The Experts below are selected from a list of 228 Experts worldwide ranked by ideXlab platform

Ali Dasdan - One of the best experts on this subject based on the ideXlab platform.

  • The Value of Socially Tagged URLs for a Search Engine
    2012
    Co-Authors: Santanu Kolay, Ali Dasdan
    Abstract:

    Social Bookmarking has emerged as a growing source of human generated content on the web. In essence, Bookmarking involves URLs and tags on them. In this paper, we perform a large scale study of the usefulness of bookmarked URLs from the top Social Bookmarking Site Delicious. Instead of focusing on the dimension of tags, which has been covered in the previous work, we explore Social Bookmarking from the dimension of URLs. More specifically, we investigate the Delicious URLs and their content to quantify their value to a search engine. For their value in leading to good content, we show that the Delicious URLs have higher quality content and more external outlinks. For their value in satisfying users, we show that the Delicious URLs have more clicked URLs as well as get more clicks. We suggest that based on their value, the Delicious URLs should be used as another source of seed URLs for crawlers

  • WWW - The value of Socially tagged urls for a search engine
    Proceedings of the 18th international conference on World wide web - WWW '09, 2009
    Co-Authors: Santanu Kolay, Ali Dasdan
    Abstract:

    Social Bookmarking has emerged as a growing source of human generated content on the web. In essence, Bookmarking involves URLs and tags on them. In this paper, we perform a large scale study of the usefulness of bookmarked URLs from the top Social Bookmarking Site Delicious. Instead of focusing on the dimension of tags, which has been covered in the previous work, we explore Social Bookmarking from the dimension of URLs. More specifically, we investigate the Delicious URLs and their content to quantify their value to a search engine. For their value in leading to good content, we show that the Delicious URLs have higher quality content and more external outlinks. For their value in satisfying users, we show that the Delicious URLs have more clicked URLs as well as get more clicks. We suggest that based on their value, the Delicious URLs should be used as another source of seed URLs for crawlers.

Hana Shepherd - One of the best experts on this subject based on the ideXlab platform.

  • Emergence of Consensus and Shared Vocabularies in Collaborative Tagging Systems
    2010
    Co-Authors: Valentin Robu, Harry Halpin, Hana Shepherd
    Abstract:

    This paper uses data from the Social Bookmarking Site del.icio.us to empirically examine the dynamics of collaborative tagging systems and to study how coherent categorization schemes emerge from unsupervised tagging by individual users. First, we study the formation of stable distributions in tagging systems, seen as an implicit form of “consensus ” reached by the users of the system around the tags that best describe a resource. We show that final tag frequencies for most resources converge to power law distributions and we propose an empirical method to examine the dynamics of the convergence process, based on the Kullback-Leibler divergence measure. The convergence analysis is performed both for the most utilized tags at the top of tag distributions and the so-called “long tail.” Second, we study the information structures that emerge from collaborative tagging, namely tag correlation (or folksonomy) graphs. We show how community-based network techniques can be used to extract simple tag vocabularies from the tag correlation graphs by partitioning them into subsets of related tags. Furthermore, we also show, for a specialized domain, that shared vocabularies produced by collaborative tagging are richer than the vocabularies which can be extracted from large-scale query logs provided by a major search engine. Although the empirical analysis presented in this paper is based on a set of tagging data obtained from del.icio.us, the methods developed are general, and the conclusions should be applicable across all webSites that employ tagging. Categories and Subject Descriptors: H.5.3 [Group and organizational interfaces]: Collaborative computing

  • emergence of consensus and shared vocabularies in collaborative tagging systems
    ACM Transactions on The Web, 2009
    Co-Authors: Valentin Robu, Harry Halpin, Hana Shepherd
    Abstract:

