Nov 2026· IEEE Transactions on Knowledge and Data Engineering· Vol 38, pp. 7722-7733· 1 citation· 45 references
Abstract
Knowledge Graph Question Answering (KGQA) requires multi-step reasoning over a large, structured search space, where free-form LLM planning can hallucinate intermediate subgraphs or query sketches and yield unexecutable reasoning. We propose READS, a constraint-guided discriminative reasoning framework that makes KGQA controllable by reformulating it into subgraph search, constraint-based pruning, and answer selection, and solving each step via selection over KG-grounded options rather than open-vocabulary generation. READS constructs a token-level constrained decoding tree from KG-reachable candidates and uses LLM scoring to traverse and prune the search space, improving executability and faithfulness. READS obtains training supervision by parsing annotated SPARQL queries, assuming benchmark-provided topic entities. On WebQSP and CWQ, READS achieves 0.840/0.845 and 0.802/0.820 in Hits@1/F1, outperforming strong LLM-based baselines.
Large language models (LLMs) are increasingly combined with knowledge graphs (KGs) to ground reasoning in structured evidence. However, most LLM-based KGQA methods rely on traversing existing graph edges and become unreliable when reasoning paths are broken by missing facts. Alternatives that ask LLMs to generate missi...
A relation-centric exploration paradigm is introduced, which uses relations rather than entities as search units and thus avoids unreliable entity pruning and proposes Compositional Chain-of-Relations (CCoR), a simple and effective framework that grounds both phases in the KG with two relation chains.
Large language models (LLMs) have demonstrated strong capabilities in question answering, yet they still frequently suffer from hallucinations on knowledge-intensive tasks. Knowledge graphs (KGs) provide LLMs with structured, interpretable, and updatable factual grounding, making them a promising external knowledge sou...
Yang Hong, Ya-Jun Yang, Xin Wang et al.· 0 citations
GRS-QA is introduced, a reasoning graph-structured question answering dataset that augments multi-hop QA instances from exisiting datasets, with directed reasoning graphs representing intermediate inference steps, and highlights the importance of unified structural annotations for understanding and improving multi-hop...
Khin S. Yone, Devasha Trivedi, Anish Pahilajani et al.· International Conference on...· 0 citations
KGVoyager is presented, a KG-agnostic agentic architecture that generates SPARQL queries from natural language questions by dynamically discovering graph structure and semantics, requiring only a query endpoint of the underlying graph.
SCoP (Structured Constraint Parsing), a constraint-centric framework that externalizes temporal decisions before answer inference, separates answer-seeking event patterns from temporal anchor events, conservatively grounds them to canonical TKG entities and relations, and translates temporal intent into executable cons...
Xiao-Kun Guo, Zhen Xu, Dongdong Huo et al.· 0 citations
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