Towards Semantic Social Networks

Jung, Jason J.; Euzenat, Jérôme

doi:10.1007/978-3-540-72667-8_20

Cited by 65 publications

(34 citation statements)

References 14 publications

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“…Two social networks FOAF (Friend-of-a-Friend) and DBLP (co-author) networks are integrated in terms of attribute similarity of persons. Jung and Euzenat (2007) construct a semantic social network which includes a social network, an ontology network and a concept network. It is shown that relationships in one network might be inferred from another.…”

Section: Applicationsmentioning

confidence: 99%

Community detection via heterogeneous interaction analysis

Tang

Wang

Liu

2011

Data Min Knowl Disc

178

120

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The pervasiveness of Web 2.0 and social networking sites has enabled people to interact with each other easily through various social media. For instance, popular sites like Del.icio.us, Flickr, and YouTube allow users to comment on shared content (bookmarks, photos, videos), and users can tag their favorite content. Users can also connect with one another, and subscribe to or become a fan or a follower of others. These diverse activities result in a multi-dimensional network among actors, forming group structures with group members sharing similar interests or affiliations. This work systematically addresses two challenges. First, it is challenging to effectively integrate interactions over multiple dimensions to discover hidden community structures shared by heterogeneous interactions. We show that representative community detection methods for single-dimensional networks can be presented in a unified view. Based on this unified view, we present and analyze four possible integration strategies to extend community detection from single-dimensional to multi-dimensional networks. In particular, we propose a novel integration scheme based on structural features. Another challenge is the evaluation of different methods without ground truth information about community membership. We employ a novel cross-dimension network validation (CDNV) procedure to compare the performance of different methods. 123 2 L. Tang et al.We use synthetic data to deepen our understanding, and real-world data to compare integration strategies as well as baseline methods in a large scale. We study further the computational time of different methods, normalization effect during integration, sensitivity to related parameters, and alternative community detection methods for integration.

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Section: Applicationsmentioning

confidence: 99%

Community detection via heterogeneous interaction analysis

Tang

Wang

Liu

2011

Data Min Knowl Disc

178

120

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“…The strict relationship between documents produced in web 2.0 environments and the specific social network [7] bring us to consider the information objects as the result of the activities of the network; consequently, we should also represent social relationships in a well structured way, using approaches based on the semantic web concepts.…”

Section: Social Semantic Webmentioning

confidence: 99%

An Ontology to Model e-portfolio and Social Relationship in Web 2.0 Informal Learning Environments

Taibi

Gentile

Fulantelli

et al. 2010

INT J COMPUT COMMUN

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Web 2.0 applications and the increasingly use of social networks have been creating new informal learning opportunities. Students interact and collaborate using new learning environments which are structurally different from traditional e-learning environments. In these informal unstructured learning contexts the boundaries between the learning contexts and social spheres disappear, and the definition of the students competences appears more and more important. In this paper we propose a semantic web approach in order to create the basis for a software platform to model learner profiles. In particular we propose to extend the FOAF ontology, used to describe people and their personal relationships, with an ontology related to the IMS Learning Portfolio used to model students' competencies. This ontology could be a fundamental layer for a new Web 2.0 learning environment in which students' informal learning activities carried out in social networks can be managed and evaluated.

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“…Erétéo et al [4] present a framework to provide "semantic aware social network analysis". Jung and Euzenat [2] propose a three layer model to represent and extract information from social networks. In terms of data management as previously defined, none of these models cope with the requirements.…”

Section: Network Data Models and Databasesmentioning

confidence: 99%

“…The recording of social interactions over the Web, and the tagging and annotation of data are creating networks of data with the structure of what is classically known as social networks [1,2,3]. Examples include Wikipedia, del.icio.us, YouTube, DBLP, Flickr, Facebook, and the blogosphere.…”

Section: Introductionmentioning

confidence: 99%

Representing, Querying and Transforming Social Networks with RDF/SPARQL

Martín

Gutiérrez

2009

Lecture Notes in Computer Science

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Abstract. As social networks are becoming ubiquitous on the Web, the Semantic Web goals indicate that it is critical to have a standard model allowing exchange, interoperability, transformation, and querying of social network data.In this paper we show that RDF/SPARQL meet this desiderata. Building on developments of social network analysis, graph databases and Semantic Web, we present a social networks data model based on RDF, and a query and transformation language based on SPARQL meeting the above requirements. We study its expressive power and complexity showing that it behaves well, and present an illustrative prototype.

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Towards Semantic Social Networks

Cited by 65 publications

References 14 publications

Community detection via heterogeneous interaction analysis

Community detection via heterogeneous interaction analysis

An Ontology to Model e-portfolio and Social Relationship in Web 2.0 Informal Learning Environments

Representing, Querying and Transforming Social Networks with RDF/SPARQL

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