    This article uses data from the Social Bookmarking Site del.icio.us to empirically examine the dynamics of collaborative tagging systems and to study how coherent categorization schemes emerge from unsupervised tagging by individual users. First, we study the formation of stable distributions in tagging systems, seen as an implicit form of “consensus” reached by the users of the system around the tags that best describe a resource. We show that final tag frequencies for most resources converge to power law distributions and we propose an empirical method to examine the dynamics of the convergence process, based on the Kullback-Leibler divergence measure. The convergence analysis is performed for both the most utilized tags at the top of tag distributions and the so-called long tail. Second, we study the information structures that emerge from collaborative tagging, namely tag correlation (or folksonomy) graphs. We show how community-based network techniques can be used to extract simple tag vocabularies from the tag correlation graphs by partitioning them into subsets of related tags. Furthermore, we also show, for a specialized domain, that shared vocabularies produced by collaborative tagging are richer than the vocabularies which can be extracted from large-scale query logs provided by a major search engine. Although the empirical analysis presented in this article is based on a set of tagging data obtained from del.icio.us, the methods developed are general, and the conclusions should be applicable across other webSites that employ tagging.

  • Emergence of consensus and shared vocabularies in collaborative tagging systems
    2009
    Co-Authors: Valentin Robu, Harry Halpin, Hana Shepherd
    Abstract:

    This paper uses data from the Social Bookmarking Site del.icio.us to empirically examine the dynamics of collaborative tagging systems and to study how coherent categorization schemes emerge from unsupervised tagging by individual users. First, we study the formation of stable distributions in tagging systems, seen as an implicit form of “consensus ” reached by the users of the system around the tags that best describe a resource. We show that final tag frequencies for most resources converge to power law distributions and we propose an empirical method to examine the dynamics of the convergence process, based on the Kullback-Leibler divergence measure. The convergence analysis is performed both for the most utilized tags at the top of tag distributions and the so-called “long tail.” Second, we study the information structures that emerge from collaborative tagging, namely tag correlation (or folksonomy) graphs. We show how community-based network techniques can be used to extract simple tag vocabularies from the tag correlation graphs by partitioning them into subsets of related tags. Furthermore, we also show, for a specialized domain, that shared vocabularies produced by collaborative tagging are richer than the vocabularies which can be extracted from large-scale query logs provided by a major search engine

  • the complex dynamics of collaborative tagging
    The Web Conference, 2007
    Co-Authors: Harry Halpin, Valentin Robu, Hana Shepherd
    Abstract:

    The debate within the Web community over the optimal means by which to organize information often pits formalized classifications against distributed collaborative tagging systems. A number of questions remain unanswered, however, regarding the nature of collaborative tagging systems including whether coherent categorization schemes can emerge from unsupervised tagging by users. This paper uses data from the Social Bookmarking Site delicio. us to examine the dynamics of collaborative tagging systems. In particular, we examine whether the distribution of the frequency of use of tags for "popular" Sites with a long history (many tags and many users) can be described by a power law distribution, often characteristic of what are considered complex systems. We produce a generative model of collaborative tagging in order to understand the basic dynamics behind tagging, including how a power law distribution of tags could arise. We empirically examine the tagging history of Sites in order to determine how this distribution arises over time and to determine the patterns prior to a stable distribution. Lastly, by focusing on the high-frequency tags of a Site where the distribution of tags is a stabilized power law, we show how tag co-occurrence networks for a sample domain of tags can be used to analyze the meaning of particular tags given their relationship to other tags.

  • WWW - The complex dynamics of collaborative tagging
    Proceedings of the 16th international conference on World Wide Web - WWW '07, 2007
    Co-Authors: Harry Halpin, Valentin Robu, Hana Shepherd
    Abstract:

    The debate within the Web community over the optimal means by which to organize information often pits formalized classifications against distributed collaborative tagging systems. A number of questions remain unanswered, however, regarding the nature of collaborative tagging systems including whether coherent categorization schemes can emerge from unsupervised tagging by users. This paper uses data from the Social Bookmarking Site delicio. us to examine the dynamics of collaborative tagging systems. In particular, we examine whether the distribution of the frequency of use of tags for "popular" Sites with a long history (many tags and many users) can be described by a power law distribution, often characteristic of what are considered complex systems. We produce a generative model of collaborative tagging in order to understand the basic dynamics behind tagging, including how a power law distribution of tags could arise. We empirically examine the tagging history of Sites in order to determine how this distribution arises over time and to determine the patterns prior to a stable distribution. Lastly, by focusing on the high-frequency tags of a Site where the distribution of tags is a stabilized power law, we show how tag co-occurrence networks for a sample domain of tags can be used to analyze the meaning of particular tags given their relationship to other tags.

