Labeling Categories and Relationships in an Evolving Social Network

  • Ming-Shun Lin
  • Hsin-Hsi Chen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4956)


Modeling and naming general entity-entity relationships is challenging in construction of social networks. Given a seed denoting a person name, we utilize Google search engine, NER (Named Entity Recognizer) parser, and CODC (Co-Occurrence Double Check) formula to construct an evolving social network. For each entity pair in the network, we try to label their categories and relationships. Firstly, we utilize an open directory project (ODP) resource, which is the largest human-edited directory of the web, to build a directed graph, and then use three ranking algorithms, PageRank, HITS, and a Markov chain random process to extract potential categories defined in the ODP. These categories capture the major contexts of the designated named entities. Finally, we combine the ranks of these categories and tf*idf scores of noun phrases to extract relationships. In our experiments, total 6 evolving social networks with 618 pairs of named entities demonstrate that the Markov chain random process is better than the other two algorithms.


Category Labeling Relationships Labeling and Evolving Social Network 


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  1. 1.
    Aleman-Meza, B., Nagarajan, M., Ramakrishnan, C., Ding, L., Kolari, P., Sheth, A., Arpinar, I.B., Joshi, A., Finin, T.: Semantic Analytics on Social Networks: Experiences in Addressing the Problem of Conflict of Interest Detection. In: Proc. WWW Conference, pp. 407–416 (2006)Google Scholar
  2. 2.
    Cinlar, E.: Introduction to Stochastic Processes. Prentice Hall, Englewood Cliffs (1975)zbMATHGoogle Scholar
  3. 3.
    Chen, H.H., Lin, M.S., Wei, Y.C.: Novel Association Measures Using Web Search with Double Checking. In: Proc. COLING-ACL Conference, pp. 1009–1016 (2006)Google Scholar
  4. 4.
    Ferragina, P., Gulli, A.: A Personalized Search Engine Based on Web Snippet Hierarchical Clustering. In: Proc. WWW Conference, pp. 801–810 (2005)Google Scholar
  5. 5.
    Golub, G.H., Greif, C.: Arnoldi-type Algorithms for Computing Stationary Distribution Vectors, with Application to PageRank. Technical Report, SCCM-04-15, Stanford University Technical Report (2004)Google Scholar
  6. 6.
    Kleinberg, J.M.: Authoritative Sources in a Hyperlinked Environment. Journal of the ACM 46(5), 604–632 (1999)zbMATHCrossRefMathSciNetGoogle Scholar
  7. 7.
    Lin, M.S., Chen, H.H.: Constructing a Named Entity Ontology from Web Corpora. In: Proc. LREC Conference, pp. 1450–1453 (2006)Google Scholar
  8. 8.
    Matsuo, Y., Tomobe, H., Hasida, K., Ishizuka, M.: Finding Social Network for Trust Calculation. In: Proc. ECAI Conference, pp. 510–514 (2004)Google Scholar
  9. 9.
    Maguitman, A.G., Menczer, F., Roinestad, H., Vespignani, A.: Algorithmic Detection of Semantic Similarity. In: Proc. WWW Conference, pp. 107–116 (2005)Google Scholar
  10. 10.
    Matsuo, Y., Junichiro, M., Masahiro, H.: Polyphonet: An Advanved Social Network Extraction System from the Web. In: Proc. WWW Conference, pp. 397–406 (2006)Google Scholar
  11. 11.
    Mori, J., Ishizuka, M., Matsuo, Y.: Extracting Keyphrases to Represent Relations in Social Networks from Web. In: Proc. IJCAI Conference, pp. 2820–2825 (2007)Google Scholar
  12. 12.
    Page, L., Brin, S., Motwani, R., Winograd, T.: The PageRank Citation Ranking: Bringing Order to the Web. Stanford Digital Libraries Working Paper (1998)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Ming-Shun Lin
    • 1
  • Hsin-Hsi Chen
    • 1
  1. 1.Department of Computer Science and Information EngineeringNational Taiwan UniversityTaipeiTaiwan

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