to measure the level of generality of given tags. We evaluate these methods by comparing them with the grounded measures. Our results suggest that the generality of tags in social tagging systems can be approximated with simple measures. Our work has implications for a number of problems related to social tagging systems, including search, tag recommendation, and the acquisition of light-weight ontologies from tagging data.} }

%0 = inproceedings %A = Benz, Dominik and Körner, Christian and Hotho, Andreas and Stumme, Gerd and Strohmaier, Markus %B = Proceedings of the 8th Extended Semantic Web Conference (ESWC 2011) %C = Heraklion, Crete %D = 2011 %T = One Tag to Bind Them All : Measuring Term Abstractness in Social Metadata %U = http://www.kde.cs.uni-kassel.de/pub/pdf/benz2011measuring.pdf

Recent research has demonstrated how the widespread adoption of collaborative tagging systems yields emergent semantics. In recent years, much has been learned about how to harvest the data produced by taggers for engineering light-weight ontologies. For example, existing measures of tag similarity and tag relatedness have proven crucial step stones for making latent semantic relations in tagging systems explicit. However, little progress has been made on other issues, such as understanding the different levels of tag generality (or tag abstractness), which is essential for, among others, identifying hierarchical relationships between concepts. In this paper we aim to address this gap. Starting from a review of linguistic definitions of word abstractness, we first use several large-scale ontologies and taxonomies as grounded measures of word generality, including Yago, Wordnet, DMOZ and Wikitaxonomy. Then, we introduce and apply several folksonomy-based methods to measure the level of generality of given tags. We evaluate these methods by comparing them with the grounded measures. Our results suggest that the generality of tags in social tagging systems can be approximated with simple measures. Our work has implications for a number of problems related to social tagging systems, including search, tag recommendation, and the acquisition of light-weight ontologies from tagging data.

@inproceedings{benz2011measuring, author = {Benz, Dominik and Körner, Christian and Hotho, Andreas and Stumme, Gerd and Strohmaier, Markus}, title = {One Tag to Bind Them All : Measuring Term Abstractness in Social Metadata}, editor = {Antoniou, Grigoris and Grobelnik, Marko and Simperl, Elena and Parsia, Bijan and Plexousakis, Dimitris and Pan, Jeff and Leenheer, Pieter De}, booktitle = {Proceedings of the 8th Extended Semantic Web Conference (ESWC 2011)}, address = {Heraklion, Crete}, year = {2011}, url = {http://www.kde.cs.uni-kassel.de/pub/pdf/benz2011measuring.pdf}, keywords = {venus, term_abstractness, 2011, eswc, myown, tag_abstractness}, abstract = {Recent research has demonstrated how the widespread adoption of collaborative tagging systems yields emergent semantics. In recent years, much has been learned about how to harvest the data produced by taggers for engineering light-weight ontologies. For example, existing measures of tag similarity and tag relatedness have proven crucial step stones for making latent semantic relations in tagging systems explicit. However, little progress has been made on other issues, such as understanding the different levels of tag generality (or tag abstractness), which is essential for, among others, identifying hierarchical relationships between concepts. In this paper we aim to address this gap. Starting from a review of linguistic definitions of word abstractness, we first use several large-scale ontologies and taxonomies as grounded measures of word generality, including Yago, Wordnet, DMOZ and Wikitaxonomy. Then, we introduce and apply several folksonomy-based methods to measure the level of generality of given tags. We evaluate these methods by comparing them with the grounded measures. Our results suggest that the generality of tags in social tagging systems can be approximated with simple measures. Our work has implications for a number of problems related to social tagging systems, including search, tag recommendation, and the acquisition of light-weight ontologies from tagging data.} }

%0 = inproceedings %A = Benz, Dominik and Körner, Christian and Hotho, Andreas and Stumme, Gerd and Strohmaier, Markus %B = Proceedings of the 8th Extended Semantic Web Conference (ESWC 2011) %C = Heraklion, Crete %D = 2011 %T = One Tag to Bind Them All : Measuring Term Abstractness in Social Metadata %U = http://www.kde.cs.uni-kassel.de/pub/pdf/benz2011measuring.pdf

Recent research has demonstrated how the widespread adoption of collaborative tagging systems yields emergent semantics. In recent years, much has been learned about how to harvest the data produced by taggers for engineering light-weight ontologies. For example, existing measures of tag similarity and tag relatedness have proven crucial step stones for making latent semantic relations in tagging systems explicit. However, little progress has been made on other issues, such as understanding the different levels of tag generality (or tag abstractness), which is essential for, among others, identifying hierarchical relationships between concepts. In this paper we aim to address this gap. Starting from a review of linguistic definitions of word abstractness, we first use several large-scale ontologies and taxonomies as grounded measures of word generality, including Yago, Wordnet, DMOZ and Wikitaxonomy. Then, we introduce and apply several folksonomy-based methods to measure the level of generality of given tags. We evaluate these methods by comparing them with the grounded measures. Our results suggest that the generality of tags in social tagging systems can be approximated with simple measures. Our work has implications for a number of problems related to social tagging systems, including search, tag recommendation, and the acquisition of light-weight ontologies from tagging data.

