Path-Based Learning for Plant Domain Knowledge Graph

  • Cuicui Dong
  • Huifang Du
  • Yaru Du
  • Ying Chen
  • Wenzhe Li
  • Ming ZhaoEmail author
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 784)


Learning to embed the knowledge graph has been a hot topic in research communities. As for that, TransE is a promising method that can achieve state-of-art performance for many of the benchmark tasks. However, none of the previous work considers the knowledge graph in plant domain in which case the properties of the graph are significantly different. For the knowledge graph in plant domain, most of its relations belong to one-to-many, many-to-one or many-to-many types (actually majority of them are attribute-type relations), which are not in the scope of consideration for classical TransE model. In order to deal with such unique challenges, we propose a novel model called PTA (path-based TransE for attributes). It constructs the relation path by combining attributes and hyponymy relations, and embeds them to a lower dimensional space as well. We conduct extensive experiments on link prediction task where the performance is measured by mean rank and Hit@10. The results show that our new model significantly outperforms other competing methods on several different tasks.


Knowledge graph PTA TransE PtransE 


  1. 1.
    Nickel, M., Murphy, K., Tresp, V., Gabrilovich, E.: A review of relational machine learning for knowledge graphs. arXiv preprint arXiv:1503.00759 (2015)
  2. 2.
    Bordes, A., Usunier, N., Garcia-Duran, A., Weston, J., Yakhnenko, O.: Translating embeddings for modeling multi-relational data. In: Advances in Neural Information Processing Systems, pp. 2787–2795 (2013)Google Scholar
  3. 3.
    Bollacker, K., Evans, C., Paritosh, P., Sturge, T., Taylor, J.: Freebase: a collaboratively created graph database for structuring human knowledge. In: Proceedings of the 2008 ACM SIGMOD International Conference on Management of Data, pp. 1247–1250. ACM, June 2008Google Scholar
  4. 4.
    Miller, G.A.: WordNet: a lexical database for English. Commun. ACM 38(11), 39–41 (1995)CrossRefGoogle Scholar
  5. 5.
    Miller, G.A., Beckwith, R., Fellbaum, C., Gross, D., Miller, K.J.: Introduction to WordNet: an on-line lexical database. Int. J. Lexicogr. 3(4), 235–244 (1990)CrossRefGoogle Scholar
  6. 6.
    Yao, X., Van Durme, B.: Information extraction over structured data: question answering with freebase. In: ACL (1), pp. 956–966 (2014)Google Scholar
  7. 7.
    Frank, A., Krieger, H.U., Xu, F., Uszkoreit, H., Crysmann, B., Jrg, B., Schfer, U.: Question answering from structured knowledge sources. J. Appl. Logic 5(1), 20–48 (2007)CrossRefGoogle Scholar
  8. 8.
    Tarau, P., Figa, E.: Knowledge-based conversational agents and virtual storytelling. In: Proceedings of the 2004 ACM Symposium on Applied Computing, pp. 39–44. ACM, March 2004Google Scholar
  9. 9.
    Hakkani-Tr, D., Celikyilmaz, A., Heck, L.P., Tr, G., Zweig, G.: Probabilistic enrichment of knowledge graph entities for relation detection in conversational understanding. In: INTERSPEECH, pp. 2113–2117, September 2014Google Scholar
  10. 10.
    Milne, D.N., Witten, I.H., Nichols, D.M.: A knowledge-based search engine powered by wikipedia. In: Proceedings of the Sixteenth ACM Conference on Information and Knowledge Management, pp. 445–454. ACM, November 2007Google Scholar
  11. 11.
    Matsuo, Y., Sakaki, T., Uchiyama, K., Ishizuka, M.: Graph-based word clustering using a web search engine. In: Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing, pp. 542–550. Association for Computational Linguistics, July 2006Google Scholar
  12. 12.
