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Neural Computing and Applications

, Volume 31, Supplement 2, pp 1135–1144 | Cite as

An MLP-based representation of neural tensor networks for the RDF data models

  • Farhad Abedini
  • Mohammad Bagher MenhajEmail author
  • Mohammad Reza Keyvanpour
Original Article
  • 104 Downloads

Abstract

In this paper, a new representation of neural tensor networks is presented. Recently, state-of-the-art neural tensor networks have been introduced to complete RDF knowledge bases. However, mathematical model representation of these networks is still a challenging problem, due to tensor parameters. To solve this problem, it is proposed that these networks can be represented as two-layer perceptron network. To complete the network topology, the traditional gradient based learning rule is then developed. It should be mentioned that for tensor networks there have been developed some learning rules which are complex in nature due to the complexity of the objective function used. Indeed, this paper is aimed to show that the tensor network can be viewed and represented by the two-layer feedforward neural network in its traditional form. The simulation results presented in the paper easily verify this claim.

Keywords

Knowledge base Neural tensor network MLP Learning rules RDF 

Notes

Compliance with ethical standards

Conflict of interest

The authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest (such as honoraria; educational grants; participation in speakers’ bureaus; membership, employment, consultancies, stock ownership or other equity interest; and expert testimony or patent-licensing arrangements), or non-financial interest (such as personal or professional relationships, affiliations, knowledge or beliefs) in the subject matter or materials discussed in this manuscript.

