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Cited by 717Advanced Graph Neural NetworksSemantic Web and OntologiesTopic Modeling

Knowledge Graphs: Opportunities and Challenges

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Peng, Ciyuan, Xia, Feng, Naseriparsa, Mehdi, Osborne, Francesco·2023·Artificial Intelligence Review
DOI 10.1007/s10462-023-10465-9PMC PMC10068207
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Abstract

With the explosive growth of artificial intelligence (AI) and big data, it has become vitally important to organize and represent the enormous volume of knowledge appropriately. As graph data, knowledge graphs accumulate and convey knowledge of the real world. It has been well-recognized that knowledge graphs effectively represent complex information; hence, they rapidly gain the attention of academia and industry in recent years. Thus to develop a deeper understanding of knowledge graphs, this paper presents a systematic overview of this field. Specifically, we focus on the opportunities and challenges of knowledge graphs. We first review the opportunities of knowledge graphs in terms of two aspects: (1) AI systems built upon knowledge graphs; (2) potential application fields of knowledge graphs. Then, we thoroughly discuss severe technical challenges in this field, such as knowledge graph embeddings, knowledge acquisition, knowledge graph completion, knowledge fusion, and knowledge reasoning. We expect that this survey will shed new light on future research and the development of knowledge graphs.

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  • Knowledge graphs offer a structured alternative in which information is represented as entities and the relations between them.

    “recent work has explored structured memory representations based on knowledge graphs, where information is organized as entities and their relationships rather than independent embeddings.”

    From Selective Forgetting: A Graph-Based Memory Framework for Long-Term LLM Agents (2026) · cites this paper as Peng et al. [2023]

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Sorted by citations

Authors
Peng, C., Xia, F., Naseriparsa, M., & Osborne, F.
Affiliations
  • Federation University (AU)
  • RMIT University (AU)
  • The Open University (GB)
Corresponding author
Xia, Feng
ORCID
Venue
Artificial Intelligence Review
Publisher
Springer Science and Business Media LLC
Published
2023-04-03
Volume
56
Issue
11
Pages
13071-13102
ISSN
  • 0269-2821 (ISSN-L)
  • 1573-7462
DOI
10.1007/s10462-023-10465-9
PMC
PMC10068207
OpenAlex
W4362579589
Cited by
717
References
6
Citations by year
2026: 228 · 2025: 293 · 2024: 168 · 2023: 28
FWCI
67.59
Type
journal article
Language
en
Open access
Licence
Locations
  • Artificial Intelligence Review (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • arXiv (Cornell University) (submittedVersion, public-domain)
  • FedUni ResearchOnline (Federation University Australia) (submittedVersion, cc-by)
  • BOA (University of Milano-Bicocca) (submittedVersion, cc-by)
  • Open Research Online (The Open University) (submittedVersion, cc-by)
  • Figshare (submittedVersion, cc-by)
  • arXiv (Cornell University) (public-domain)
Funders
Royal Melbourne Institute of Technology
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Advanced Graph Neural Networks
  • Physical Sciences › Computer Science › Artificial Intelligence › Semantic Web and Ontologies
  • Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
Keywords
knowledge graphs, knowledge graph embedding, knowledge acquisition, knowledge graph completion, knowledge fusion, knowledge reasoning, graph data, knowledge graph applications

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 ›

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