A Novel Metric for Information Retrieval in Semantic Networks

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7117)


We propose a novel graph metric for semantic entity-relationship networks. The metric is used for solving two tasks. First, given a semantic entity-relationship graph, such as for example DBpedia, we find relevant neighbors for a given query node. This could be useful for retrieving information relating to a specific entity. Second, we search for paths between two given nodes to discover interesting links. As an example, this can be helpful to analyze the various relationships between Albert Einstein and Niels Bohr. Compared to using the default step metric our approach yields more specific and informative results, as we demonstrate using two semantic web datasets. The proposed metric is defined via paths that maximize the log-likelihood of a restricted round trip and can intuitively be interpreted in terms of random walks on graphs. Our distance metric is also related to the commute distance, which is highly plausible for the described tasks but prohibitively expensive to compute. Our metric can be calculated efficiently using standard graph algorithms, rendering the approach feasible for the very large graphs of the semantic web’s linked data.


Entity-relationship graph information retrieval random walk commute distance graph metric path finding 


  1. Antezana, E., Kuiper, M., Mironov, V.: Biological knowledge management: the emerging role of the Semantic Web technologies. Briefings in Bioinformatics (2009)Google Scholar
  2. Auer, S., Bizer, C., Kobilarov, G., Lehmann, J., Cyganiak, R., Ives, Z.G.: Dbpedia: A Nucleus for a Web of Open Data. In: Aberer, K., Choi, K.-S., Noy, N., Allemang, D., Lee, K.-I., Nixon, L.J.B., Golbeck, J., Mika, P., Maynard, D., Mizoguchi, R., Schreiber, G., Cudré-Mauroux, P. (eds.) ASWC 2007 and ISWC 2007. LNCS, vol. 4825, pp. 722–735. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  3. Baluja, S., Seth, R., Sivakumar, D., Jing, Y., Yagnik, J., Kumar, S., Ravichandran, D., Aly, M.: Video suggestion and discovery for YouTube: taking random walks through the view graph. In: Proceeding of the 17th International Conference on World Wide Web, pp. 895–904. ACM (2008)Google Scholar
  4. Brin, S., Page, L.: The anatomy of a large-scale hypertextual Web search engine. Computer Networks and ISDN Systems 30(1-7), 107–117 (1998)CrossRefGoogle Scholar
  5. Bundschus, M., Dejori, M., Stetter, M., Tresp, V., Kriegel, H.-P.: Extraction of semantic biomedical relations from text using conditional random fields. BMC Bioinformatics 9(1), 207 (2008)CrossRefGoogle Scholar
  6. Dijkstra, E.: A note on two problems in connexion with graphs. Numerische Mathematik 1(1), 269–271 (1959)MathSciNetCrossRefzbMATHGoogle Scholar
  7. Kasneci, G., Suchanek, F., Ifrim, G., Ramanath, M., Weikum, G.: Naga: Searching and ranking knowledge. In: Proc. of ICDE, pp. 1285–1288 (2008)Google Scholar
  8. Klein, D.J., Randić, M.: Resistance distance. Journal of Mathematical Chemistry 12(1), 81–95 (1993)MathSciNetCrossRefGoogle Scholar
  9. Lovász, L.: Random walks on graphs: A survey. Combinatorics, Paul Erdos is Eighty 2(1), 1–46 (1993)MathSciNetGoogle Scholar
  10. Momtchev, V., Peychev, D., Primov, T., Georgiev, G.: Expanding the pathway and interaction knowledge in linked life data. In: Proc. of International Semantic Web Challenge (2009)Google Scholar
  11. Sarkar, P., Moore, A., Prakash, A.: Fast incremental proximity search in large graphs. In: Proceedings of the 25th International Conference on Machine Learning, pp. 896–903. ACM (2008)Google Scholar
  12. Suchanek, F.M., Kasneci, G., Weikum, G.: Yago: A Core of Semantic Knowledge. In: 16th International World Wide Web Conference (WWW 2007). ACM Press, New York (2007)Google Scholar
  13. Yen, J.: Finding the k shortest loopless paths in a network. Management Science 17(11), 712–716 (1971)MathSciNetCrossRefzbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.Corporate TechnologySiemens AGMünchenGermany
  2. 2.Cornell UniversityIthacaUSA

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