Advertisement

Discovering Graph Patterns for Fact Checking in Knowledge Graphs

  • Peng Lin
  • Qi Song
  • Jialiang Shen
  • Yinghui Wu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10827)

Abstract

Given a knowledge graph and a fact (a triple statement), fact checking is to decide whether the fact belongs to the missing part of the graph. This paper proposes a new fact checking method based on supervised graph pattern mining. Our method discovers discriminant graph patterns associated with the training facts. These patterns can then be used to construct classifiers based on either rules or latent features. (1) We propose a class of graph fact checking rules (\(\mathsf {GFCs}\)). A \(\mathsf {GFC}\) incorporates graph patterns that best distinguish true and false facts of generalized fact statements. We provide quality measures to characterize useful patterns that are both discriminant and diversified. (2) We show that it is feasible to discover \(\mathsf {GFCs}\) in large graphs, by developing a supervised pattern discovery algorithm. To find useful \(\mathsf {GFCs}\) as early as possible, it generates graph patterns relevant to training facts, and dynamically selects patterns from a pattern stream with small update cost per pattern. We further construct two \(\mathsf {GFC}\)-based models, which make use of ordered \(\mathsf {GFCs}\) as predictive rules and latent features from the pattern matches of \(\mathsf {GFCs}\), respectively. Using real-world knowledge bases, we experimentally verify the efficiency and the effectiveness of \(\mathsf {GFC}\)-based techniques for fact checking.

Notes

Acknowledgments

This work is supported in part by NSF IIS-1633629 and Huawei Innovation Research Program (HIRP).

