TY - RPRT AU - Heymann, Paul AU - Garcia-Molina, Hector A2 - T1 - Collaborative Creation of Communal Hierarchical Taxonomies in Social Tagging Systems PB - Computer Science Department AD - PY - 2006/04 VL - IS - 2006-10 SP - EP - UR - http://dbpubs.stanford.edu:8090/pub/2006-10 M3 - KW - clustering KW - collaborative KW - folksonomy KW - kdubiq KW - ol KW - social KW - sosbuch KW - summerschool KW - tagging KW - taggingsurvey KW - taxonomy KW - webzu L1 - N1 - N1 - N1 - AB - Collaborative tagging systems---systems where many casual users annotate objects with free-form strings (tags) of their choosing---have recently emerged as a powerful way to label and organize large collections of data. During our recent investigation into these types of systems, we discovered a simple but remarkably effective algorithm for converting a large corpus of tags annotating objects in a tagging system into a navigable hierarchical taxonomy of tags. We first discuss the algorithm and then present a preliminary model to explain why it is so effective in these types of systems. ER - TY - CONF AU - Schmitz, Patrick A2 - T1 - Inducing Ontology from Flickr Tags. T2 - Collaborative Web Tagging Workshop at WWW2006, Edinburgh, Scotland PB - CY - PY - 2006/05 M2 - VL - IS - SP - EP - UR - http://www.ibiblio.org/www_tagging/2006/22.pdf M3 - KW - clustering KW - folksonomy KW - learning KW - ol KW - ontology KW - semantic KW - sosbuch KW - tagging KW - taggingsurvey KW - toread KW - webzu L1 - SN - N1 - N1 - AB - ER - TY - BOOK AU - Cimiano, Philipp A2 - T1 - Ontology Learning and Population from Text: Algorithms, Evaluation and Applications PB - Springer-Verlag New York, Inc. AD - Secaucus, NJ, USA PY - 2006/ VL - IS - SP - EP - UR - http://portal.acm.org/citation.cfm?id=1177318 M3 - KW - ol KW - text KW - webzu L1 - SN - 0387306323 N1 - Ontology Learning and Population from Text N1 - AB - ER - TY - CONF AU - Benz, Dominik AU - Hotho, Andreas A2 - Hinneburg, Alexander T1 - Position Paper: Ontology Learning from Folksonomies. T2 - LWA 2007: Lernen - Wissen - Adaption, Halle, September 2007, Workshop Proceedings (LWA) PB - Martin-Luther-University Halle-Wittenberg CY - PY - 2007/ M2 - VL - IS - SP - 109 EP - 112 UR - http://dblp.uni-trier.de/db/conf/lwa/lwa2007.html#BenzH07 M3 - KW - 2007 KW - folksonomy KW - kdubiq KW - learning KW - myown KW - ol KW - ontology KW - summerschool KW - webzu L1 - SN - 978-3-86010-907-6 N1 - dblp N1 - AB - ER - TY - JOUR AU - Limpens, Freddy AU - Gandon, Fabien AU - Buffa, Michel T1 - Bridging ontologies and folksonomies to leverage knowledge sharing on the social Web: A brief survey JO - Automated Software Engineering - Workshops, 2008. ASE Workshops 2008. 23rd IEEE/ACM International Conference on PY - 2008/sept. VL - IS - SP - 13 EP - 18 UR - M3 - 10.1109/ASEW.2008.4686305 KW - folksonomy KW - ol KW - ontology KW - survey KW - toread KW - webzu L1 - SN - N1 - Welcome to IEEE Xplore 2.0: Bridging ontologies and folksonomies to leverage knowledge sharing on the social Web: A brief survey N1 - AB - Social tagging systems have recently became very popular as a means to classify large sets of resources shared among on-line communities over the social Web. However, the folksonomies resulting from the use of these systems revealed limitations : tags are ambiguous and their spelling may vary, and folksonomies are difficult to exploit in order to retrieve or exchange information. This article compares the recent attempts to overcome these limitations and to support the use of folksonomies with formal languages and ontologies from the Semantic Web. ER - TY - GEN AU - Medelyan, Olena AU - Legg, Catherine AU - Milne, David AU - Witten, Ian H. A2 - T1 - Mining Meaning from Wikipedia JO - PB - AD - PY - 2008/ VL - IS - SP - EP - UR - http://arxiv.org/abs/0809.4530 M3 - KW - mining KW - ol KW - relation KW - semantic KW - webzu KW - wikipedia L1 - N1 - Mining Meaning from Wikipedia N1 - AB - Wikipedia is a goldmine of information; not just for its many readers, but

also for the growing community of researchers who recognize it as a resource of

exceptional scale and utility. It represents a vast investment of manual effort

and judgment: a huge, constantly evolving tapestry of concepts and relations

that is being applied to a host of tasks.

This article provides a comprehensive description of this work. It focuses on

research that extracts and makes use of the concepts, relations, facts and

descriptions found in Wikipedia, and organizes the work into four broad

categories: applying Wikipedia to natural language processing; using it to

facilitate information retrieval and information extraction; and as a resource

for ontology building. The article addresses how Wikipedia is being used as is,

how it is being improved and adapted, and how it is being combined with other

structures to create entirely new resources. We identify the research groups

and individuals involved, and how their work has developed in the last few

years. We provide a comprehensive list of the open-source software they have

produced. We also discuss the implications of this work for the long-awaited

semantic web.

ER - TY - JOUR AU - Zhou, Lina T1 - Ontology learning: state of the art and open issues JO - Information Technology and Management PY - 2007/10 VL - 8 IS - 3 SP - 241 EP - 252 UR - http://dx.doi.org/10.1007/s10799-007-0019-5 M3 - KW - learning KW - ol KW - ontology KW - survey KW - webzu L1 - SN - N1 - SpringerLink - Zeitschriftenbeitrag N1 - AB - Abstract  Ontology is one of the fundamental cornerstones of the semantic Web. The pervasive use of ontologies in information sharing

and knowledge management calls for efficient and effective approaches to ontology development. Ontology learning, which seeksto discover ontological knowledge from various forms of data automatically or semi-automatically, can overcome the bottleneckof ontology acquisition in ontology development. Despite the significant progress in ontology learning research over the pastdecade, there remain a number of open problems in this field. This paper provides a comprehensive review and discussion ofmajor issues, challenges, and opportunities in ontology learning. We propose a new learning-oriented model for ontology developmentand a framework for ontology learning. Moreover, we identify and discuss important dimensions for classifying ontology learningapproaches and techniques. In light of the impact of domain on choosing ontology learning approaches, we summarize domaincharacteristics that can facilitate future ontology learning effort. The paper offers a road map and a variety of insightsabout this fast-growing field. ER - TY - JOUR AU - Meyer, M. AU - Rensing, C. AU - Steinmetz, R. T1 - Using community & generated contents as a substitute corpus for metadata generation JO - Int. J. Adv. Media Commun. PY - 2008/ VL - 2 IS - 1 SP - 59 EP - 72 UR - http://portal.acm.org/citation.cfm?id=1356291 M3 - http://dx.doi.org/10.1504/IJAMC.2008.016758 KW - elearning KW - ol KW - webzu KW - wikipedia L1 - SN - N1 - Using community-generated contents as a substitute corpus for metadata generation N1 - AB - ER -