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POLYPHONET: An Advanced Social Network Extraction System from the Web. Yutaka Matsuo Junichiro Mori Masahiro Hamasaki National Institute of Advanced University of Tokyo National Institute of Advanced

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polyphonet an advanced social network extraction system from the web

POLYPHONET: An Advanced Social NetworkExtraction System from the Web

Yutaka Matsuo Junichiro Mori Masahiro Hamasaki

National Institute of Advanced University of Tokyo National Institute of Advanced

Industrial Science and Hongo 7-3-1, Tokyo 113-8656 Industrial Science and

Technology Japan Technology

y.matsuo@aist.go.jp jmori@mi.ci.i.u-tokyo.ac.jp hamasaki@ni.aist.go.jp

(WWW2006)

Finding Social Network for Trust Calculation (ECAI 2004)

Yutaka Matsuo, Hironori Tomobe, Koiti Hasida and Mitsuru Ishizuka

abstract
ABSTRACT
  • Social networks in Semantic Web:
    • Knowledge management,
    • Information retrieval,
    • Ubiquitous computing..
  • POLYPHONET:
    • Extract relations of persons
    • Detect groups of persons
    • Obtain keywords for a person.
introduction and related work 1 3
Introduction and Related work – 1/3
  • Social Network :
    • “Please indicate which persons you would regard as your friend.”
  • Social networking services (SNSs)
    • Friendster : http://www.friendster.com/
    • Orkut : http://www.orkut.com/
    • Imeem : http://www.imeem.com/
    • 3600 : http://360.yahoo.com/
  • Web of trust
  • Ontology construction
introduction and related work 2 3
Introduction and Related work – 2/3
  • Referral Web (1995):
    • social network extraction system from the Web
    • Two person X and Y by putting a query “X and Y” to a search engine.
  • Flink :
    • online social networks for a Semantic Web community
    • Given a set of names as input, the component uses a search engine to obtain hit counts
introduction and related work 3 3
Introduction and Related work – 3/3
  • Name disambiguation probability model
  • Co-occurrence information
    • provided by a search engine
    • to detect the proof of relations
    • Google-Hacks [book]
  • PageRank, HITS
    • Web graphs
    • Link structure of Web pages is seen as a social network.
social network extraction 1 4
Social Network Extraction – 1/4
  • Nodes and Edges
    • Nodes: a list of persons is given beforehand
      • JSAI2003,JSAI2004,JSAI2005 and UbiComp2005
    • Edges between of nodes are added using a search engine.
  • Co-occurrence
    • matching coefficient, nX^Y
    • mutual information, log(nX^Y /nXnY )
    • Dice coefficient, (2nX^Y )/(nX+ nY)
    • Jaccard coefficient,(nX^Y /nXvY)
    • overlap coefficient, (nX^Y / min(nX, nY))[ECAI 2004]
    • cosine, (nX^Y / )
advanced extraction
Advanced Extraction
  • Relationship:
    • Relationships between people
    • 30 kinds of relationships
    • http://vocab.org/relationship
  • POLYPHONET
    • Co-author: co-authors of a technical paper
    • Lab: members of the same laboratory or research institute
    • Proj: members of the same project or committee
    • Conf: participants in the same conference or workshop
advanced extraction class of relation 1 2
Advanced Extraction - Class of Relation 1/2
  • GoogleTop(“X Y”,5)
  • C4.5
  • Five-fold cross validation (JSAI Case)

High tf-idf terms manually categorize data set.

advanced extraction scalability 1 3
Advanced Extraction – Scalability 1/3
  • For example -
    • The network density of the JSAI2003 social network is 0.0196 with o.2 threshold.
advanced extraction intellectual link 1 6
Advanced Extraction – Intellectual link 1/6
  • Intellectual link :
    • A relation between a pair of persons with similar interests or citations
  • Evaluation :
    • They plot the probability that the two persons will attend the same session at a JSAI conference.
  • Idea :
    • If two persons are researchers of very similar topics, the distribution of word co-occurrences will be similar.
advanced extraction intellectual link 3 6
Advanced Extraction – Intellectual link3/6
  • Keyword extraction
    • 567 researchers with 3981 pages
    • They gave questionnaires to 10 researchers and defined the correct set of keywords.
conclusion
Conclusion
  • This paper describes a social network mining approach using the Web and organize those methods into small pseudocodes.
  • New aspects of social networks are investigated: classes of relations, scalability, and a person-word matrix.
  • This paper implemented every algorithm on POLYPHONET.