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Improving Knowledge Base Completion by Incorporating Implicit Information

  • Wenqiang HeEmail author
  • Yansong Feng
  • Dongyan Zhao
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9544)

Abstract

Over the past few years, many large Knowledge Bases (KBs) have been constructed through relation extraction technology but they are still often incomplete. As a supplement to training a more powerful extractor, Knowledge Base Completion which aims at learning new facts based on existing ones has recently attracted much attention. Most of the existing methods, however, are only utilizing the explicit facts in a single KB. By analyzing the data, we find that some implicit information should also been captured for a more comprehensive consideration during completion process. These information include the intrinsic properties of KBs (e.g. relational constraints) and potential synergies between various KBs (i.e. semantic similarity). For the former, we distinguish the missing data by using relational constraints to reduce the data sparsity. For the later, we incorporate two semantical regularizations into the learning model to encode the semantic similarity. Experimental results show that our approach is better than the methods that consider only explicit facts or only a single knowledge base, and achieves significant accuracy improvements in binary relation prediction.

Keywords

Knowledge base completion Implicit information Relational constraints Semantical regularizations 

Notes

Acknowledgments

This work was supported by the National High Technology R&D Program of China (Grant No.2014AA015102, 2015AA015403), National Natural Science Foundation of China (Grant No.61272344, 61202233, 61370055) and the joint project with IBM Research. Any correspondence please refer to Wenqiang He.

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Peking UniversityBeijingChina

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