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Knowledge Graphs and Their Reciprocal Relationship with Large Language Models

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Dehal, Ramandeep Singh, Sharma, Mehak, Rajabi, Enayat·2025·Machine Learning and Knowledge Extraction
DOI 10.3390/make7020038
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Abstract

The reciprocal relationship between Large Language Models (LLMs) and Knowledge Graphs (KGs) highlights their synergistic potential in enhancing artificial intelligence (AI) applications. LLMs, with their natural language understanding and generative capabilities, support the automation of KG construction through entity recognition, relation extraction, and schema generation. Conversely, KGs serve as structured and interpretable data sources that improve the transparency, factual consistency and reliability of LLM-based applications, mitigating challenges such as hallucinations and lack of explainability. This study conducts a systematic literature review of 77 studies to examine AI methodologies supporting LLM–KG integration, including symbolic AI, machine learning, and hybrid approaches. The research explores diverse applications spanning healthcare, finance, justice, and industrial automation, revealing the transformative potential of this synergy. Through in-depth analysis, this study identifies key limitations in current approaches, including challenges in scalability with maintaining dynamic and real-time Knowledge Graphs, difficulty in adapting general-purpose LLMs to specialized domains, limited explainability in tracing model outputs to interpretable reasoning, and ethical concerns surrounding bias, fairness, and transparency. In response, the study highlights potential strategies to optimize LLM–KG synergy. The findings from this study provide actionable insights for researchers and practitioners aiming for robust, transparent, and adaptive AI systems to enhance knowledge-driven AI applications through LLM–KG integration, further advancing generative AI and explainable AI (XAI) applications.

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  • Structured data integration significantly enhances LLM capabilities across multiple dimensions, with knowledge graphs providing structured information that reduces hallucinations by grounding responses in verifiable facts and improving factual accuracy through clearly defined information sources.

    “Structured data integration significantly enhances LLM capabilities across multiple dimensions, with knowledge graphs providing structured information that reduces hallucinations by grounding responses in verifiable facts and improving factual accuracy through clearly defined information sources [1010, 1352, 204, 571].”

    From A Survey of Context Engineering for Large Language Models (2025) · cites this paper as [204]

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Cited by

Sorted by citations

Authors
Dehal, R. S., Sharma, M., & Rajabi, E.
Affiliations
Cape Breton University (CA)
Corresponding author
Rajabi, Enayat
ORCID
Venue
Machine Learning and Knowledge Extraction
Publisher
MDPI AG
Published
2025-04-21
Volume
7
Issue
2
Pages
38
ISSN
2504-4990 (ISSN-L)
DOI
10.3390/make7020038
OpenAlex
W4409634738
Cited by
41
References
5
Citations by year
2026: 31 · 2025: 10
FWCI
16.58
Type
journal article
Language
en
Open access
Licence
Locations
  • Machine Learning and Knowledge Extraction (publishedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
NSERC (Natural Sciences and Engineering Research Council) Discovery Grant (RGPIN-2020-05869)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
  • Physical Sciences › Computer Science › Artificial Intelligence › Advanced Graph Neural Networks
  • Physical Sciences › Computer Science › Artificial Intelligence › Semantic Web and Ontologies
Keywords
knowledge graphs, large language models, knowledge graph construction, entity recognition, relation extraction, schema generation, hallucination mitigation, explainable AI, interpretability, domain adaptation, bias and fairness, medical applications, financial applications, industrial automation

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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.

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