arXiv:2609.14528v1 Announce Type: cross Abstract: Multi-Hop Knowledge Graph Question Answering (KGQA) tasks require models to assemble relational evidence along paths in a KG to answer natural-langua...
arXiv:2606. 28076v1 Announce Type: new Abstract: Knowledge graph question answering (KGQA) aims to answer natural-language questions by reasoning over structured facts.
arXiv:2610.06650v2 Announce Type: replace Abstract: Wikidata is one of the largest open knowledge bases, yet answering a complex question over it still requires a SPARQL query that names the right en...
arXiv:2603.28773v2 Announce Type: replace-cross Abstract: Large language models (LLMs) frequently generate confident yet factually incorrect content when used for language generation (a phenomenon of...
FedV-KGQA addresses multi‑hop question answering over vertically partitioned knowledge graphs where each silo holds disjoint relation types. The system trains local embeddings, concatenates silo‑specific entity views, anchors questions at a topic entity, and ranks candidates without sharing raw triples. Experiments show federated fusion nearly matches centralized accuracy, that anchoring and enrichment are more critical than embedding choice, and that the cheapest encoder depends on target accuracy.
arXiv:2609.39786v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly combined with knowledge graphs (KGs) to ground reasoning in structured evidence. However, most LLM-based...
The paper introduces Constrained Entity Selection under Partial Knowledge (CES-PK), a framework for improving large language model (LLM) based knowledge graph question answering (KGQA) by filtering candidate answers with lightweight symbolic constraints instead of full semantic parsing. CES-PK uses a three-valued constraint semantics—satisfied, violated, unknown—to handle incomplete knowledge graphs and avoid incorrect rejections under open‑world assumptions. Experiments on the Hetionet biomedical knowledge graph show that applying type, relation, and exclusion constraints increases precision while preserving recall, and that satisfied constraints can be used to rank remaining candidates.
arXiv:2606. 17856v1 Announce Type: new Abstract: Graph-based retrieval-augmented generation (GraphRAG) is effective for knowledge-intensive and multi-hop query tasks; however, many existing methods primarily seed entity-based graphs and rely on implicit semantic relevance propagation.
arXiv:2606. 18075v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) has emerged as a paradigm for enhancing large language models (LLMs) with external knowledge, yet existing graph-based methods face a fundamental limitation: entity-centric and chunk-centric approaches operate on representations anchored to original text without true knowledge fusion.
arXiv:2606. 05901v1 Announce Type: cross Abstract: Large language models (LLMs) have fundamentally transformed the landscape of Natural Language Processing.
The paper presents an end‑to‑end pipeline for automatically constructing a tree‑structured knowledge graph (KG) from Vietnamese high school history textbooks and evaluating retrieval strategies that exploit the KG’s hierarchical structure. The KG construction uses a three‑phase hybrid relation extraction process, including intra‑batch deduplication, approximate cross‑batch search, and LLM extraction with a centroid filter and dual‑LLM validator, resulting in 750 nodes and 4,341 semantic edges across 41 ontology types. Retrieval evaluation compares three graph traversal strategies—Top‑Down, Horizontal, and Bottom‑Up—on a benchmark of 1,210 Vietnamese queries, finding that the Top‑Down strategy with structural information outperforms a vector baseline by 4.7 percentage points in NDCG@10.
arXiv:2410.12537v4 Announce Type: replace-cross Abstract: Complex query answering (CQA) on knowledge graphs (KGs) is gaining momentum as a challenging reasoning task. In this paper, we show that the...