Text2KGBench: A Benchmark for Ontology-Driven Knowledge Graph Generation from Text
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Abstract (arXiv)
The recent advances in large language models (LLM) and foundation models with emergent capabilities have been shown to improve the performance of many NLP tasks. LLMs and Knowledge Graphs (KG) can complement each other such that LLMs can be used for KG construction or completion while existing KGs can be used for different tasks such as making LLM outputs explainable or fact-checking in Neuro-Symbolic manner. In this paper, we present Text2KGBench, a benchmark to evaluate the capabilities of language models to generate KGs from natural language text guided by an ontology. Given an input ontology and a set of sentences, the task is to extract facts from the text while complying with the given ontology (concepts, relations, domain/range constraints) and being faithful to the input sentences. We provide two datasets (i) Wikidata-TekGen with 10 ontologies and 13,474 sentences and (ii) DBpedia-WebNLG with 19 ontologies and 4,860 sentences. We define seven evaluation metrics to measure fact extraction performance, ontology conformance, and hallucinations by LLMs. Furthermore, we provide results for two baseline models, Vicuna-13B and Alpaca-LoRA-13B using automatic prompt generation from test cases. The baseline results show that there is room for improvement using both Semantic Web and Natural Language Processing techniques.
Key facts. Cited by 48 papers in the map (data through 14 Oct 2026). Builds on 36 of its 55 references that are in the map. First posted on arXiv 4 Aug 2023.
Builds on
36 of 55 references.
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Citing papers per year: 2023: 4; 2024: 13; 2025: 16; 2026: 15.
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- 2026Integrating Graphs, Large Language Models, and Agents: Reasoning and Retrieval17 Apr 2026
- 2024Science Checker Reloaded: A Bidirectional Paradigm for Transparency and Logical Reasoning21 Feb 2024
- 2023Cognitive Mirage: A Review of Hallucinations in Large Language Modelscited by 84
- 2023Symbol-LLM: Towards Foundational Symbol-centric Interface For Large Language Modelscited by 39
- 2024iText2KG: Incremental Knowledge Graphs Construction Using Large Language Modelscited by 11
- 2023Insights into Classifying and Mitigating LLMs' Hallucinationscited by 6
- 2024Way to Specialist: Closing Loop Between Specialized LLM and Evolving Domain Knowledge Graphcited by 6
- 2025Automatic Prompt Optimization for Knowledge Graph Construction: Insights from an Empirical Studycited by 6
- 2024KGValidator: A Framework for Automatic Validation of Knowledge Graph Constructioncited by 5
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- 2024Human Evaluation of Procedural Knowledge Graph Extraction from Text with Large Language Modelscited by 4
- 2025Ontology-Guided, Hybrid Prompt Learning for Generalization in Knowledge Graph Question Answeringcited by 3
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12 most cited of 38 All 38 (JSON)
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- 2026Propagating construction-time knowledge quality into medical question answering: A framework grounded in clinical guidelines28 Aug 2026
- 2026A Grounded and Decomposed Framework for Relation-Level Hallucination Evaluation in Abstractive Summarization8 Aug 2026
- 2026Reversing Arrows in Large Language Models4 Aug 2026
- 2026Evaluation of Pipelines for Data Integration into Knowledge Graphs21 May 2026
- 2026Tacit Knowledge Extraction via Logic Augmented Generation and Active Inference8 May 2026
- 2026A Unified Benchmark for Evaluating Knowledge Graph Construction Methods and Graph Neural Networks6 May 2026
- 2026A Benchmark for Gap and Overlap Analysis as a Test of KG Task Readiness12 Apr 2026
- 2026Beyond Predefined Schemas: TRACE-KG for Context-Enriched Knowledge Graph Generation3 Apr 2026
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