|Title||Chapter 5: Modeling and Aggregating Social Network Data|
|Publication Type||Book Chapter|
|Year of Publication||2007|
|Authors||Peter Mika, Ramesh Jain, Amit Sheth|
|Keywords||Beyond Computing, Data Integration, Mika, Networks, Online networks, Ontology, Semantic Web, Semantics, Social, social networks, Web, Web 2.0|
Social Networks and the Semantic Web combines the concepts and the methods of two fields of investigation, which together have the power to aid in the analysis of the social Web and the design of a new class of applications that combine human intelligence with machine processing. Social Network Analysis and the emerging Semantic Web are also the fields that stand to gain most from the new Web in achieving their full potential. On the one hand, the social Web delivers social network data at an extraordinary scale, with a dynamics and precision that has been outside of reach for more traditional methods of observing social structure and behavior. In realizing this potential, the technology of the Semantic Web provides the key in aggregating information across heterogeneous sources. The Semantic Web itself benefits by incorporating user-generated metadata and other clues left behind by users.
|Full Text|| |
P. Mika, R. Jain, and Amit Sheth, 'Chapter 5: Modeling and Aggregating Social Network Data,' In Book: Social Networks and the Semantic Web, Springer 2007, pp. 93-120.