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Parallel tensor factorization for relational learning

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Abstract

Link prediction is a statistical relational learning problem that has a variety of applications in recommender systems, expert systems, and knowledge bases. Numerous approaches have already been devised to solve the problem. Tensor factorization is one of the ways to solve the link prediction problem. Many tensor factorization techniques have been devised in the last few decades, including Tucker, CANDECOMP/PARAFAC, and DEDICOM. RESCAL is one of the famous tensor factorization technique that can solve large scale problems with relatively less time and space complexity. The time complexity of RESCAL can further be reduced by making it parallel. This variant can also be applied to large scale datasets. This article focuses on devising a parallel version for RESCAL. A decent decrease in execution time has been observed in the execution of parallel RESCAL.

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  1. http://alchemy.cs.washington.edu/data/.

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Authors and Affiliations

  1. College of Technological Innovation, Zayed University, Abu Dhabi, UAE

    Feras Al-Obeidat

  2. ISEG, University of Lisbon, Lisbon, Portugal

    Álvaro Rocha

  3. Department of Computer Science, National University of Computer and Emerging Sciences, Chiniot-Faisalabad, Pakistan

    Muhammad Shahrose Khan

  4. Department of Computer Science and IT, University of Sargodha, Sargodha, Pakistan

    Fahad Maqbool & Saad Razzaq

Authors
  1. Feras Al-Obeidat
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  2. Álvaro Rocha
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  3. Muhammad Shahrose Khan
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  4. Fahad Maqbool
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  5. Saad Razzaq
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Correspondence to Fahad Maqbool.

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Al-Obeidat, F., Rocha, Á., Khan, M.S. et al. Parallel tensor factorization for relational learning. Neural Comput & Applic 34, 8455–8464 (2022). https://doi.org/10.1007/s00521-021-05692-6

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  • DOI: https://doi.org/10.1007/s00521-021-05692-6

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