Harry Halpin - One of the best experts on this subject based on the ideXlab platform.

  • The Semantics of Tagging
    Social Semantics, 2012
    Co-Authors: Harry Halpin
    Abstract:

    This chapter puts forward the hypothesis that the Fregean sense of a URI can be constructed out of user-defined tags, or natural language terms applied to a web-page accessible via a URI using a ‘collaborative’ tagging Site. We use the data from the Social Bookmarking Site http://del.icio.us to empirically examine the dynamics of collaborative tagging systems and to study how coherent categorization schemes emerge from unsupervised tagging by individual users, which can then be considered a ‘stable’ Fregean sense manufactured by a Social consensus. First, we study the formation of stable distributions in tagging systems, seen as an implicit form of consensus reached by the users of the system around the tags that best describe a resource. We show that final tag frequencies for most resources converge to power law distributions and we propose an empirical method to examine the dynamics of the convergence process, based on the Kullback-Leibler divergence measure. The convergence analysis is performed both for the most utilized tags at the top of tag distributions and the long tail. It is assumed such a ‘power-law’ comes from the support the user in the tag selection process by providing tag suggestions, or recommendations, based on a popularity measurement of tags other users provided when tagging the same resource. So we investigate the influence of tag suggestions on the emergence of power-law distributions as a result of collaborative tag behavior. Although previous research has already shown that power-laws emerge in tagging systems, the cause of why power-law distributions emerge is not understood empirically. The majority of theories and mathematical models of tagging found in the literature assume that the emergence of power-laws in tagging systems is mainly driven by the imitation behavior of users when observing tag suggestions provided by the user interface of the tagging system. We present experimental results that show that the power-law distribution forms when tag suggestions are not presented to the users, and the power-law distribution does not hold when there are tag suggestions presented to the user. Looking to see if we can move beyond tagging as in our search for sense, we study the information structures that emerge from collaborative tagging, namely tag correlation (or folksonomy) graphs. We show how community-based network techniques can be used to extract simple tag vocabularies from the tag correlation graphs by partitioning them into subsets of related tags. Furthermore, we also show, for a specialized domain, that shared vocabularies produced by collaborative tagging are richer than the vocabularies which can be extracted from large-scale query logs provided by a major search engine. Although the empirical analysis presented in this paper is based on a set of tagging data obtained from del.icio.us , the methods developed are general, and the conclusions should be applicable across all webSites that employ tagging.

  • Emergence of Consensus and Shared Vocabularies in Collaborative Tagging Systems
    2010
    Co-Authors: Valentin Robu, Harry Halpin, Hana Shepherd
    Abstract:

    This paper uses data from the Social Bookmarking Site del.icio.us to empirically examine the dynamics of collaborative tagging systems and to study how coherent categorization schemes emerge from unsupervised tagging by individual users. First, we study the formation of stable distributions in tagging systems, seen as an implicit form of “consensus ” reached by the users of the system around the tags that best describe a resource. We show that final tag frequencies for most resources converge to power law distributions and we propose an empirical method to examine the dynamics of the convergence process, based on the Kullback-Leibler divergence measure. The convergence analysis is performed both for the most utilized tags at the top of tag distributions and the so-called “long tail.” Second, we study the information structures that emerge from collaborative tagging, namely tag correlation (or folksonomy) graphs. We show how community-based network techniques can be used to extract simple tag vocabularies from the tag correlation graphs by partitioning them into subsets of related tags. Furthermore, we also show, for a specialized domain, that shared vocabularies produced by collaborative tagging are richer than the vocabularies which can be extracted from large-scale query logs provided by a major search engine. Although the empirical analysis presented in this paper is based on a set of tagging data obtained from del.icio.us, the methods developed are general, and the conclusions should be applicable across all webSites that employ tagging. Categories and Subject Descriptors: H.5.3 [Group and organizational interfaces]: Collaborative computing

