Ontology-Guided Reverse Thinking Makes Large Language Models Stronger on Knowledge Graph Question Answering

• URL: http://arxiv.org/abs/2502.11491v1
• Date: Mon, 17 Feb 2025 06:53:15 GMT
• Title: Ontology-Guided Reverse Thinking Makes Large Language Models Stronger on Knowledge Graph Question Answering
• Authors: Runxuan Liu, Bei Luo, Jiaqi Li, Baoxin Wang, Ming Liu, Dayong Wu, Shijin Wang, Bing Qin,
• Abstract summary: Ontology-Guided Reverse Thinking (ORT) is a novel framework that constructs reasoning paths from purposes back to conditions. ORT operates in three key phases: (1) using LLM to extract purpose labels and condition labels, (2) constructing label reasoning paths based on the KG ontology, and (3) using the label reasoning paths to guide knowledge retrieval. Experiments on the WebQSP and CWQ datasets show that ORT achieves state-of-the-art performance and significantly enhances the capability of LLMs for KGQA.
• Score: 24.821273655754222
• License:
• Abstract: Large language models (LLMs) have shown remarkable capabilities in natural language processing. However, in knowledge graph question answering tasks (KGQA), there remains the issue of answering questions that require multi-hop reasoning. Existing methods rely on entity vector matching, but the purpose of the question is abstract and difficult to match with specific entities. As a result, it is difficult to establish reasoning paths to the purpose, which leads to information loss and redundancy. To address this issue, inspired by human reverse thinking, we propose Ontology-Guided Reverse Thinking (ORT), a novel framework that constructs reasoning paths from purposes back to conditions. ORT operates in three key phases: (1) using LLM to extract purpose labels and condition labels, (2) constructing label reasoning paths based on the KG ontology, and (3) using the label reasoning paths to guide knowledge retrieval. Experiments on the WebQSP and CWQ datasets show that ORT achieves state-of-the-art performance and significantly enhances the capability of LLMs for KGQA.

Related papers

• Harnessing Large Language Models for Knowledge Graph Question Answering via Adaptive Multi-Aspect Retrieval-Augmentation [81.18701211912779]
  We introduce an Adaptive Multi-Aspect Retrieval-augmented over KGs (Amar) framework. This method retrieves knowledge including entities, relations, and subgraphs, and converts each piece of retrieved text into prompt embeddings. Our method has achieved state-of-the-art performance on two common datasets.
  arXiv  Detail & Related papers  (2024-12-24T16:38:04Z)
• Path-of-Thoughts: Extracting and Following Paths for Robust Relational Reasoning with Large Language Models [62.12031550252253]
  We present Path-of-Thoughts (PoT), a novel framework designed to tackle relation reasoning. PoT efficiently extracts a task-agnostic graph that identifies crucial entities, relations, and attributes within the problem context. PoT identifies relevant reasoning chains within the graph corresponding to the posed question, facilitating inference of potential answers.
  arXiv  Detail & Related papers  (2024-12-23T20:27:12Z)
• Debate on Graph: a Flexible and Reliable Reasoning Framework for Large Language Models [33.662269036173456]
  Large Language Models (LLMs) may suffer from hallucinations in real-world applications due to the lack of relevant knowledge. Knowledge Graph Question Answering (KGQA) serves as a critical touchstone for the integration. We propose an interactive KGQA framework that leverages the interactive learning capabilities of LLMs to perform reasoning and Debating over Graphs (DoG)
  arXiv  Detail & Related papers  (2024-09-05T01:11:58Z)
• Logic Query of Thoughts: Guiding Large Language Models to Answer Complex Logic Queries with Knowledge Graphs [102.37496443389203]
  'Logic-Query-of-Thoughts' (LGOT) is the first of its kind to combine knowledge graph reasoning and large language models. Our experimental findings demonstrate substantial performance enhancements, with up to 20% improvement over ChatGPT.
  arXiv  Detail & Related papers  (2024-03-17T17:01:45Z)
• Right for Right Reasons: Large Language Models for Verifiable Commonsense Knowledge Graph Question Answering [18.48602809114524]
  Knowledge Graph Question Answering (KGQA) methods seek to answer Natural Language questions using the relational information stored in Knowledge Graphs (KGs) With the recent advancements of Large Language Models (LLMs) and their remarkable reasoning abilities, there is a growing trend to leverage them for KGQA. We propose Right for Right Reasons (R3), a commonsense KGQA methodology that allows for a verifiable reasoning procedure.
  arXiv  Detail & Related papers  (2024-03-03T04:22:13Z)
• keqing: knowledge-based question answering is a nature chain-of-thought mentor of LLM [27.76205400533089]
  Large language models (LLMs) have exhibited remarkable performance on various natural language processing (NLP) tasks, especially for question answering. We present a novel framework to assist LLMs, such as ChatGPT, to retrieve question-related structured information on the knowledge graph. The experimental results on KBQA datasets show that Keqing can achieve competitive performance and illustrate the logic of answering each question.
  arXiv  Detail & Related papers  (2023-12-31T08:39:04Z)
• Probabilistic Tree-of-thought Reasoning for Answering Knowledge-intensive Complex Questions [93.40614719648386]
  Large language models (LLMs) are capable of answering knowledge-intensive complex questions with chain-of-thought (CoT) reasoning. Recent works turn to retrieving external knowledge to augment CoT reasoning. We propose a novel approach: Probabilistic Tree-of-thought Reasoning (ProbTree)
  arXiv  Detail & Related papers  (2023-11-23T12:52:37Z)
• DIVKNOWQA: Assessing the Reasoning Ability of LLMs via Open-Domain Question Answering over Knowledge Base and Text [73.68051228972024]
  Large Language Models (LLMs) have exhibited impressive generation capabilities, but they suffer from hallucinations when relying on their internal knowledge. Retrieval-augmented LLMs have emerged as a potential solution to ground LLMs in external knowledge.
  arXiv  Detail & Related papers  (2023-10-31T04:37:57Z)
• UniKGQA: Unified Retrieval and Reasoning for Solving Multi-hop Question Answering Over Knowledge Graph [89.98762327725112]
  Multi-hop Question Answering over Knowledge Graph(KGQA) aims to find the answer entities that are multiple hops away from the topic entities mentioned in a natural language question. We propose UniKGQA, a novel approach for multi-hop KGQA task, by unifying retrieval and reasoning in both model architecture and parameter learning.
  arXiv  Detail & Related papers  (2022-12-02T04:08:09Z)

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.