OBJECTIVES:
The student should be made to:
Understand the concept of semantic web and related applications.
Learn knowledge representation using ontology.
Understand human behaviour in social web and related communities.
Learn visualization of social networks.
UNIT I INTRODUCTION (9)
Introduction to Semantic Web: Limitations of current Web - Development of Semantic Web -
Emergence of the Social Web - Social Network analysis: Development of Social Network Analysis -
Key concepts and measures in network analysis - Electronic sources for network analysis: Electronic
discussion networks, Blogs and online communities - Web-based networks - Applications of Social
Network Analysis.
UNIT II MODELLING, AGGREGATING AND KNOWLEDGE
REPRESENTATION (9)
Ontology and their role in the Semantic Web: Ontology-based knowledge Representation - Ontology
languages for the Semantic Web: Resource Description Framework - Web Ontology Language -
Modelling and aggregating social network data: State-of-the-art in network data representation -
Ontological representation of social individuals - Ontological representation of social relationships -
Aggregating and reasoning with social network data - Advanced representations.
UNIT III EXTRACTION AND MINING COMMUNITIES IN WEB SOCIAL
NETWORKS (9)
Extracting evolution of Web Community from a Series of Web Archive - Detecting communities in
social networks - Definition of community - Evaluating communities - Methods for community
detection and mining - Applications of community mining algorithms - Tools for detecting communities
social network infrastructures and communities - Decentralized online social networks - Multi-
Relational characterization of dynamic social network communities.
UNIT IV PREDICTING HUMAN BEHAVIOUR AND PRIVACY ISSUES (9)
Understanding and predicting human behaviour for social communities - User data management -
Inference and Distribution - Enabling new human experiences - Reality mining - Context - Awareness
- Privacy in online social networks - Trust in online environment - Trust models based on subjective
logic - Trust network analysis - Trust transitivity analysis - Combining trust and reputation - Trust
derivation based on trust comparisons - Attack spectrum and countermeasures.
UNIT V VISUALIZATION AND APPLICATIONS OF SOCIAL NETWORKS (9)
Graph theory - Centrality - Clustering - Node-Edge Diagrams - Matrix representation - Visualizing
online social networks, Visualizing social networks with matrix-based representations - Matrix and
Node-Link Diagrams - Hybrid representations - Applications - Cover networks - Community welfare -Collaboration networks - Co-Citation networks.
TOTAL: 45 PERIODS
OUTCOMES:
Upon completion of the course, the student should be able to:
Develop semantic web related applications.
Represent knowledge using ontology.
Predict human behaviour in social web and related communities.
Visualize social networks.
TEXT BOOKS:
1. Peter Mika, “Social Networks and the Semantic Web”, First Edition, Springer 2007.
2. Borko Furht, “Handbook of Social Network Technologies and Applications”, 1st Edition, Springer,
2010.
REFERENCES:
1. Guandong Xu ,Yanchun Zhang and Lin Li, “Web Mining and Social Networking – Techniques and
applications”, First Edition Springer, 2011.
2. Dion Goh and Schubert Foo, “Social information Retrieval Systems: Emerging Technologies and
Applications for Searching the Web Effectively”, IGI Global Snippet, 2008.
3. Max Chevalier, Christine Julien and Chantal Soulé-Dupuy, “Collaborative and Social Information
Retrieval and Access: Techniques for Improved user Modelling”, IGI Global Snippet, 2009.
4. John G. Breslin, Alexander Passant and Stefan Decker, “The Social Semantic Web”, Springer,
2009.
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