References

  1. 1.
    Suchanek FM, Kasneci G, Weikum G (2007) Yago: a core of semantic knowledge. In: Proceedings of the 16th international conference on World Wide Web. ACM, pp 697–706Google Scholar
  2. 2.
    Hoffart J, Suchanek FM, Berberich K, Weikum G (2013) YAGO2: a spatially and temporally enhanced knowledge base from Wikipedia. Artif Intell 194:28–61MathSciNetCrossRefzbMATHGoogle Scholar
  3. 3.
    Auer S, Bizer C, Kobilarov G, Lehmann J, Cyganiak R, Ives Z (2007) Dbpedia: a nucleus for a web of open data. Springer, Berlin, pp 722–735Google Scholar
  4. 4.
    Bollacker K, Evans C, Paritosh P, Sturge T, Taylor J (2008) Freebase: a collaboratively created graph database for structuring human knowledge. In: Proceedings of the 2008 ACM SIGMOD international conference on management of data. ACM, pp 1247–1250Google Scholar
  5. 5.
    Miller GA (1995) WordNet: a lexical database for English. Commun ACM 38(11):39–41CrossRefGoogle Scholar
  6. 6.
    Socher R, Chen D, Manning CD, Ng A (2013) Reasoning with neural tensor networks for knowledge base completion. In: Advances in neural information processing systems, pp 926–934Google Scholar
  7. 7.
    West R, Gabrilovich E, Murphy K, Sun S, Gupta R, Lin D (2014) Knowledge base completion via search-based question answering. In: Proceedings of the 23rd international conference on World Wide Web. ACM, pp 515–526Google Scholar
  8. 8.
    He W, Feng Y, Zou L, Zhao D (2015) Knowledge base completion using matrix factorization. In: Web technologies and applications. Springer, pp 256–267Google Scholar
  9. 9.
    Zhao Y, Gao S, Gallinari P, Guo J (2015) Knowledge base completion by learning pairwise-interaction differentiated embeddings. Data Min Knowl Disc 29(5):1486–1504MathSciNetCrossRefzbMATHGoogle Scholar
  10. 10.
    Abedini F, Mirhashem M (2011) SESR: semantic entity extraction for computing semantic relatedness. In: International conference on advanced computer theory and engineering, 4th (ICACTE 2011). ASME PressGoogle Scholar
  11. 11.
    Abedini F, Mahmoudi F, Jadidinejad AH (2011) From text to knowledge: semantic entity extraction using yago ontology. Int J Mach Learn Comput 1(2):113CrossRefGoogle Scholar
  12. 12.
    Abedini F, Mirhashem SM (2012) From text to facts: recognizing ontological facts for a new application. Int J Mach Learn Comput 2(3):183CrossRefGoogle Scholar
  13. 13.
    Suchanek FM, Sozio M, Weikum G (2009) SOFIE: a self-organizing framework for information extraction. In: Proceedings of the 18th international conference on World Wide Web. ACM, pp 631–640Google Scholar
  14. 14.
    Bühmann L, Lehmann J (2013) Pattern based knowledge base enrichment. In: The semantic Web–ISWC 2013. Springer, Berlin, Heidelberg, pp 33–48Google Scholar
  15. 15.
    Hellmann S, Bryl V, Bühmann L, Dojchinovski M, Kontokostas D, Lehmann J, Zamazal O (2014) Knowledge base creation, enrichment and repair. In: Linked open data—creating knowledge out of interlinked data. Springer International Publishing, pp 45–69Google Scholar
  16. 16.
    Khalatbari S, Mirroshandel SA (2015) Automatic construction of domain ontology using Wikipedia and enhancing it by Google Search Engine. Inf Syst Telecommun 3:248–258Google Scholar
  17. 17.
    Bühmann L, Lehmann J (2012). Universal OWL axiom enrichment for large knowledge bases. In: Knowledge engineering and knowledge management. Springer, Berlin, Heidelberg, pp 57–71Google Scholar
  18. 18.
    Bordes A, Weston J, Collobert R, Bengio Y (2011) Learning structured embeddings of knowledge bases. In: Conference on artificial intelligence (No. EPFL-CONF-192344)Google Scholar
  19. 19.
    Jenatton R, Roux NL, Bordes A, Obozinski GR (2012) A latent factor model for highly multi-relational data. In: Advances in neural information processing systems, pp 3167–3175Google Scholar
  20. 20.
    Bordes A, Glorot X, Weston J, Bengio Y (2012) Joint learning of words and meaning representations for open-text semantic parsing. In: International conference on artificial intelligence and statistics, pp 127–135Google Scholar
  21. 21.
    Sutskever I, Tenenbaum JB, Salakhutdinov RR (2009) Modelling relational data using bayesian clustered tensor factorization. In: Advances in neural information processing systems, pp 1821–1828Google Scholar
  22. 22.
    Turian J, Ratinov L, Bengio Y (2010) Word representations: a simple and general method for semi-supervised learning. In: Proceedings of the 48th annual meeting of the association for computational linguistics. Association for Computational Linguistics, pp 384–394Google Scholar
  23. 23.
    Collobert R, Weston J (2008) A unified architecture for natural language processing: deep neural networks with multitask learning. In: Proceedings of the 25th international conference on machine learning. ACM, pp 160–167Google Scholar
  24. 24.
    Hagan MT, Menhaj MB (1994) Training feedforward networks with the Marquardt algorithm. IEEE Trans Neural Netw 5(6):989–993CrossRefGoogle Scholar
  25. 25.
    Tavoosi J, Suratgar AA, Menhaj MB (2015) Stability analysis of recurrent type-2 TSK fuzzy systems with nonlinear consequent part. Neural Comput Appl 28(1):47–56CrossRefGoogle Scholar
  26. 26.
    Phan AH, Cichocki A (2012) Seeking an appropriate alternative least squares algorithm for nonnegative tensor factorizations. Neural Comput Appl 21(4):623–637CrossRefGoogle Scholar
  27. 27.
    Huang S, Chen J, Luo Z (2014) Sparse tensor CCA for color face recognition (Retraction of vol 24, pg 1647, 2014). Neural Comput Appl 25(7–8):2091CrossRefGoogle Scholar
  28. 28.
    Ben X, Zhang P, Yan R, Yang M, Ge G (2016) Gait recognition and micro-expression recognition based on maximum margin projection with tensor representation. Neural Comput Appl 27(8):2629–2646CrossRefGoogle Scholar
  29. 29.
  30. 30.
    Chang KW, Yih WT, Yang B, Meek C (2014) Typed tensor decomposition of knowledge bases for relation extraction. In: EMNLP, pp 1568–1579Google Scholar
  31. 31.
    Iyyer M, Boyd-Graber JL, Claudino LMB, Socher R, Daumé III H (2014) A neural network for factoid question answering over paragraphs. In: EMNLP, pp 633–644Google Scholar
  32. 32.
    Angeli G, Manning CD (2014) NaturalLI: natural logic inference for common sense reasoning. In: EMNLP, pp 534–545Google Scholar
  33. 33.
    Cheng J, Zhang X, Li P, Zhang S, Ding Z, Wang H (2016) Exploring sentiment parsing of microblogging texts for opinion polling on chinese public figures. Appl Intell 45(2):429–442CrossRefGoogle Scholar
  34. 34.
    Zhang X, Du C, Li P, Li Y (2016) Knowledge graph completion via local semantic contexts. In: Database systems for advanced applications. Springer International Publishing, pp 432–446Google Scholar
  35. 35.
    Shi B, Weninger T (2016) Discriminative predicate path mining for fact checking in knowledge graphs. Knowl Based Syst 104:123–133CrossRefGoogle Scholar
  36. 36.
    Ong BT, Sugiura K, Zettsu K (2016) Dynamically pre-trained deep recurrent neural networks using environmental monitoring data for predicting PM2.5. Neural Comput Appl 27(6):1553–1566CrossRefGoogle Scholar
  37. 37.
    Ma H, Tseng YC, Chen LI (2015) A CMAC-based scheme for determining membership with classification of text strings. Neural Comput Appl 27(7):1959–1967CrossRefGoogle Scholar

Copyright information

© The Natural Computing Applications Forum 2017

Authors and Affiliations

  • Farhad Abedini
    • 1
  • Mohammad Bagher Menhaj
    • 2
    Email author
  • Mohammad Reza Keyvanpour
    • 3
  1. 1.Faculty of Computer and Information Technology Engineering, Qazvin BranchIslamic Azad UniversityQazvinIran
  2. 2.Center of Excellence in Control and Robotics, Electrical Engineering DepartmentAmirkabir University of TechnologyTehranIran
  3. 3.Department of Computer EngineeringAlzahra UniversityVanak, TehranIran

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