your caption here 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 Industrial Science and Hongo 7-3-1, Tokyo Industrial Science and Technology Japan Technology (WWW2006) Finding Social Network for Trust Calculation (ECAI 2004) Yutaka Matsuo, Hironori Tomobe, Koiti Hasida and Mitsuru Ishizuka

your caption here 2 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.

your caption here 3 Introduction and Related work – 1/3 Social Network :  “Please indicate which persons you would regard as your friend.” Social networking services (SNSs)  Friendster :  Orkut :  Imeem :  : Web of trust Ontology construction

your caption here 4 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

your caption here 5 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.

your caption here 6 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, n X^Y  mutual information, log(n X^Y /n X n Y )  Dice coefficient, (2n X^Y )/(n X + n Y )  Jaccard coefficient,(n X^Y /n XvY )  overlap coefficient, (n X^Y / min(n X, n Y )) [ECAI 2004]  cosine, (n X^Y / )

your caption here 7 Social Network Extraction – 2/4 K=30

your caption here 8 Social Network Extraction – 3/4

your caption here 9 Social Network Extraction – 4/4 _

your caption here 10 Advanced Extraction Relationship:  Relationships between people  30 kinds of relationships  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

your caption here 11 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.

your caption here 12 Advanced Extraction - Class of Relation 2/2

your caption here 13 Advanced Extraction – Scalability 1/3 For example -  The network density of the JSAI2003 social network is with o.2 threshold.

your caption here 14 Advanced Extraction – Scalability 2/3 _

your caption here 15 Advanced Extraction – Scalability 3/3

your caption here 16 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.

your caption here 17 Advanced Extraction – Intellectual link 2/6 Termex [37] Keyword extraction

your caption here 18 Advanced Extraction – Intellectual link 3/6 Keyword extraction  567 researchers with 3981 pages  They gave questionnaires to 10 researchers and defined the correct set of keywords.

your caption here 19 Advanced Extraction – Intellectual link 4/6 X 2 idf hit

your caption here 20 Advanced Extraction – Intellectual link 5/6

your caption here 21 Advanced Extraction – Intellectual link 6/6

your caption here 22 POLYPHONET – 1/4

your caption here 23 POLYPHONET – 2/4

your caption here 24 POLYPHONET – 3/4

your caption here 25 POLYPHONET – 4/4

your caption here 26 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.

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