  • emergence of consensus and shared vocabularies in collaborative tagging systems
    ACM Transactions on The Web, 2009
    Co-Authors: Valentin Robu, Harry Halpin, Hana Shepherd
    Abstract:

    This article uses data from the Social Bookmarking Site del.icio.us to empirically examine the dynamics of collaborative tagging systems and to study how coherent categorization schemes emerge from unsupervised tagging by individual users. First, we study the formation of stable distributions in tagging systems, seen as an implicit form of “consensus” reached by the users of the system around the tags that best describe a resource. We show that final tag frequencies for most resources converge to power law distributions and we propose an empirical method to examine the dynamics of the convergence process, based on the Kullback-Leibler divergence measure. The convergence analysis is performed for both the most utilized tags at the top of tag distributions and the so-called long tail. Second, we study the information structures that emerge from collaborative tagging, namely tag correlation (or folksonomy) graphs. We show how community-based network techniques can be used to extract simple tag vocabularies from the tag correlation graphs by partitioning them into subsets of related tags. Furthermore, we also show, for a specialized domain, that shared vocabularies produced by collaborative tagging are richer than the vocabularies which can be extracted from large-scale query logs provided by a major search engine. Although the empirical analysis presented in this article is based on a set of tagging data obtained from del.icio.us, the methods developed are general, and the conclusions should be applicable across other webSites that employ tagging.

  • Emergence of consensus and shared vocabularies in collaborative tagging systems
    2009
    Co-Authors: Valentin Robu, Harry Halpin, Hana Shepherd
    Abstract:

    This paper uses data from the Social Bookmarking Site del.icio.us to empirically examine the dynamics of collaborative tagging systems and to study how coherent categorization schemes emerge from unsupervised tagging by individual users. First, we study the formation of stable distributions in tagging systems, seen as an implicit form of “consensus ” reached by the users of the system around the tags that best describe a resource. We show that final tag frequencies for most resources converge to power law distributions and we propose an empirical method to examine the dynamics of the convergence process, based on the Kullback-Leibler divergence measure. The convergence analysis is performed both for the most utilized tags at the top of tag distributions and the so-called “long tail.” Second, we study the information structures that emerge from collaborative tagging, namely tag correlation (or folksonomy) graphs. We show how community-based network techniques can be used to extract simple tag vocabularies from the tag correlation graphs by partitioning them into subsets of related tags. Furthermore, we also show, for a specialized domain, that shared vocabularies produced by collaborative tagging are richer than the vocabularies which can be extracted from large-scale query logs provided by a major search engine

  • the complex dynamics of collaborative tagging
    The Web Conference, 2007
    Co-Authors: Harry Halpin, Valentin Robu, Hana Shepherd
    Abstract:

    The debate within the Web community over the optimal means by which to organize information often pits formalized classifications against distributed collaborative tagging systems. A number of questions remain unanswered, however, regarding the nature of collaborative tagging systems including whether coherent categorization schemes can emerge from unsupervised tagging by users. This paper uses data from the Social Bookmarking Site delicio. us to examine the dynamics of collaborative tagging systems. In particular, we examine whether the distribution of the frequency of use of tags for "popular" Sites with a long history (many tags and many users) can be described by a power law distribution, often characteristic of what are considered complex systems. We produce a generative model of collaborative tagging in order to understand the basic dynamics behind tagging, including how a power law distribution of tags could arise. We empirically examine the tagging history of Sites in order to determine how this distribution arises over time and to determine the patterns prior to a stable distribution. Lastly, by focusing on the high-frequency tags of a Site where the distribution of tags is a stabilized power law, we show how tag co-occurrence networks for a sample domain of tags can be used to analyze the meaning of particular tags given their relationship to other tags.

Santanu Kolay - One of the best experts on this subject based on the ideXlab platform.

