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PullNet: Open Domain Question Answering with Iterative Retrieval on Knowledge Bases and Text
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We consider open-domain queston answering (QA) where answers are drawn from either a corpus, a knowledge base (KB), or a combination of both of these. We focus on a setting in which a corpus is supplemented with a large but incomplete KB, and on questions that require non-trivial (e.g., ``multi-hop'') reasoning. We describe PullNet, an integrated framework for (1) learning what to retrieve (from the KB and/or corpus) and (2) reasoning with this heterogeneous information to find the best answer. PullNet uses an {iterative} process to construct a question-specific subgraph that contains information relevant to the question. In each iteration, a graph convolutional network (graph CNN) is used to identify subgraph nodes that should be expanded using retrieval (or ``pull'') operations on the corpus and/or KB. After the subgraph is complete, a similar graph CNN is used to extract the answer from the subgraph. This retrieve-and-reason process allows us to answer multi-hop questions using large KBs and corpora. PullNet is weakly supervised, requiring question-answer pairs but not gold inference paths. Experimentally PullNet improves over the prior state-of-the art, and in the setting where a corpus is used with incomplete KB these improvements are often dramatic. PullNet is also often superior to prior systems in a KB-only setting or a text-only setting.
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Cited by 7 Pith papers
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RTSoG combines question decomposition, LLM-reward-guided Monte Carlo Tree Search with a self-critic stop signal, and weighted path stacking to achieve new state-of-the-art KGQA accuracy, though without code or error bars.
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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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GOSU: Retrieval-Augmented Generation with Global-Level Optimized Semantic Unit-Centric Framework
GOSU globally merges semantic units from text chunks into a unit-centric knowledge graph and uses three-tier keyword retrieval to improve RAG generation quality, according to LLM-judge win rates.
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DTKG: Dual-Track Knowledge Graph-Verified Reasoning Framework for Multi-Hop QA
DTKG routes multi-hop questions into either a fact-verification or knowledge-graph chain-reasoning branch, reporting modest accuracy gains on four QA benchmarks.
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Explainable Knowledge Graph Retrieval-Augmented Generation (KG-RAG) with KG-SMILE
KG-SMILE applies perturbation and linear regression to a knowledge graph to attribute which entities and relations drive a GraphRAG system's answers.
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Neural-Symbolic Reasoning over Knowledge Graphs: A Survey from a Query Perspective
A survey of knowledge graph reasoning methods organized by query type, covering symbolic, neural, neural-symbolic, and large language model approaches.
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