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Knowledge Graph Curation: A Practical Framework

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Dieter FenselAuthors Info & Claims

IJCKG '21: Proceedings of the 10th International Joint Conference on Knowledge Graphs

Pages 166 - 171

https://doi.org/10.1145/3502223.3502247

Published: 24 January 2022 Publication History

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Abstract

Knowledge Graphs (KGs) have shown to be very important for applications such as personal assistants, question-answering systems, and search engines. Therefore, it is crucial to ensure their high quality. However, KGs inevitably contain errors, duplicates, and missing values, which may hinder their adoption and utility in business applications, as they are not curated, e.g., low-quality KGs produce low-quality applications that are built on top of them. In this vision paper, we propose a practical knowledge graph curation framework for improving the quality of KGs. First, we define a set of quality metrics for assessing the status of KGs, Second, we describe the verification and validation of KGs as cleaning tasks, Third, we present duplicate detection and knowledge fusion strategies for enriching KGs. Furthermore, we give insights and directions toward a better architecture for curating KGs.

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Index Terms

Knowledge Graph Curation: A Practical Framework
1. Information systems
  1. Information retrieval
  2. Information systems applications

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IJCKG '21: Proceedings of the 10th International Joint Conference on Knowledge Graphs

December 2021

204 pages

ISBN:9781450395656

DOI:10.1145/3502223

Copyright © 2021 ACM.

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Published: 24 January 2022

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IJCKG'21: The 10th International Joint Conference on Knowledge Graphs

December 6 - 8, 2021

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Aravind Krishnan ADeepak GSanthanavijayan AVenugopal K(2025)CKGLD: Curating Knowledge Graph for Linked Open Data Generation for Cyber Law and EthicsEmerging Trends and Technologies on Intelligent Systems10.1007/978-981-97-5703-9_31(363-371)Online publication date: 28-Feb-2025
https://doi.org/10.1007/978-981-97-5703-9_31
Mao WShen JSu QLiu SPirasteh SIshii K(2024)A Method for Constructing an Urban Waterlogging Emergency Knowledge Graph Based on Spatiotemporal ProcessesISPRS International Journal of Geo-Information10.3390/ijgi1310034913:10(349)Online publication date: 3-Oct-2024
https://doi.org/10.3390/ijgi13100349
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Schatz KKorn DTropsha AChirkova R(2022)Workflow for Domain- and Task-Sensitive Curation of Knowledge Graphs, with Use Case of DRKG2022 IEEE International Conference on Big Data (Big Data)10.1109/BigData55660.2022.10020536(3692-3701)Online publication date: 17-Dec-2022
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