Optimizing knowledge graphs through voting-based user feedback

Ruida Yang, Xin Lin*, Jianliang XU, Yan Yang, Liang He

*Corresponding author for this work

Research output: Chapter in book/report/conference proceedingConference contributionpeer-review

Abstract

Knowledge graphs have been used in a wide range of applications to support search, recommendation, and question answering (Q&A). For example, in Q&A systems, given a new question, we may use a knowledge graph to automatically identify the most suitable answers based on similarity evaluation. However, such systems may suffer from two major limitations. First, the knowledge graph constructed based on source data may contain errors. Second, the knowledge graph may become out of date and cannot quickly adapt to new knowledge. To address these issues, in this paper, we propose an interactive framework that refines and optimizes knowledge graphs through user votes. We develop an efficient similarity evaluation notion, called extended inverse P-distance, based on which the graph optimization problem can be formulated as a signomial geometric programming problem. We then propose a basic single-vote solution and a more advanced multi-vote solution for graph optimization. We also propose a split-and-merge optimization strategy to scale up the multi-vote solution. Extensive experiments based on real-life and synthetic graphs demonstrate the effectiveness and efficiency of our proposed framework.

Original languageEnglish
Title of host publicationProceedings - 2020 IEEE 36th International Conference on Data Engineering, ICDE 2020
PublisherIEEE Computer Society
Pages421-432
Number of pages12
ISBN (Electronic)9781728129037
DOIs
Publication statusPublished - Apr 2020
Event36th IEEE International Conference on Data Engineering, ICDE 2020 - Dallas, United States
Duration: 20 Apr 202024 Apr 2020

Publication series

NameProceedings - International Conference on Data Engineering
Volume2020-April
ISSN (Print)1084-4627

Conference

Conference36th IEEE International Conference on Data Engineering, ICDE 2020
Country/TerritoryUnited States
CityDallas
Period20/04/2024/04/20

Scopus Subject Areas

  • Software
  • Signal Processing
  • Information Systems

User-Defined Keywords

  • Data Cleaning
  • Knowledge Graphs
  • Query Processing
  • Question Answering

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