@inproceedings{benz2011measuring, author = {Benz, Dominik and Körner, Christian and Hotho, Andreas and Stumme, Gerd and Strohmaier, Markus}, title = {One Tag to Bind Them All: Measuring Term Abstractness in Social Metadata}, editor = {Antoniou, Grigoris and Grobelnik, Marko and Simperl, Elena and Parsia, Bijan and Plexousakis, Dimitris and Pan, Jeff and Leenheer, Pieter De}, booktitle = {Proceedings of the 8th Extended Semantic Web Conference (ESWC 2011)}, address = {Heraklion, Crete}, year = {2011}, url = {http://www.kde.cs.uni-kassel.de/pub/pdf/benz2011measuring.pdf}, keywords = {itegpub, abstractness, 2011, social, bind, myown, measuring, term}, abstract = {Recent research has demonstrated how the widespread adoption of collaborative tagging systems yields emergent semantics. In recent years, much has been learned about how to harvest the data produced by taggers for engineering light-weight ontologies. For example, existing measures of tag similarity and tag relatedness have proven crucial step stones for making latent semantic relations in tagging systems explicit. However, little progress has been made on other issues, such as understanding the different levels of tag generality (or tag abstractness), which is essential for, among others, identifying hierarchical relationships between concepts. In this paper we aim to address this gap. Starting from a review of linguistic definitions of word abstractness, we first use several large-scale ontologies and taxonomies as grounded measures of word generality, including Yago, Wordnet, DMOZ and Wikitaxonomy. Then, we introduce and apply several folksonomy-based methods to measure the level of generality of given tags. We evaluate these methods by comparing them with the grounded measures. Our results suggest that the generality of tags in social tagging systems can be approximated with simple measures. Our work has implications for a number of problems related to social tagging systems, including search, tag recommendation, and the acquisition of light-weight ontologies from tagging data.} }

%0 = inproceedings %A = Benz, Dominik and Körner, Christian and Hotho, Andreas and Stumme, Gerd and Strohmaier, Markus %B = Proceedings of the 8th Extended Semantic Web Conference (ESWC 2011) %C = Heraklion, Crete %D = 2011 %T = One Tag to Bind Them All: Measuring Term Abstractness in Social Metadata %U = http://www.kde.cs.uni-kassel.de/pub/pdf/benz2011measuring.pdf

@inproceedings{benz2011measuring, author = {Benz, Dominik and Körner, Christian and Hotho, Andreas and Stumme, Gerd and Strohmaier, Markus}, title = {One Tag to Bind Them All: Measuring Term Abstractness in Social Metadata}, booktitle = {Working Notes of the LWA 2011 - Learning, Knowledge, Adaptation}, year = {2011}, keywords = {itegpub, abstractness, metadata, 2011, social, myown, measuring} }

%0 = inproceedings %A = Benz, Dominik and Körner, Christian and Hotho, Andreas and Stumme, Gerd and Strohmaier, Markus %B = Working Notes of the LWA 2011 - Learning, Knowledge, Adaptation %D = 2011 %T = One Tag to Bind Them All: Measuring Term Abstractness in Social Metadata

@inproceedings{benz2011measuring, author = {Benz, Dominik and Körner, Christian and Hotho, Andreas and Stumme, Gerd and Strohmaier, Markus}, title = {One Tag to Bind Them All: Measuring Term Abstractness in Social Metadata}, booktitle = {Working Notes of the LWA 2011 - Learning, Knowledge, Adaptation}, year = {2011}, keywords = {abstractness, metadata, 2011, social, myown, measuring} }

%0 = inproceedings %A = Benz, Dominik and Körner, Christian and Hotho, Andreas and Stumme, Gerd and Strohmaier, Markus %B = Working Notes of the LWA 2011 - Learning, Knowledge, Adaptation %D = 2011 %T = One Tag to Bind Them All: Measuring Term Abstractness in Social Metadata

