Abstract
Mining and explaining relationships between objects are challenging tasks in the field of knowledge search. We propose a new approach for the tasks using disjoint paths formed by links in Wikipedia. To realizing this approach, we propose a naive and a generalized flow based method, and a technique of avoiding flow confluences for forcing a generalized flow to be disjoint as possible. We also apply the approach to classification of relationships. Our experiments reveal that the generalized flow based method can mine many disjoint paths important for a relationship, and the classification is effective for explaining relationships.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Zhang, X., Asano, Y., Yoshikawa, M.: Analysis of implicit relations on Wikipedia: Measuring strength through mining elucidatory objects. In: Kitagawa, H., et al. (eds.) DASFAA 2010. LNCS, vol. 5981, pp. 460–475. Springer, Heidelberg (2010)
Koren, Y., North, S.C., Volinsky, C.: Measuring and extracting proximity in networks. In: Proc. of 12th ACM SIGKDD Conference, pp. 245–255 (2006)
Faloutsos, C., McCurley, K.S., Tomkins, A.: Fast discovery of connection subgraphs. In: Proc. of 10th ACM SIGKDD Conference, pp. 118–127 (2004)
Zhang, B., Li, H., Liu, Y., Ji, L., Xi, W., Fan, W., Chen, Z., Ma, W.Y.: Improving web search results using affinity graph. In: Proc. of 28th SIGIR, pp. 504–511 (2005)
Chen, H., Karger, D.R.: Less is more: probabilistic models for retrieving fewer relevant documents. In: Proc. of 29th SIGIR, pp. 429–436 (2006)
Clarke, C.L., Kolla, M., Cormack, G.V., Vechtomova, O., Ashkan, A., Büttcher, S., MacKinnon, I.: Novelty and diversity in information retrieval evaluation. In: Proc. of 31th SIGIR, pp. 659–666 (2008)
Tong, H., Faloutsos, C.: Center-piece subgraphs: Problem definition and fast solutions. In: Proc. of 12th ACM SIGKDD Conference, pp. 404–413 (2006)
Cheng, J., Ke, Y., Ng, W., Yu, J.X.: Context-aware object connection discovery in large graphs. In: Proc. of 25th ICDE, pp. 856–867 (2009)
Doyle, P.G., Snell, J.L.: Random Walks and Electric Networks, vol. 22. Mathematical Association America, New York (1984)
Zhu, J., Nie, Z., Liu, X., Zhang, B., Wen, J.R.: Statsnowball: a statistical approach to extracting entity relationships. In: Proc. of 18th WWW, pp. 101–110 (2009)
Anyanwu, K., Maduko, A., Sheth, A.P.: Semrank: ranking complex relationship search results on the semantic web. In: Proc. of 14th WWW, pp. 117–127 (2005)
Kasneci, G., Suchanek, F.M., Ifrim, G., Ramanath, M., Weikum, G.: Naga: Searching and ranking knowledge. In: Proc. of 24th ICDE, pp. 953–962 (2008)
Aleman-Meza, B., Halaschek-Wiener, C., Arpinar, I.B., Sheth, A.P.: Context-aware semantic association ranking. In: Proc. of 1st SWDB, pp. 33–50 (2003)
Manning, C., Schutze, H.: Foundations of Statistical Natural Language Processing. MIT Press, Cambridge (1999)
Wayne, K.D.: Generalized Maximum Flow Algorithm. PhD thesis, Cornell University, New York, U.S (January 1999)
Ahuja, R.K., Magnanti, T.L., Orlin, J.B.: Network Flows: Theory, Algorithms, and Applications. Prentice-Hall, New Jersey (1993)
Fung, B.C.M., Wang, K., Ester, M.: Hierarchical document clustering using frequent itemsets. In: Proc. of 3rd SDM (2003)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zhang, X., Asano, Y., Yoshikawa, M. (2010). Mining and Explaining Relationships in Wikipedia. In: Bringas, P.G., Hameurlain, A., Quirchmayr, G. (eds) Database and Expert Systems Applications. DEXA 2010. Lecture Notes in Computer Science, vol 6262. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15251-1_1
Download citation
DOI: https://doi.org/10.1007/978-3-642-15251-1_1
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-15250-4
Online ISBN: 978-3-642-15251-1
eBook Packages: Computer ScienceComputer Science (R0)