Heymann, P. & Garcia-Molina, H. (2006),
'Collaborative Creation of Communal Hierarchical Taxonomies in Social Tagging Systems'
(2006-10)
, Technical report, Computer Science Department
.
[BibTeX]
[Endnote]
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.
Schmitz, P. (2006),
Inducing Ontology from Flickr Tags., in
'Collaborative Web Tagging Workshop at WWW2006, Edinburgh, Scotland'
.
[BibTeX]
[Endnote]
Cimiano, P.
(2006),
Ontology Learning and Population from Text: Algorithms, Evaluation and Applications
, Springer-Verlag New York, Inc.
, Secaucus, NJ, USA
.
[BibTeX]
[Endnote]
Benz, D. & Hotho, A. (2007),
Position Paper: Ontology Learning from Folksonomies., in
Alexander Hinneburg, ed.,
'LWA 2007: Lernen - Wissen - Adaption, Halle, September 2007, Workshop Proceedings (LWA)'
, Martin-Luther-University Halle-Wittenberg,
, pp. 109-112
.
[BibTeX]
[Endnote]
Limpens, F.; Gandon, F. & Buffa, M. (2008),
'Bridging ontologies and folksonomies to leverage knowledge sharing on the social Web: A brief survey', Automated Software Engineering - Workshops, 2008. ASE Workshops 2008. 23rd IEEE/ACM International Conference on
, 13-18
.
[BibTeX]
[Endnote]
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.
Medelyan, O.; Legg, C.; Milne, D. & Witten, I. H. (2008),
'Mining Meaning from Wikipedia'
, cite arxiv:0809.4530
Comment: An extensive survey of re-using information in Wikipedia in natural
language processing, information retreival and extraction and ontology
building. submitted
.
[BibTeX]
[Endnote]
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.
Zhou, L. (2007),
'Ontology learning: state of the art and open issues', Information Technology and Management
8
(3)
, 241--252
.
[BibTeX]
[Endnote]
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.
Meyer, M.; Rensing, C. & Steinmetz, R. (2008),
'Using community & generated contents as a substitute corpus for metadata generation', Int. J. Adv. Media Commun.
2
(1)
, 59--72
.
[BibTeX]
[Endnote]