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Knowledge Fusion via Joint Tensor and Matrix Factorization

  • Zengguang Hao
  • Yafang WangEmail author
  • Zining Liu
  • Gerard de Melo
  • Zenglin Xu
Article
  • 32 Downloads

Abstract

We consider the task of knowledge fusion, an important aspect of cognitive intelligence, with the goal of combining part-of knowledge drawn from different sources. For this, entities and relations are cast into matrix-based representations. Unlike previous work on relation prediction, we consider the challenging setting of graphs with large amounts of completely separate connected components and no overlap between the training and test set entities. In order to address these challenges, we propose a novel cognitively inspired factorization method that jointly factorizes a subject–predicate–object tensor via RESCAL and a similarity matrix via matrix factorization. Our experimental results show that our method significantly outperforms several strong baseline models, including RESCAL and several TransE-style models. The proposed joint factorization of a subject–predicate–object tensor while applying matrix factorization to a similarity matrix obtains substantially higher average accuracy rates than previous approaches. This shows that it can successfully address the challenge of knowledge fusion of disconnected data.

Keywords

Knowledge fusion Connected components Entity overlap Tensor factorization Word similarities 

Notes

Funding Information

The authors would like to acknowledge the financial support provided by the National Natural Science Foundation of China (no. 61503217)

Compliance with Ethical Standards

Conflict of Interest

The authors declare that they have no conflict of interest.

Ethical Approval

This article does not describe any studies with human participants or animals performed by any of the authors.

Informed Consent

Informed consent was not required as no humans or animals were involved.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Zengguang Hao
    • 1
  • Yafang Wang
    • 1
    Email author
  • Zining Liu
    • 1
  • Gerard de Melo
    • 2
  • Zenglin Xu
    • 3
  1. 1.Shandong UniversityJinanChina
  2. 2.Rutgers UniversityNew YorkUSA
  3. 3.University of Electronic Science and Technology of ChinaChengduChina

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