While recent research has advanced our understanding about the structure and dynamics of social tagging systems, we know little about (i) the underlying motivations for tagging (why users tag), and (ii) how they influence the properties of resulting tags and folksonomies. In this paper, we focus on problem (i) based on a distinction between two types of user motivations that we have identified in earlier work: Categorizers vs. Describers. To that end, we systematically define and evaluate a number of measures designed to discriminate between describers, i.e. users who use tags for describing resources as opposed to categorizers, i.e. users who use tags for categorizing resources. Subsequently, we present empirical findings from qualitative and quantitative evaluations of the measures on real world tagging behavior. In addition, we conducted a recommender evaluation in which we study the effectiveness of each of the presented measures and found the measure based on the tag content to be the most accurate in predicting the user behavior closely followed by a content independent measure. The overall contribution of this paper is the presentation of empirical evidence that tagging motivation can be approximated with simple statistical measures. Our research is relevant for (a) designers of tagging systems aiming to better understand the motivations of their users and (b) researchers interested in studying the effects of users' tagging motivation on the properties of resulting tags and emergent structures in social tagging systems

@inproceedings{koerner2010categorizers, author = {Körner, Christian and Kern, Roman and Grahsl, Hans-Peter and Strohmaier, Markus}, title = {Of categorizers and describers: an evaluation of quantitative measures for tagging motivation}, booktitle = {HT '10: Proceedings of the 21st ACM Conference on Hypertext and Hypermedia}, publisher = {ACM}, address = {New York, NY, USA}, year = {2010}, pages = {157--166}, url = {http://portal.acm.org/citation.cfm?id=1810617.1810645}, doi = {10.1145/1810617.1810645}, isbn = {978-1-4503-0041-4}, keywords = {tagging, folksonomy, motivation, categorizer, describer}, abstract = {While recent research has advanced our understanding about the structure and dynamics of social tagging systems, we know little about (i) the underlying motivations for tagging (why users tag), and (ii) how they influence the properties of resulting tags and folksonomies. In this paper, we focus on problem (i) based on a distinction between two types of user motivations that we have identified in earlier work: Categorizers vs. Describers. To that end, we systematically define and evaluate a number of measures designed to discriminate between describers, i.e. users who use tags for describing resources as opposed to categorizers, i.e. users who use tags for categorizing resources. Subsequently, we present empirical findings from qualitative and quantitative evaluations of the measures on real world tagging behavior. In addition, we conducted a recommender evaluation in which we study the effectiveness of each of the presented measures and found the measure based on the tag content to be the most accurate in predicting the user behavior closely followed by a content independent measure. The overall contribution of this paper is the presentation of empirical evidence that tagging motivation can be approximated with simple statistical measures. Our research is relevant for (a) designers of tagging systems aiming to better understand the motivations of their users and (b) researchers interested in studying the effects of users' tagging motivation on the properties of resulting tags and emergent structures in social tagging systems} }

%0 = inproceedings %A = Körner, Christian and Kern, Roman and Grahsl, Hans-Peter and Strohmaier, Markus %B = HT '10: Proceedings of the 21st ACM Conference on Hypertext and Hypermedia %C = New York, NY, USA %D = 2010 %I = ACM %T = Of categorizers and describers: an evaluation of quantitative measures for tagging motivation %U = http://portal.acm.org/citation.cfm?id=1810617.1810645

@inproceedings{koerner2010social, author = {Körner, Christian and Benz, Dominik and Hotho, Andreas and Strohmaier, Markus and Stumme, Gerd and C. and Benz and D. and Hotho and A. and Strohmaier and M. and Stumme and G.}, title = {Social Bookmarking Systems: Verbosity Improves Semantics}, booktitle = {Proceedings of INSNA Sunbelt XXX}, address = {Riva del Garda Fierecongressi, Trento, Italy}, year = {2010}, keywords = {2010, myown} }

%0 = inproceedings %A = Körner, Christian and Benz, Dominik and Hotho, Andreas and Strohmaier, Markus and Stumme, Gerd and C. and Benz and D. and Hotho and A. and Strohmaier and M. and Stumme and G. %B = Proceedings of INSNA Sunbelt XXX %C = Riva del Garda Fierecongressi, Trento, Italy %D = 2010 %T = Social Bookmarking Systems: Verbosity Improves Semantics

@inproceedings{koerner2010social, author = {Körner, Christian and Benz, Dominik and Hotho, Andreas and Strohmaier, Markus and Stumme, Gerd}, title = {Social Bookmarking Systems: Verbosity Improves Semantics}, booktitle = {Proceedings of INSNA Sunbelt XXX}, address = {Riva del Garda Fierecongressi, Trento, Italy}, year = {2010}, keywords = {2010, myown} }

%0 = inproceedings %A = Körner, Christian and Benz, Dominik and Hotho, Andreas and Strohmaier, Markus and Stumme, Gerd %B = Proceedings of INSNA Sunbelt XXX %C = Riva del Garda Fierecongressi, Trento, Italy %D = 2010 %T = Social Bookmarking Systems: Verbosity Improves Semantics

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 a 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.

