BLINKG: A Benchmark for LLM-Integrated Knowledge Graph Generation

• URL: http://arxiv.org/abs/2605.19518v1
• Date: Tue, 19 May 2026 08:15:53 GMT
• Title: BLINKG: A Benchmark for LLM-Integrated Knowledge Graph Generation
• Abstract summary: Large Language Models (LLMs) can be used to automate the Generating Knowledge Graphs (KGs) process.<n>This paper proposes a benchmark designed to evaluate the mapping capabilities of LLMs in constructing KGs from heterogeneous data sources.
• Score: 3.038521817582494
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Generating Knowledge Graphs (KGs) remains one of the most time-consuming and labor-intensive tasks for knowledge engineers, as they need to identify semantic equivalences between input data sources and ontology terms. While declarative solutions (e.g., RML, SPARQL-Anything) have helped to generalize this process, aligning input schema elements with ontology terms still involves intricate transformations and requires considerable manual effort. With the advent of Large Language Models (LLMs), there is growing interest in leveraging their capabilities to assist KG engineers. Although some studies have explored using LLMs to automate KG construction, there is still no standardized framework for assessing how effectively they establish correspondences between data schemes and ontology concepts. Therefore, in this paper, we propose BLINKG, a benchmark designed to evaluate the mapping capabilities of LLMs in constructing KGs from heterogeneous data sources. The benchmark includes a set of scenarios with increasing complexity, based on real-world use cases. We conduct an extensive experimental evaluation of several stateof-the-art LLMs using BLINK and observe that they already offer promising solutions. However, their performance remains limited in complex scenarios. Thanks to this benchmark, we can already assess the current capabilities of LLMs for KG construction. Additionally, we define a set of requirements for achieving (semi)automated (LLM-driven) KG construction, opening new research lines in this area.

Related papers

• MARS: Multi-hop Adaptive Retrieval and SPARQL Generation for KGQA [5.340503366346383]
  Large language models (LLMs) have demonstrated strong reasoning performance, but their tendency to hallucinate limits their reliability in knowledge-intensive tasks.<n>We propose MARS, a scalable knowledge graph question answering (KGQA) approach that requires no model fine-tuning.
  arXiv  Detail & Related papers  (2026-07-16T04:40:05Z)
• KG2Code: Bridging Knowledge Graphs and Large Language Models via Executable Code for Question Answering [64.63418702318029]
  KG2Code is a novel approach that transforms knowledge graphs into a code-based representation.<n> KG2Code-QA is introduced as a KGQA framework that formulates KGQA as a code generation task.
  arXiv  Detail & Related papers  (2026-06-26T11:27:55Z)
• A Human-in-the-Loop, LLM-Centered Architecture for Knowledge-Graph Question Answering [41.99844472131922]
  Large Language Models excel at language understanding but are limited in knowledge-intensive domains.<n>This work introduces an interactive framework in which LLMs generate and explain Cypher graph queries.
  arXiv  Detail & Related papers  (2026-02-05T10:10:19Z)
• Enrich-on-Graph: Query-Graph Alignment for Complex Reasoning with LLM Enriching [61.824094419641575]
  Large Language Models (LLMs) struggle with hallucinations and factual errors in knowledge-intensive scenarios like knowledge graph question answering (KGQA)<n>We attribute this to the semantic gap between structured knowledge graphs (KGs) and unstructured queries, caused by inherent differences in their focuses and structures.<n>Existing methods usually employ resource-intensive, non-scalable reasoning on vanilla KGs, but overlook this gap.<n>We propose a flexible framework, Enrich-on-Graph (EoG), which leverages LLMs' prior knowledge to enrich KGs, bridge the semantic gap between graphs and queries.
  arXiv  Detail & Related papers  (2025-09-25T06:48:52Z)
• Enhancing Manufacturing Knowledge Access with LLMs and Context-aware Prompting [9.520082987178851]
  Large Language Models (LLMs) can automatically translate natural language queries into the SPARQL format.<n>We evaluate strategies that use LLMs as mediators to facilitate information retrieval from Knowledge graphs (KGs)<n>Our findings show that LLMs can significantly improve their performance on generating correct and complete queries when provided only the adequate context of the KG schema.
  arXiv  Detail & Related papers  (2025-07-30T12:39:01Z)
• Beyond Isolated Dots: Benchmarking Structured Table Construction as Deep Knowledge Extraction [80.88654868264645]
  Arranged and Organized Extraction Benchmark designed to evaluate ability of large language models to comprehend fragmented documents.<n>AOE includes 11 carefully crafted tasks across three diverse domains, requiring models to generate context-specific schema tailored to varied input queries.<n>Results show that even the most advanced models struggled significantly.
  arXiv  Detail & Related papers  (2025-07-22T06:37:51Z)
• LightPROF: A Lightweight Reasoning Framework for Large Language Model on Knowledge Graph [57.382255728234064]
  Large Language Models (LLMs) have impressive capabilities in text understanding and zero-shot reasoning.<n> Knowledge Graphs (KGs) provide rich and reliable contextual information for the reasoning process of LLMs.<n>We propose a novel Lightweight and efficient Prompt learning-ReasOning Framework for KGQA (LightPROF)
  arXiv  Detail & Related papers  (2025-04-04T03:03:47Z)
• Leveraging Large Language Models for Semantic Query Processing in a Scholarly Knowledge Graph [1.7418328181959968]
  The proposed research aims to develop an innovative semantic query processing system. It enables users to obtain comprehensive information about research works produced by Computer Science (CS) researchers at the Australian National University.
  arXiv  Detail & Related papers  (2024-05-24T09:19:45Z)
• Generate-on-Graph: Treat LLM as both Agent and KG in Incomplete Knowledge Graph Question Answering [87.67177556994525]
  We propose a training-free method called Generate-on-Graph (GoG) to generate new factual triples while exploring Knowledge Graphs (KGs) GoG performs reasoning through a Thinking-Searching-Generating framework, which treats LLM as both Agent and KG in IKGQA.
  arXiv  Detail & Related papers  (2024-04-23T04:47:22Z)
• From human experts to machines: An LLM supported approach to ontology and knowledge graph construction [0.0]
  Large Language Models (LLMs) have recently gained popularity for their ability to understand and generate human-like natural language. This work explores the (semi-)automatic construction of KGs facilitated by open-source LLMs.
  arXiv  Detail & Related papers  (2024-03-13T08:50:15Z)
• KG-Agent: An Efficient Autonomous Agent Framework for Complex Reasoning over Knowledge Graph [134.8631016845467]
  We propose an autonomous LLM-based agent framework, called KG-Agent. In KG-Agent, we integrate the LLM, multifunctional toolbox, KG-based executor, and knowledge memory. To guarantee the effectiveness, we leverage program language to formulate the multi-hop reasoning process over the KG.
  arXiv  Detail & Related papers  (2024-02-17T02:07:49Z)
• ML-Bench: Evaluating Large Language Models and Agents for Machine Learning Tasks on Repository-Level Code [76.84199699772903]
  ML-Bench is a benchmark rooted in real-world programming applications that leverage existing code repositories to perform tasks. To evaluate both Large Language Models (LLMs) and AI agents, two setups are employed: ML-LLM-Bench for assessing LLMs' text-to-code conversion within a predefined deployment environment, and ML-Agent-Bench for testing autonomous agents in an end-to-end task execution within a Linux sandbox environment.
  arXiv  Detail & Related papers  (2023-11-16T12:03:21Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.