Complex Logical Reasoning over Knowledge Graphs using Large Language Models

• URL: http://arxiv.org/abs/2305.01157v3
• Date: Sun, 31 Mar 2024 19:56:37 GMT
• Title: Complex Logical Reasoning over Knowledge Graphs using Large Language Models
• Authors: Nurendra Choudhary, Chandan K. Reddy,
• Abstract summary: Reasoning over knowledge graphs (KGs) is a challenging task that requires a deep understanding of the relationships between entities. Current approaches rely on learning geometries to embed entities in vector space for logical query operations. We propose a novel decoupled approach, Language-guided Abstract Reasoning over Knowledge graphs (LARK), that formulates complex KG reasoning as a combination of contextual KG search and logical query reasoning.
• Score: 13.594992599230277
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Reasoning over knowledge graphs (KGs) is a challenging task that requires a deep understanding of the complex relationships between entities and the underlying logic of their relations. Current approaches rely on learning geometries to embed entities in vector space for logical query operations, but they suffer from subpar performance on complex queries and dataset-specific representations. In this paper, we propose a novel decoupled approach, Language-guided Abstract Reasoning over Knowledge graphs (LARK), that formulates complex KG reasoning as a combination of contextual KG search and logical query reasoning, to leverage the strengths of graph extraction algorithms and large language models (LLM), respectively. Our experiments demonstrate that the proposed approach outperforms state-of-the-art KG reasoning methods on standard benchmark datasets across several logical query constructs, with significant performance gain for queries of higher complexity. Furthermore, we show that the performance of our approach improves proportionally to the increase in size of the underlying LLM, enabling the integration of the latest advancements in LLMs for logical reasoning over KGs. Our work presents a new direction for addressing the challenges of complex KG reasoning and paves the way for future research in this area.

Related papers

• Causal Reasoning in Large Language Models: A Knowledge Graph Approach [6.5344638992876085]
  Large language models (LLMs) typically improve performance by either retrieving semantically similar information, or enhancing reasoning abilities through structured prompts like chain-of-thought. This paper proposes a knowledge graph (KG)-based random-walk reasoning approach that leverages causal relationships.
  arXiv  Detail & Related papers  (2024-10-15T13:24:44Z)
• GIVE: Structured Reasoning with Knowledge Graph Inspired Veracity Extrapolation [108.2008975785364]
  Graph Inspired Veracity Extrapolation (GIVE) is a novel reasoning framework that integrates the parametric and non-parametric memories. Our method facilitates a more logical and step-wise reasoning approach akin to experts' problem-solving, rather than gold answer retrieval.
  arXiv  Detail & Related papers  (2024-10-11T03:05:06Z)
• KG-RAG: Bridging the Gap Between Knowledge and Creativity [0.0]
  Large Language Model Agents (LMAs) face issues such as information hallucinations, catastrophic forgetting, and limitations in processing long contexts. This paper introduces a KG-RAG (Knowledge Graph-Retrieval Augmented Generation) pipeline to enhance the knowledge capabilities of LMAs. Preliminary experiments on the ComplexWebQuestions dataset demonstrate notable improvements in the reduction of hallucinated content.
  arXiv  Detail & Related papers  (2024-05-20T14:03:05Z)
• Improving Complex Reasoning over Knowledge Graph with Logic-Aware Curriculum Tuning [89.89857766491475]
  We propose a complex reasoning schema over KG upon large language models (LLMs) We augment the arbitrary first-order logical queries via binary tree decomposition to stimulate the reasoning capability of LLMs. Experiments across widely used datasets demonstrate that LACT has substantial improvements(brings an average +5.5% MRR score) over advanced methods.
  arXiv  Detail & Related papers  (2024-05-02T18:12:08Z)
• When Do Program-of-Thoughts Work for Reasoning? [51.2699797837818]
  We propose complexity-impacted reasoning score (CIRS) to measure correlation between code and reasoning abilities. Specifically, we use the abstract syntax tree to encode the structural information and calculate logical complexity. Code will be integrated into the EasyInstruct framework at https://github.com/zjunlp/EasyInstruct.
  arXiv  Detail & Related papers  (2023-08-29T17:22:39Z)
• Modeling Hierarchical Reasoning Chains by Linking Discourse Units and Key Phrases for Reading Comprehension [80.99865844249106]
  We propose a holistic graph network (HGN) which deals with context at both discourse level and word level, as the basis for logical reasoning. Specifically, node-level and type-level relations, which can be interpreted as bridges in the reasoning process, are modeled by a hierarchical interaction mechanism.
  arXiv  Detail & Related papers  (2023-06-21T07:34:27Z)
• Query Structure Modeling for Inductive Logical Reasoning Over Knowledge Graphs [67.043747188954]
  We propose a structure-modeled textual encoding framework for inductive logical reasoning over KGs. It encodes linearized query structures and entities using pre-trained language models to find answers. We conduct experiments on two inductive logical reasoning datasets and three transductive datasets.
  arXiv  Detail & Related papers  (2023-05-23T01:25:29Z)
• Logical Message Passing Networks with One-hop Inference on Atomic Formulas [57.47174363091452]
  We propose a framework for complex query answering that decomposes the Knowledge Graph embeddings from neural set operators. On top of the query graph, we propose the Logical Message Passing Neural Network (LMPNN) that connects the local one-hop inferences on atomic formulas to the global logical reasoning. Our approach yields the new state-of-the-art neural CQA model.
  arXiv  Detail & Related papers  (2023-01-21T02:34:06Z)

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.