Towards the semantic web: Collaborative tag suggestions
Z. Xu, Y. Fu, J. Mao, и D. Su. Proceedings of the Collaborative Web Tagging Workshop at the WWW 2006, Edinburgh, Scotland, (мая 2006)
Аннотация
Content organization over the Internet went through several
interesting phases of evolution: from structured directories to
unstructured Web search engines and more recently, to tagging
as a way for aggregating information, a step towards the
semantic web vision. Tagging allows ranking and data
organization to directly utilize inputs from end users, enabling
machine processing of Web content. Since tags are created by
individual users in a free form, one important problem facing
tagging is to identify most appropriate tags, while eliminating
noise and spam. For this purpose, we define a set of general
criteria for a good tagging system. These criteria include high
coverage of multiple facets to ensure good recall, least effort to
reduce the cost involved in browsing, and high popularity to
ensure tag quality. We propose a collaborative tag suggestion
algorithm using these criteria to spot high-quality tags. The
proposed algorithm employs a goodness measure for tags derived
from collective user authorities to combat spam. The goodness
measure is iteratively adjusted by a reward-penalty algorithm,
which also incorporates other sources of tags, e.g., content-based
auto-generated tags. Our experiments based on My Web 2.0 show
that the algorithm is effective.
%0 Conference Paper
%1 xu2006tsw
%A Xu, Z.
%A Fu, Y.
%A Mao, J.
%A Su, D.
%B Proceedings of the Collaborative Web Tagging Workshop at the WWW 2006
%C Edinburgh, Scotland
%D 2006
%K recommender tag tagging
%T Towards the semantic web: Collaborative tag suggestions
%U http://www.ibiblio.org/www_tagging/2006/13.pdf
%X Content organization over the Internet went through several
interesting phases of evolution: from structured directories to
unstructured Web search engines and more recently, to tagging
as a way for aggregating information, a step towards the
semantic web vision. Tagging allows ranking and data
organization to directly utilize inputs from end users, enabling
machine processing of Web content. Since tags are created by
individual users in a free form, one important problem facing
tagging is to identify most appropriate tags, while eliminating
noise and spam. For this purpose, we define a set of general
criteria for a good tagging system. These criteria include high
coverage of multiple facets to ensure good recall, least effort to
reduce the cost involved in browsing, and high popularity to
ensure tag quality. We propose a collaborative tag suggestion
algorithm using these criteria to spot high-quality tags. The
proposed algorithm employs a goodness measure for tags derived
from collective user authorities to combat spam. The goodness
measure is iteratively adjusted by a reward-penalty algorithm,
which also incorporates other sources of tags, e.g., content-based
auto-generated tags. Our experiments based on My Web 2.0 show
that the algorithm is effective.
@inproceedings{xu2006tsw,
abstract = {Content organization over the Internet went through several
interesting phases of evolution: from structured directories to
unstructured Web search engines and more recently, to tagging
as a way for aggregating information, a step towards the
semantic web vision. Tagging allows ranking and data
organization to directly utilize inputs from end users, enabling
machine processing of Web content. Since tags are created by
individual users in a free form, one important problem facing
tagging is to identify most appropriate tags, while eliminating
noise and spam. For this purpose, we define a set of general
criteria for a good tagging system. These criteria include high
coverage of multiple facets to ensure good recall, least effort to
reduce the cost involved in browsing, and high popularity to
ensure tag quality. We propose a collaborative tag suggestion
algorithm using these criteria to spot high-quality tags. The
proposed algorithm employs a goodness measure for tags derived
from collective user authorities to combat spam. The goodness
measure is iteratively adjusted by a reward-penalty algorithm,
which also incorporates other sources of tags, e.g., content-based
auto-generated tags. Our experiments based on My Web 2.0 show
that the algorithm is effective.},
added-at = {2009-04-27T14:41:20.000+0200},
address = {Edinburgh, Scotland},
author = {Xu, Z. and Fu, Y. and Mao, J. and Su, D.},
biburl = {https://www.bibsonomy.org/bibtex/27e367bbd3d0fe37ab2dd5d9191c4eadd/jaeschke},
booktitle = {Proceedings of the Collaborative Web Tagging Workshop at the WWW 2006},
interhash = {e18fd92b0ffa21b9f0cbb3a2fe15b873},
intrahash = {7e367bbd3d0fe37ab2dd5d9191c4eadd},
keywords = {recommender tag tagging},
month = May,
timestamp = {2014-07-28T15:57:31.000+0200},
title = {Towards the semantic web: Collaborative tag suggestions},
url = {http://www.ibiblio.org/www_tagging/2006/13.pdf},
year = 2006
}