mlsanityData to 14 Oct 2026

FedV-KGQA: Multi-Hop Question Answering over Vertically Partitioned Knowledge Graphs

arXiv 2608.24846 ↗First posted 25 Aug 2026cs.AIcited by 2

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Abstract (arXiv)

Real-world data for knowledge graph question answering is often distributed across different organizations due to governance and data sovereignty constraints. While centralized systems exist, they cannot answer multi-hop questions when the required facts are split across vertically partitioned silos. In this paper, we propose FedV-KGQA, a framework for multi-hop reasoning over knowledge graphs in which organizations share entities but own disjoint sets of relations. Our approach combines local graph enrichment and knowledge graph embeddings to ensure raw triples and relation parameters never leave each silo, establishing a structural data boundary without requiring centralized graph access. We further introduce a topic entity anchoring mechanism that grounds questions in the correct graph neighborhood without any runtime inter-silo communication. We evaluate 12 model configurations across three benchmarks and show that FedV-KGQA performs strongly, remains close to centralized performance, generalizes to 3-hop reasoning, and is robust to embedding perturbations.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 26 of its 43 references that are in the map. First posted on arXiv 25 Aug 2026.

Builds on

26 of 43 references.

References · 26

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14 more
  1. 2020FedE: Embedding Knowledge Graphs in Federated Settingcited by 46
  2. 2021Differentially Private Federated Knowledge Graphs Embeddingcited by 45
  3. 2023Heterogeneous Federated Knowledge Graph Embedding Learning and Unlearningcited by 40
  4. 2025Large Language Models Meet Knowledge Graphs for Question Answering: Synthesis and Opportunitiescited by 31
  5. 2024Harnessing Large Language Models for Knowledge Graph Question Answering via Adaptive Multi-Aspect Retrieval-Augmentationcited by 24
  6. 2022Efficient Federated Learning on Knowledge Graphs via Privacy-preserving Relation Embedding Aggregationcited by 18
  7. 2024LLM-Based Multi-Hop Question Answering with Knowledge Graph Integration in Evolving Environmentscited by 18
  8. 2025Ontology-Guided Reverse Thinking Makes Large Language Models Stronger on Knowledge Graph Question Answeringcited by 15
  9. 2024Vertical Federated Learning for Effectiveness, Security, Applicability: A Surveycited by 14
  10. 2021Improving Embedded Knowledge Graph Multi-hop Question Answering by introducing Relational Chain Reasoningcited by 7
  11. 2024Learning Federated Neural Graph Databases for Answering Complex Queries from Distributed Knowledge Graphscited by 4
  12. 2024Fed-RD: Privacy-Preserving Federated Learning for Financial Crime Detectioncited by 4
  13. 2024A Differentially Private Blockchain-Based Approach for Vertical Federated Learningcited by 3
  14. 2026Federated Personal Knowledge Graph Completion with Lightweight Large Language Models for Personalized Recommendationscited by 2

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026FedV-KGQA in Practice: Design Lessons and an Interactive Prototypecited by 0
  2. 2026Cost Characterization of Vertically Partitioned Federated Knowledge Graphscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2022Exploiting Hybrid Semantics of Relation Paths for Multi-hop Question Answering Over Knowledge Graphs
  2. 2023Relation-Aware Question Answering for Heterogeneous Knowledge Graphs
  3. 2025iQUEST: An Iterative Question-Guided Framework for Knowledge Base Question Answering
  4. 2025KG-o1: Enhancing Multi-hop Question Answering in Large Language Models via Knowledge Graph Integration
  5. 2025StepChain GraphRAG: Reasoning Over Knowledge Graphs for Multi-Hop Question Answering
  6. 2026Theseus in the Graph: Towards Traceable Multi-Hop Graph Navigation
  7. 2025Efficient Multi-Hop Question Answering over Knowledge Graphs via LLM Planning and Embedding-Guided Search
  8. 2026MARS: Multi-hop Adaptive Retrieval and SPARQL Generation for KGQA