No AI summary available for this article.
Why It Matters
Real-world data for knowledge graph question answering is often distributed across different organizations due to governance and data sovereignty constraints.
Provenance
Discovered via ArXiv and published by ArXiv.
Key Claims
Original description
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 l...
Discovered via ArXiv
Research papers and preprints from arXiv.
Publisher: arxiv.org
ID: http://arxiv.org/abs/2608.24846v1 · Indexed 5 days ago