Integrating Folksonomies with the Semantic Web

Specia, Lucia; Motta, Enrico

doi:10.1007/978-3-540-72667-8_44

Cited by 271 publications

(202 citation statements)

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“…describing objects with keywords to enhance content retrieval, avoids the limitations known in ontology building as they allow the instant maintenance of tags. Statistical analysis of the large amount of data produced in tagging application can be used for ontology construction [5,6]. Additionally, Web resources, such as Google, Wikipedia, or Freebase, and terminological resources, such as Wordnet, will be exploited for ontology construction and background enrichment.…”

Section: Methodology and Contributionmentioning

confidence: 99%

Lightweight Community-Driven Ontology Evolution

Siorpaes

2007

The Semantic Web

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Abstract. Only few well-maintained domain ontologies can be found on the Web. The likely reasons for the lack of useful domain ontologies include that (1) informal means to convey intended meaning more efficiently are used for ontology specification only to a very limited extent, (2) many relevant domains of discourse show a substantial degree of conceptual dynamics, (3) ontology representation languages are hard to understand for the majority of (potential) ontology users and domain experts, and (4) the community does not have control over the ontology evolution. In this thesis, we propose to (1) ground a methodology for community-grounded ontology building on the culture and philosophy of wikis by giving users who have no or little expertise in ontology engineering the opportunity to contribute in all stages of the ontology lifecycle and (2) exploit the combination of human and computational intelligence to discover and resolve inconsistencies and align lightweight domain ontologies. The contribution of this thesis is a methodology and prototype for communitygrounded building and evolution of lightweight domain ontologies. Problem StatementEven though one can observe highly active research in the areas of ontologies and the Semantic Web, there is a lack of up-to-date domain ontologies for many fields. The complexity of reality makes building ontologies difficult in several ways: (1) Ontology engineering methodologies tend to focus on formal means for specifying ontologies. However, in order to describe the intended meaning of ontology elements, informal means are also needed. (2) Many relevant domains of discourse, such as ecommerce, show a high degree of conceptual dynamics, i.e. it is hard to keep up with the pace of change in reality [1]. (3) A prerequisite for using an ontology and thus committing to its view of the world is to be able to understand the meaning of concepts and relations [1]. This is problematic for many users, since they cannot easily figure out what they would be committing to when using a particular ontology file from the Web, as the discourse that lead to a certain specification is not available. (4) Most ontologies are built by a group of engineers and the user community does not have direct control over the evolution of the ontology. MotivationA community-oriented approach has several advantages as compared to an isolated engineering-oriented approach, where a small group of engineers carefully constructs

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Section: Methodology and Contributionmentioning

confidence: 99%

Lightweight Community-Driven Ontology Evolution

Siorpaes

2007

The Semantic Web

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“…Such views are more succinct and informative than the original networks. It is for this reason that community detection has found applications in the field of recommendation systems [15][16][17][18], as well as for representing user profiles [19,20]. Other applications that make use of the knowledge extracted from tag communities include sense disambiguation [21] and ontology evolution/population [16].…”

Section: Community Trend and Event Detectionmentioning

confidence: 99%

Extracting Emergent Semantics from Large-Scale User-Generated Content

Kompatsiaris

Diplaris

Papadopoulos

2012

Advances in Intelligent and Soft Computing

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Abstract. This paper presents a survey of novel technologies for uncovering implicit knowledge through the analysis of user-contributed content in Web2.0 applications. The special features of emergent semantics are herein described, along with the various dimensions that the techniques should be able to handle. Consequently a series of application domains is given where the extracted information can be consumed. The relevant techniques are reviewed and categorised according to their capability for scaling, multi-modal analysis, social networks analysis, semantic representation, real-time and spatio-temporal processing. A showcase of such an emergent semantics extraction application, namely ClustTour, is also presented, and open issues and future challenges in this new field are discussed.

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“…Specia & Motta [33] present a combination of pre-processing strategies and statistical techniques together with knowledge provided by ontologies available on the Semantic Web to generate clusters of highly related tags that correspond to ontology concepts. As explained in subsequent sections, we shall also make use of tag processing and filtering techniques, similar to those presented in [33]. Angeletou [4] proposes a semantic enrichment of folksonomies by exploiting online ontologies, thesauri and other knowledge sources to make explicit the semantic relations between social tags.…”

Section: Categorisation Of Social Tagsmentioning

confidence: 99%

Categorising social tags to improve folksonomy-based recommendations

Cantador

Konstas

Jose

2011

Journal of Web Semantics

102

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This is the author’s version of a work that was accepted for publication in Web Semantics: Science, Services and Agents on the World Wide Web. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Web Semantics: Science, Services and Agents on the World Wide Web, 9, 1, (2011) DOI: 10.1016/j.websem.2010.10.001In social tagging systems, users have different purposes when they annotate items. Tags not only depict the content of the annotated items, for example by listing the objects that appear in a photo, or express contextual information about the items, for example by providing the location or the time in which a photo was taken, but also describe subjective qualities and opinions about the items, or can be related to organisational aspects, such as self-references and personal tasks. Current folksonomy-based search and recommendation models exploit the social tag space as a whole to retrieve those items relevant to a tag-based query or user profile, and do not take into consideration the purposes of tags. We hypothesise that a significant percentage of tags are noisy for content retrieval, and believe that the distinction of the personal intentions underlying the tags may be beneficial to improve the accuracy of search and recommendation processes. We present a mechanism to automatically filter and classify raw tags in a set of purpose-oriented categories. Our approach finds the underlying meanings (concepts) of the tags, mapping them to semantic entities belonging to external knowledge bases, namely WordNet and Wikipedia, through the exploitation of ontologies created within the W3C Linking Open Data initiative. The obtained concepts are then transformed into semantic classes that can be uniquely assigned to content- and context-based categories. The identification of subjective and organisational tags is based on natural language processing heuristics. We collected a representative dataset from Flickr social tagging system, and conducted an empirical study to categorise real tagging data, and evaluate whether the resultant tags categories really benefit a recommendation model using the Random Walk with Restarts method. The results show that content- and context-based tags are considered superior to subjective and organisational tags, achieving equivalent performance to using the whole tag space.This research was supported by the European Commission (SALERO, FP6-027122), by the Spanish Ministry of Science and Education (RIM3, TIN2008-06566-C04-02), and by the Spanish Ministry of Industry (i3media, CENIT-2007-1012)

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Integrating Folksonomies with the Semantic Web

Cited by 271 publications

References 10 publications

Lightweight Community-Driven Ontology Evolution

Lightweight Community-Driven Ontology Evolution

Extracting Emergent Semantics from Large-Scale User-Generated Content

Categorising social tags to improve folksonomy-based recommendations

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