Social Semantic Web

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The concept of the Social Semantic Web subsumes developments in which social interactions on the Web lead to the creation of explicit and semantically rich knowledge representations. The Social Semantic Web can be seen as a Web of collective knowledge systems, which are able to provide useful information based on human contributions and which get better as more people participate.[1] The Social Semantic Web combines technologies, strategies and methodologies from the Semantic Web, social software and the Web 2.0.[2]

Overview

The social-semantic web (s2w) aims to complement the formal Semantic Web vision by adding a pragmatic approach relying on description languages for semantic browsing using heuristic classification and semiotic ontologies. A socio-semantic system has a continuous process of eliciting crucial knowledge of a domain through semi-formal ontologies, taxonomies or folksonomies. S2w emphasize the importance of humanly created loose semantics as means to fulfil the vision of the semantic web. Instead of relying entirely on automated semantics with formal ontology processing and inferencing, humans are collaboratively building semantics aided by socio-semantic information systems. While the semantic web enables integration of business processing with precise automatic logic inference computing across domains, the socio-semantic web opens up for a more social interface to the semantics of businesses, allowing interoperability between business objects, actions and their users.

Socio-semantic web was coined by Manuel Zacklad and Jean-Pierre Cahier in 2003[citation needed] and used in the field of Computer Supported Cooperative Work (CSCW). It recently gained wider appeal after the release of Peter Morville's book Ambient Findability.[3] In Chapter 6 he defines the socio-semantic web as relying on "the pace-layering of ontologies, taxonomies, and folksonomies to learn and adapt as well as teach and remember." We are seeing an increasing use of folksonomies on the web, and a corresponding decrease in the use of hierarchical taxonomies. Morville, the recognized librarian and information architect writes; “I’ll take the ancient tree of knowledge over the transient leaves of popularity any day”.[4] There is undoubtedly scepticism towards the widespread and bushfire like adoption of folksonomies. The socio-semantic web may be seen as a middle way between the top-down monolithic taxonomy approach like the Yahoo! Directory and the more recent collaborative tagging (folksonomy) approaches.

The socio-semantic web differs from the semantic web in that the semantic web often is regarded as a system that will solve the epistemic interoperability issues we have to day. While the semantic web will provide ways for businesses to interoperate across domains the socio-semantic web will enable users to share knowledge.

Morville is vague in his definition of the socio-semantic web and does not lay out any proposed models. We have identified three possible social approaches for solving the problems of user driven ontology evolution for the semantic web. First, users could create a folksonomy (flat taxonomy). With Social Network Analysis (SNA) in conjunction with automated parsers, the ontology could be extracted from the tags and this ontology could be entered into a Topic Map/TMCL[5] or RDF/OWL ontology store. Secondly a set of ontology engineers or ontologists could manually analyze the tags created by the users and by using this data, create a more sound ontology. The third approach is to create a system for self governance where the users themselves create the ontology over time in an organic fashion. All of these approaches could start out with an empty ontology or be seeded manually or with an existing ontology, for example the WordNet ontology.[6] Social Networks Ontology is the most important concept in social web.

Examples

  • DBpedia is a community effort to extract structured information from Wikipedia and to make this information available on the Web. DBpedia allows you to ask sophisticated queries against Wikipedia and to link other datasets on the Web to Wikipedia data.
  • SIOC provides methods for interconnecting discussion methods such as blogs, forums and mailing lists to each other. It consists of the SIOC ontology, an open-standard machine readable format for expressing the information contained both explicitly and implicitly in internet discussion methods, of SIOC metadata producers for a number of popular blogging platforms and content management systems, and of storage and browsing / searching systems for leveraging this SIOC data.
  • OPO provides a way to describe the data relative to user's presence in online social systems, for the purposes of data integration and exchange among heterogeneous systems. The presence information, scattered and distributed all over the Web can be consolidated using OPO-based tools.
  • Stumpedia is a social project and community effort that relies on human participation and folksonomies to index, organize, and review the world wide web. The aim is to help build Natural Language Processing and the Semantic Web.
  • Semandeks is a bottom-up approach for building the semantic web. Its strength is the UI it uses.
  • Twine combines features of forums, wikis, online databases and newsgroups and employs intelligent software to automatically mine and store data relationships expressed using RDF statements.
  • S3DB is a HTTP-REST webservice prototype for a collaborative annotation system defined by a RDFS model.[7]
  • Faviki and Tagnauts are social bookmarking communities which restrict their users to tags to which Wikipedia articles exist.
  • Twarql annotates streaming social data using standard Semantic Web vocabularies such as SIOC, OPO, etc. Therefore, enabling SPARQL to filter social data.
  • FOAF is a machine-readable ontology describing persons, their activities and their relations to other people and objects.

References

  1. ↑ Tom Gruber (2006). "Where the Social Web Meets the Semantic Web". Keynote presentation at ISWC, The 5th International Semantic Web Conference, November 7, 2006
  2. ↑ Katrin Weller (2010), Knowledge Representation in the Social Semantic Web. Berlin: De Gruyter Saur.
  3. ↑ Peter Morville (26 September 2005). Ambient Findability. O'Reilly Media. ISBN 978-0-596-00765-2. 
  4. ↑ (Morville 2005, p. 139)
  5. ↑ "The Topic Map Constraint Language". 
  6. ↑ "Wordnet in RDFS and OWL". 
  7. ↑ Almeida JS, Deus HF, Maass W (2010). "S3DB core: a framework for RDF generation and management in bioinformatics infrastructures". BMC Bioinformatics 11: 387. doi:10.1186/1471-2105-11-387. PMC 2918582. PMID 20646315. 

External links

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