  • The Value of Socially Tagged URLs for a Search Engine
    2012
    Co-Authors: Santanu Kolay, Ali Dasdan
    Abstract:

    Social Bookmarking has emerged as a growing source of human generated content on the web. In essence, Bookmarking involves URLs and tags on them. In this paper, we perform a large scale study of the usefulness of bookmarked URLs from the top Social Bookmarking Site Delicious. Instead of focusing on the dimension of tags, which has been covered in the previous work, we explore Social Bookmarking from the dimension of URLs. More specifically, we investigate the Delicious URLs and their content to quantify their value to a search engine. For their value in leading to good content, we show that the Delicious URLs have higher quality content and more external outlinks. For their value in satisfying users, we show that the Delicious URLs have more clicked URLs as well as get more clicks. We suggest that based on their value, the Delicious URLs should be used as another source of seed URLs for crawlers

  • WWW - The value of Socially tagged urls for a search engine
    Proceedings of the 18th international conference on World wide web - WWW '09, 2009
    Co-Authors: Santanu Kolay, Ali Dasdan
    Abstract:

    Social Bookmarking has emerged as a growing source of human generated content on the web. In essence, Bookmarking involves URLs and tags on them. In this paper, we perform a large scale study of the usefulness of bookmarked URLs from the top Social Bookmarking Site Delicious. Instead of focusing on the dimension of tags, which has been covered in the previous work, we explore Social Bookmarking from the dimension of URLs. More specifically, we investigate the Delicious URLs and their content to quantify their value to a search engine. For their value in leading to good content, we show that the Delicious URLs have higher quality content and more external outlinks. For their value in satisfying users, we show that the Delicious URLs have more clicked URLs as well as get more clicks. We suggest that based on their value, the Delicious URLs should be used as another source of seed URLs for crawlers.

Valentin Robu - One of the best experts on this subject based on the ideXlab platform.

  • Emergence of Consensus and Shared Vocabularies in Collaborative Tagging Systems
    2010
    Co-Authors: Valentin Robu, Harry Halpin, Hana Shepherd
    Abstract:

    This paper uses data from the Social Bookmarking Site del.icio.us to empirically examine the dynamics of collaborative tagging systems and to study how coherent categorization schemes emerge from unsupervised tagging by individual users. First, we study the formation of stable distributions in tagging systems, seen as an implicit form of “consensus ” reached by the users of the system around the tags that best describe a resource. We show that final tag frequencies for most resources converge to power law distributions and we propose an empirical method to examine the dynamics of the convergence process, based on the Kullback-Leibler divergence measure. The convergence analysis is performed both for the most utilized tags at the top of tag distributions and the so-called “long tail.” Second, we study the information structures that emerge from collaborative tagging, namely tag correlation (or folksonomy) graphs. We show how community-based network techniques can be used to extract simple tag vocabularies from the tag correlation graphs by partitioning them into subsets of related tags. Furthermore, we also show, for a specialized domain, that shared vocabularies produced by collaborative tagging are richer than the vocabularies which can be extracted from large-scale query logs provided by a major search engine. Although the empirical analysis presented in this paper is based on a set of tagging data obtained from del.icio.us, the methods developed are general, and the conclusions should be applicable across all webSites that employ tagging. Categories and Subject Descriptors: H.5.3 [Group and organizational interfaces]: Collaborative computing

  • emergence of consensus and shared vocabularies in collaborative tagging systems
    ACM Transactions on The Web, 2009
    Co-Authors: Valentin Robu, Harry Halpin, Hana Shepherd
    Abstract:

    This article uses data from the Social Bookmarking Site del.icio.us to empirically examine the dynamics of collaborative tagging systems and to study how coherent categorization schemes emerge from unsupervised tagging by individual users. First, we study the formation of stable distributions in tagging systems, seen as an implicit form of “consensus” reached by the users of the system around the tags that best describe a resource. We show that final tag frequencies for most resources converge to power law distributions and we propose an empirical method to examine the dynamics of the convergence process, based on the Kullback-Leibler divergence measure. The convergence analysis is performed for both the most utilized tags at the top of tag distributions and the so-called long tail. Second, we study the information structures that emerge from collaborative tagging, namely tag correlation (or folksonomy) graphs. We show how community-based network techniques can be used to extract simple tag vocabularies from the tag correlation graphs by partitioning them into subsets of related tags. Furthermore, we also show, for a specialized domain, that shared vocabularies produced by collaborative tagging are richer than the vocabularies which can be extracted from large-scale query logs provided by a major search engine. Although the empirical analysis presented in this article is based on a set of tagging data obtained from del.icio.us, the methods developed are general, and the conclusions should be applicable across other webSites that employ tagging.