@inproceedings{koerner2010stop, author = {Körner, Christian and Benz, Dominik and Strohmaier, Markus and Hotho, Andreas and Stumme, Gerd}, title = {Stop Thinking, start Tagging - Tag Semantics emerge from Collaborative Verbosity}, booktitle = {Proceedings of the 19th International World Wide Web Conference (WWW 2010)}, publisher = {ACM}, address = {Raleigh, NC, USA}, year = {2010}, url = {http://www.kde.cs.uni-kassel.de/pub/pdf/koerner2010stop.pdf}, keywords = {ol_web2.0, itegpub, www2010, ibm-kde-tagging, collaborative_verbosity, 2010, myown, emergentsemantics_factors, www}, abstract = {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 a 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.} }

%0 = inproceedings %A = Körner, Christian and Benz, Dominik and Strohmaier, Markus and Hotho, Andreas and Stumme, Gerd %B = Proceedings of the 19th International World Wide Web Conference (WWW 2010) %C = Raleigh, NC, USA %D = 2010 %I = ACM %T = Stop Thinking, start Tagging - Tag Semantics emerge from Collaborative Verbosity %U = http://www.kde.cs.uni-kassel.de/pub/pdf/koerner2010stop.pdf

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 a 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.

@inproceedings{koerner2010stop, author = {Körner, Christian and Benz, Dominik and Strohmaier, Markus and Hotho, Andreas and Stumme, Gerd}, title = {Stop Thinking, start Tagging - Tag Semantics emerge from Collaborative Verbosity}, booktitle = {Proceedings of the 19th International World Wide Web Conference (WWW 2010)}, publisher = {ACM}, address = {Raleigh, NC, USA}, year = {2010}, url = {http://www.kde.cs.uni-kassel.de/pub/pdf/koerner2010stop.pdf}, keywords = {ol_web2.0, itegpub, www2010, ibm-kde-tagging, collaborative_verbosity, 2010, myown, emergentsemantics_factors, www}, abstract = {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 a 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.} }

%0 = inproceedings %A = Körner, Christian and Benz, Dominik and Strohmaier, Markus and Hotho, Andreas and Stumme, Gerd %B = Proceedings of the 19th International World Wide Web Conference (WWW 2010) %C = Raleigh, NC, USA %D = 2010 %I = ACM %T = Stop Thinking, start Tagging - Tag Semantics emerge from Collaborative Verbosity %U = http://www.kde.cs.uni-kassel.de/pub/pdf/koerner2010stop.pdf

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 a 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.

@inproceedings{koerner2010thinking, author = {Körner, Christian and Benz, Dominik and Strohmaier, Markus and Hotho, Andreas and Stumme, Gerd}, title = {Stop Thinking, start Tagging - Tag Semantics emerge from Collaborative Verbosity}, booktitle = {Proceedings of the 19th International World Wide Web Conference (WWW 2010)}, publisher = {ACM}, address = {Raleigh, NC, USA}, year = {2010}, url = {http://www.kde.cs.uni-kassel.de/benz/papers/2010/koerner2010thinking.pdf}, keywords = {tagging, semantic, l3s, myown, delicious, bibsonomy, thinking, web, emerge, itegpub, social, start, semantics}, abstract = {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 a 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.} }

%0 = inproceedings %A = Körner, Christian and Benz, Dominik and Strohmaier, Markus and Hotho, Andreas and Stumme, Gerd %B = Proceedings of the 19th International World Wide Web Conference (WWW 2010) %C = Raleigh, NC, USA %D = 2010 %I = ACM %T = Stop Thinking, start Tagging - Tag Semantics emerge from Collaborative Verbosity %U = http://www.kde.cs.uni-kassel.de/benz/papers/2010/koerner2010thinking.pdf

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 a 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.

@inproceedings{koerner2010thinking, author = {Körner, Christian and Benz, Dominik and Strohmaier, Markus and Hotho, Andreas and Stumme, Gerd}, title = {Stop Thinking, start Tagging - Tag Semantics emerge from Collaborative Verbosity}, booktitle = {Proceedings of the 19th International World Wide Web Conference (WWW 2010)}, publisher = {ACM}, address = {Raleigh, NC, USA}, year = {2010}, url = {http://www.kde.cs.uni-kassel.de/benz/papers/2010/koerner2010thinking.pdf}, keywords = {tagging, taggingsurvey, www2010, collaborative, 2010, myown, www}, abstract = {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 a 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.} }

%0 = inproceedings %A = Körner, Christian and Benz, Dominik and Strohmaier, Markus and Hotho, Andreas and Stumme, Gerd %B = Proceedings of the 19th International World Wide Web Conference (WWW 2010) %C = Raleigh, NC, USA %D = 2010 %I = ACM %T = Stop Thinking, start Tagging - Tag Semantics emerge from Collaborative Verbosity %U = http://www.kde.cs.uni-kassel.de/benz/papers/2010/koerner2010thinking.pdf