    De Bruijn, B., Martin, J.: Getting to the (c)ore of knowledge: mining biomedical literature. Int. J. Med. Informat. 67(1), 7–18 (2002)CrossRefGoogle Scholar
  13. 13.
    Rubin, D.L., Lewis, S.E., Mungall, C.J., Misra, S., Westerfield, M., Ashburner, M., Day-Richter, J., et al.: National center for biomedical ontology: advancing biomedicine through structured organization of scientific knowledge. OMICS: J. Integr. Biol. 10(2), 185–198 (2006)CrossRefGoogle Scholar
  14. 14.
    Fagin, R., Halpern, J.Y., Moses, Y., Vardi, M.: Reasoning About Knowledge. MIT Press, Cambridge (2004)zbMATHGoogle Scholar
  15. 15.
    Chein, M., Mugnier, M.L.: Graph-Based Knowledge Representation: Computational Foundations of Conceptual Graphs. Springer, London (2008)zbMATHGoogle Scholar
  16. 16.
    LibenNowell, D., Kleinberg, J.: The linkprediction problem for social networks. J. Am. Soc. Inform. Sci. Technol. 58(7), 1019–1031 (2007)CrossRefGoogle Scholar
  17. 17.
    Kunegis, J., Lommatzsch, A.: Learning spectral graph transformations for link prediction. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 561–568. ACM, June 2009Google Scholar
  18. 18.
    Lin, Y., Liu, Z., Sun, M., Liu, Y., Zhu, X.: Learning entity and relation embeddings for knowledge graph completion. In: AAAI, pp. 2181–2187, January 2015Google Scholar
  19. 19.
    Socher, R., Chen, D., Manning, C.D., Ng, A.: Reasoning with neural tensor networks for knowledge base completion. In: Advances in Neural Information Processing Systems, pp. 926–934 (2013)Google Scholar
  20. 20.
    Sowa, J.: Knowledge Representation: Logical, Philosophical, and Computational Foundations. PWS Publishing Company, Boston (2000). Book in preparationGoogle Scholar
  21. 21.
    Moore, R.C.: The role of logic in knowledge representation and common-sense reasoning. SRI International. Artificial Intelligence Center (1982)Google Scholar
  22. 22.
    Ishibuchi, H., Nozaki, K., Tanaka, H.: Distributed representation of fuzzy rules and its application to pattern classification. Fuzzy Sets Syst. 52(1), 21–32 (1992)CrossRefGoogle Scholar
  23. 23.
    Grenander, U., Miller, M.I.: Representations of knowledge in complex systems. J. Roy. Stat. Soc.: Ser. B (Methodol.) 56, 549–603 (1994)MathSciNetzbMATHGoogle Scholar
  24. 24.
    Elman, J.L.: Distributed representations, simple recurrent networks, and grammatical structure. Mach. Learn. 7(2–3), 195–225 (1991)Google Scholar
  25. 25.
    Bordes, A., Weston, J., Collobert, R., Bengio, Y.: Learning structured embeddings of knowledge bases. In: Conference on Artificial Intelligence (No. EPFLCONF-192344) (2011)Google Scholar
  26. 26.
    Jenatton, R., Roux, N.L., Bordes, A., Obozinski, G.R.: A latent factor model for highly multi-relational data. In: Advances in Neural Information Processing Systems, pp. 3167–3175 (2012)Google Scholar
  27. 27.
    Nickel, M., Tresp, V., Kriegel, H.P.: Factorizing yago: scalable machine learning for linked data. In: Proceedings of the 21st International Conference on World Wide Web, pp. 271–280. ACM, April 2012Google Scholar
  28. 28.
    Lin, Y.: A note on margin-based loss functions in classification. Stat. Probab. Lett. 68(1), 73–82 (2004)MathSciNetCrossRefzbMATHGoogle Scholar
  29. 29.
    Wang, Z., Zhang, J., Feng, J., Chen, Z.: Knowledge graph embedding by translating on hyperplanes. In: AAAI, pp. 1112–1119, July 2014Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2017

Authors and Affiliations

  • Cuicui Dong
    • 1
  • Huifang Du
    • 1
  • Yaru Du
    • 1
  • Ying Chen
    • 1
  • Wenzhe Li
    • 2
  • Ming Zhao
    • 1
    Email author
  1. 1.College of Information and Electrical EngineeringChina Agricultural UniversityBeijingChina
  2. 2.University of Southern CaliforniaLos AngelesUSA

Personalised recommendations