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Subgraph Retrieval Enhanced Model for Multi-hop Knowledge Base Question Answering
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Recent works on knowledge base question answering (KBQA) retrieve subgraphs for easier reasoning. A desired subgraph is crucial as a small one may exclude the answer but a large one might introduce more noises. However, the existing retrieval is either heuristic or interwoven with the reasoning, causing reasoning on the partial subgraphs, which increases the reasoning bias when the intermediate supervision is missing. This paper proposes a trainable subgraph retriever (SR) decoupled from the subsequent reasoning process, which enables a plug-and-play framework to enhance any subgraph-oriented KBQA model. Extensive experiments demonstrate SR achieves significantly better retrieval and QA performance than existing retrieval methods. Via weakly supervised pre-training as well as the end-to-end fine-tuning, SRl achieves new state-of-the-art performance when combined with NSM, a subgraph-oriented reasoner, for embedding-based KBQA methods.
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Cited by 4 Pith papers
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NeuroSymActive: Differentiable Neural-Symbolic Reasoning with Active Exploration for Knowledge Graph Question Answering
NeuroSymActive combines soft-unification symbolic modules, a neural path evaluator, and Monte-Carlo-style active exploration to reach strong answer accuracy on KGQA benchmarks while cutting graph lookups and model cal...
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Weak-to-Strong GraphRAG: Aligning Weak Retrievers with Large Language Models for Graph-based Retrieval Augmented Generation
ReG refines weak graph-retriever supervision with LLM-selected reasoning chains and reorganizes retrieved triples into coherent evidence chains, improving KGQA accuracy, data efficiency, and reasoning token efficiency.
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Search-on-Graph: Iterative Informed Navigation for Large Language Model Reasoning on Knowledge Graphs
An LLM that iteratively inspects 1-hop neighbors of a knowledge-graph entity and chooses the next relation achieves state-of-the-art KGQA scores on six Freebase/Wikidata benchmarks without fine-tuning.
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Optimizing the Interface Between Knowledge Graphs and LLMs for Complex Reasoning
Hyperparameter tuning of Cognee's knowledge graph pipeline yields consistent but uneven gains across three multi-hop QA benchmarks, with best configurations varying by dataset and metric.
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