References

  1. 1.
    Badanidiyuru, A., Mirzasoleiman, B., Karbasi, A., Krause, A.: Streaming submodular maximization: massive data summarization on the fly. In: SIGKDD (2014)Google Scholar
  2. 2.
    Carlson, A., Betteridge, J., Kisiel, B., Settles, B., Hruschka Jr., E.R., Mitchell, T.M.: Toward an architecture for never-ending language learning. In: AAAI (2010)Google Scholar
  3. 3.
    Chen, Y., Wang, D.Z.: Knowledge expansion over probabilistic knowledge bases. In: SIGMOD (2014)Google Scholar
  4. 4.
    Ciampaglia, G.L., Shiralkar, P., Rocha, L.M., Bollen, J., Menczer, F., Flammini, A.: Computational fact checking from knowledge networks. PLoS One 10, e0141938 (2015)CrossRefGoogle Scholar
  5. 5.
    Cukierski, W., Hamner, B., Yang, B.: Graph-based features for supervised link prediction. In: IJCNN (2011)Google Scholar
  6. 6.
    Dong, X., Gabrilovich, E., Heitz, G., Horn, W., Lao, N., Murphy, K., Strohmann, T., Sun, S., Zhang, W.: Knowledge vault: a web-scale approach to probabilistic knowledge fusion. In: KDD (2014)Google Scholar
  7. 7.
    Elseidy, M., Abdelhamid, E., Skiadopoulos, S., Kalnis, P.: GraMI: frequent subgraph and pattern mining in a single large graph. PVLDB 7, 517–528 (2014)Google Scholar
  8. 8.
    Fan, W., Wang, X., Wu, Y., Xu, J.: Association rules with graph patterns. PVLDB 8, 1502–1513 (2015)Google Scholar
  9. 9.
    Fan, W., Wu, Y., Xu, J.: Functional dependencies for graphs. In: SIGMOD (2016)Google Scholar
  10. 10.
    Finn, S., Metaxas, P.T., Mustafaraj, E., O’Keefe, M., Tang, L., Tang, S., Zeng, L.: TRAILS: a system for monitoring the propagation of rumors on Twitter. In: Computation and Journalism Symposium, New York City, NY (2014)Google Scholar
  11. 11.
    Galárraga, L., Teflioudi, C., Hose, K., Suchanek, F.M.: Fast rule mining in ontological knowledge bases with AMIE+. VLDB J. 24, 707–730 (2015)CrossRefGoogle Scholar
  12. 12.
    Galárraga, L.A., Teflioudi, C., Hose, K., Suchanek, F.: AMIE: association rule mining under incomplete evidence in ontological knowledge bases. In: WWW (2013)Google Scholar
  13. 13.
    Gardner, M., Mitchell, T.M.: Efficient and expressive knowledge base completion using subgraph feature extraction. In: EMNLP (2015)Google Scholar
  14. 14.
    Goodwin, T.R., Harabagiu, S.M.: Medical question answering for clinical decision support. In: CIKM (2016)Google Scholar
  15. 15.
    Hassan, N., Sultana, A., Wu, Y., Zhang, G., Li, C., Yang, J., Yu, C.: Data in, fact out: automated monitoring of facts by FactWatcher. VLDB 7, 1557–1560 (2014)Google Scholar
  16. 16.
  17. 17.
    Jiang, C., Coenen, F., Zito, M.: A survey of frequent subgraph mining algorithms. Knowl. Eng. Rev. 28, 75–105 (2013)CrossRefGoogle Scholar
  18. 18.
    Lao, N., Mitchell, T., Cohen, W.W.: Random walk inference and learning in a large scale knowledge base. In: EMNLP (2011)Google Scholar
  19. 19.
    Lehmann, J., Isele, R., Jakob, M., Jentzsch, A., Kontokostas, D., Mendes, P.N., Hellmann, S., Morsey, M., Van Kleef, P., Auer, S., et al.: DBpedia-a large-scale, multilingual knowledge base extracted from Wikipedia. Semant. Web 6, 167–195 (2015)Google Scholar
  20. 20.
    Lin, H., Bilmes, J.: A class of submodular functions for document summarization. In: ACL/HLT (2011)Google Scholar
  21. 21.
    Lin, Y., Liu, Z., Sun, M., Liu, Y., Zhu, X.: Learning entity and relation embeddings for knowledge graph completion. In: AAAI (2015)Google Scholar
  22. 22.
    Ma, S., Cao, Y., Fan, W., Huai, J., Wo, T.: Capturing topology in graph pattern matching. VLDB 5, 310–321 (2011)zbMATHGoogle Scholar
  23. 23.
    Nemhauser, G.L., Wolsey, L.A., Fisher, M.L.: An analysis of approximations for maximizing submodular set functions-I. Math. Program. 14, 265–294 (1978)MathSciNetCrossRefGoogle Scholar
  24. 24.
    Nickel, M., Murphy, K., Tresp, V., Gabrilovich, E.: A review of relational machine learning for knowledge graphs. Proc. IEEE 104, 11–33 (2016)CrossRefGoogle Scholar
  25. 25.
    Niu, F., Zhang, C., Ré, C., Shavlik, J.W.: Deepdive: web-scale knowledge-base construction using statistical learning and inference. VLDS 12, 25–28 (2012)Google Scholar
  26. 26.
    Passant, A.: dbrec—music recommendations using DBpedia. In: Patel-Schneider, P.F., Pan, Y., Hitzler, P., Mika, P., Zhang, L., Pan, J.Z., Horrocks, I., Glimm, B. (eds.) ISWC 2010. LNCS, vol. 6497, pp. 209–224. Springer, Heidelberg (2010).  https://doi.org/10.1007/978-3-642-17749-1_14CrossRefGoogle Scholar
  27. 27.
    Paulheim, H.: Knowledge graph refinement: a survey of approaches and evaluation methods. Semant. Web 8, 489–508 (2017)CrossRefGoogle Scholar
  28. 28.
    Shao, C., Ciampaglia, G.L., Flammini, A., Menczer, F.: Hoaxy: a platform for tracking online misinformation. In: WWW Companion (2016)Google Scholar
  29. 29.
    Shi, B., Weninger, T.: Discriminative predicate path mining for fact checking in knowledge graphs. Knowl.-Based Syst. 104, 123–133 (2016)CrossRefGoogle Scholar
  30. 30.
    Sinha, A., Shen, Z., Song, Y., Ma, H., Eide, D., Hsu, B.j.P., Wang, K.: An overview of microsoft academic service (MAS) and applications. In: WWW (2015)Google Scholar
  31. 31.
    Song, C., Ge, T., Chen, C., Wang, J.: Event pattern matching over graph streams. VLDB 8, 413–424 (2014)Google Scholar
  32. 32.
    Song, Q., Wu, Y.: Discovering summaries for knowledge graph search. In: ICDM (2016)Google Scholar
  33. 33.
    Suchanek, F.M., Kasneci, G., Weikum, G.: YAGO: a core of semantic knowledge. In: WWW (2007)Google Scholar
  34. 34.
    Thor, A., Anderson, P., Raschid, L., Navlakha, S., Saha, B., Khuller, S., Zhang, X.-N.: Link prediction for annotation graphs using graph summarization. In: Aroyo, L., Welty, C., Alani, H., Taylor, J., Bernstein, A., Kagal, L., Noy, N., Blomqvist, E. (eds.) ISWC 2011. LNCS, vol. 7031, pp. 714–729. Springer, Heidelberg (2011).  https://doi.org/10.1007/978-3-642-25073-6_45CrossRefGoogle Scholar
  35. 35.
    Vrandečić, D., Krötzsch, M.: Wikidata: a free collaborative knowledgebase. Commun. ACM 57, 78–85 (2014)CrossRefGoogle Scholar
  36. 36.
    Wang, Q., Liu, J., Luo, Y., Wang, B., Lin, C.Y.: Knowledge base completion via coupled path ranking. In: ACL (2016)Google Scholar
  37. 37.
    Wu, Y., Agarwal, P.K., Li, C., Yang, J., Yu, C.: Toward computational fact-checking. PVLDB 7, 589–600 (2014)Google Scholar
  38. 38.
    Yan, X., Cheng, H., Han, J., Yu, P.S.: Mining significant graph patterns by leap search. In: SIGMOD (2008)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Washington State UniversityPullmanUSA
  2. 2.Pacific Northwest National LaboratoryRichlandUSA
  3. 3.Beijing University of Posts and TelecommunicationsBeijingChina

Personalised recommendations