  • Emergence of consensus and shared vocabularies in collaborative tagging systems
    2009
    Co-Authors: Valentin Robu, Harry Halpin, Hana Shepherd
    Abstract:

    This paper uses data from the Social Bookmarking Site del.icio.us to empirically examine the dynamics of collaborative tagging systems and to study how coherent categorization schemes emerge from unsupervised tagging by individual users. First, we study the formation of stable distributions in tagging systems, seen as an implicit form of “consensus ” reached by the users of the system around the tags that best describe a resource. We show that final tag frequencies for most resources converge to power law distributions and we propose an empirical method to examine the dynamics of the convergence process, based on the Kullback-Leibler divergence measure. The convergence analysis is performed both for the most utilized tags at the top of tag distributions and the so-called “long tail.” Second, we study the information structures that emerge from collaborative tagging, namely tag correlation (or folksonomy) graphs. We show how community-based network techniques can be used to extract simple tag vocabularies from the tag correlation graphs by partitioning them into subsets of related tags. Furthermore, we also show, for a specialized domain, that shared vocabularies produced by collaborative tagging are richer than the vocabularies which can be extracted from large-scale query logs provided by a major search engine

  • the complex dynamics of collaborative tagging
    The Web Conference, 2007
    Co-Authors: Harry Halpin, Valentin Robu, Hana Shepherd
    Abstract:

    The debate within the Web community over the optimal means by which to organize information often pits formalized classifications against distributed collaborative tagging systems. A number of questions remain unanswered, however, regarding the nature of collaborative tagging systems including whether coherent categorization schemes can emerge from unsupervised tagging by users. This paper uses data from the Social Bookmarking Site delicio. us to examine the dynamics of collaborative tagging systems. In particular, we examine whether the distribution of the frequency of use of tags for "popular" Sites with a long history (many tags and many users) can be described by a power law distribution, often characteristic of what are considered complex systems. We produce a generative model of collaborative tagging in order to understand the basic dynamics behind tagging, including how a power law distribution of tags could arise. We empirically examine the tagging history of Sites in order to determine how this distribution arises over time and to determine the patterns prior to a stable distribution. Lastly, by focusing on the high-frequency tags of a Site where the distribution of tags is a stabilized power law, we show how tag co-occurrence networks for a sample domain of tags can be used to analyze the meaning of particular tags given their relationship to other tags.

  • WWW - The complex dynamics of collaborative tagging
    Proceedings of the 16th international conference on World Wide Web - WWW '07, 2007
    Co-Authors: Harry Halpin, Valentin Robu, Hana Shepherd
    Abstract:

    The debate within the Web community over the optimal means by which to organize information often pits formalized classifications against distributed collaborative tagging systems. A number of questions remain unanswered, however, regarding the nature of collaborative tagging systems including whether coherent categorization schemes can emerge from unsupervised tagging by users. This paper uses data from the Social Bookmarking Site delicio. us to examine the dynamics of collaborative tagging systems. In particular, we examine whether the distribution of the frequency of use of tags for "popular" Sites with a long history (many tags and many users) can be described by a power law distribution, often characteristic of what are considered complex systems. We produce a generative model of collaborative tagging in order to understand the basic dynamics behind tagging, including how a power law distribution of tags could arise. We empirically examine the tagging history of Sites in order to determine how this distribution arises over time and to determine the patterns prior to a stable distribution. Lastly, by focusing on the high-frequency tags of a Site where the distribution of tags is a stabilized power law, we show how tag co-occurrence networks for a sample domain of tags can be used to analyze the meaning of particular tags given their relationship to other tags.