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Domain Specific Question Answering Over Knowledge Graphs Using Logical Programming and Large Language Models

arXiv 2303.02206 ↗First posted 3 Mar 2023cs.LG cs.CLcited by 0

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Abstract (arXiv)

Answering questions over domain-specific graphs requires a tailored approach due to the limited number of relations and the specific nature of the domain. Our approach integrates classic logical programming languages into large language models (LLMs), enabling the utilization of logical reasoning capabilities to tackle the KGQA task. By representing the questions as Prolog queries, which are readable and near close to natural language in representation, we facilitate the generation of programmatically derived answers. To validate the effectiveness of our approach, we evaluate it using a well-known benchmark dataset, MetaQA. Our experimental results demonstrate that our method achieves accurate identification of correct answer entities for all test questions, even when trained on a small fraction of annotated data. Overall, our work presents a promising approach to addressing question answering over domain-specific graphs, offering an explainable and robust solution by incorporating logical programming languages.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 11 of its 22 references that are in the map. First posted on arXiv 3 Mar 2023.

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References · 11

  1. 2017Attention Is All You Needcited by 59,932
  2. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,491
  3. 2014Embedding Entities and Relations for Learning and Inference in Knowledge Basescited by 929
  4. 2017Convolutional 2D Knowledge Graph Embeddingscited by 729
  5. 2019TuckER: Tensor Factorization for Knowledge Graph Completioncited by 290
  6. 2019PullNet: Open Domain Question Answering with Iterative Retrieval on Knowledge Bases and Textcited by 182
  7. 2017Variational Reasoning for Question Answering with Knowledge Graphcited by 170
  8. 2018Open Domain Question Answering Using Early Fusion of Knowledge Bases and Textcited by 167
  9. 2021Improving Multi-hop Knowledge Base Question Answering by Learning Intermediate Supervision Signalscited by 100
  10. 2021TransferNet: An Effective and Transparent Framework for Multi-hop Question Answering over Relation Graphcited by 60
  11. 2019InteractE: Improving Convolution-based Knowledge Graph Embeddings by Increasing Feature Interactionscited by 58

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  2. 2024FiDeLiS: Faithful Reasoning in Large Language Model for Knowledge Graph Question Answering
  3. 2026Constrained Entity Selection under Partial Knowledge for LLM-Based Knowledge Graph QA
  4. 2024Right for Right Reasons: Large Language Models for Verifiable Commonsense Knowledge Graph Question Answering
  5. 2026From Chains to Graphs: Self-Structured Reasoning for General-Domain LLMs
  6. 2025The Role of Exploration Modules in Small Language Models for Knowledge Graph Question Answering
  7. 2024CR-LT-KGQA: A Knowledge Graph Question Answering Dataset Requiring Commonsense Reasoning and Long-Tail Knowledge
  8. 2024Decoding on Graphs: Faithful and Sound Reasoning on Knowledge Graphs through Generation of Well-Formed Chains