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A Comprehensive Survey on Automatic Knowledge Graph Construction

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Zhong, Lingfeng, Wu, Jia, Li, Qian, Peng, Hao, Wu, Xindong·2024·ACM Computing Surveys
DOI 10.1145/3618295
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Abstract

Automatic knowledge graph construction aims at manufacturing structured human knowledge. To this end, much effort has historically been spent extracting informative fact patterns from different data sources. However, more recently, research interest has shifted to acquiring conceptualized structured knowledge beyond informative data. In addition, researchers have also been exploring new ways of handling sophisticated construction tasks in diversified scenarios. Thus, there is a demand for a systematic review of paradigms to organize knowledge structures beyond data-level mentions. To meet this demand, we comprehensively survey more than 300 methods to summarize the latest developments in knowledge graph construction. A knowledge graph is built in three steps: knowledge acquisition, knowledge refinement, and knowledge evolution. The processes of knowledge acquisition are reviewed in detail, including obtaining entities with fine-grained types and their conceptual linkages to knowledge graphs; resolving coreferences; and extracting entity relationships in complex scenarios. The survey covers models for knowledge refinement, including knowledge graph completion, and knowledge fusion. Methods to handle knowledge evolution are also systematically presented, including condition knowledge acquisition, condition knowledge graph completion, and knowledge dynamic. We present the paradigms to compare the distinction among these methods along the axis of the data environment, motivation, and architecture. Additionally, we also provide briefs on accessible resources that can help readers to develop practical knowledge graph systems. The survey concludes with discussions on the challenges and possible directions for future exploration.

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Cited by

See all 382

Metadata

Authors
Zhong, L., Wu, J., Li, Q., Peng, H., & Wu, X.
Venue
ACM Computing Surveys
Publisher
Association for Computing Machinery (ACM)
Published
2023-11-30
Volume
56
Issue
4
Pages
1-62
ISSN
  • 0360-0300 (ISSN-L)
  • 1557-7341
DOI
10.1145/3618295
OpenAlex
W4386443326
Cited by
382
References
273
FWCI
33.56
Type
journal article
Language
en
Open access
bronze
Licence
www.acm.org/publications/policies/copyright_policy#Background (vor)
Funders
  • Australian Research Council (DE200100964, LP210301259, and DP230100899)
  • National Key R&D Program of China (2021YFB1714800)
  • National Natural Science Foundation of China (62120106008)

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: A Comprehensive Survey on Automatic Knowledge Graph Construction. https://app.cochat.ai/papers/10.1145/3618295
The paper
Zhong, L., Wu, J., Li, Q., Peng, H., & Wu, X. (2024). A Comprehensive Survey on Automatic Knowledge Graph Construction. ACM Computing Surveys. https://doi.org/10.1145/3618295

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.