Problem
Research questions and friction points this paper is trying to address.
Knowledge Graph Question Answering
Vertically Partitioned Graphs
Multi-hop Question Answering
FedV-KGQA in Practice: Design Lessons and an Interactive Prototype
🤖 AI Summary
研究针对垂直分割知识图谱的多跳问答问题，提出FedV-KGQA方法，通过联邦学习融合局部图谱信息，实现高效准确的知识图谱问答。
📝 Abstract
Knowledge graph question answering usually assumes that one system can reach the whole graph. In practice, facts are often held by organizations that share entity identifiers but own disjoint relation types, so no single party sees a complete reasoning chain. This poster presents the empirical findings of FedV-KGQA on multi-hop question answering over such vertically partitioned graphs. Each silo enriches its local graph and trains a knowledge graph embedding on its own triples. A server then concatenates the silo-specific entity views, anchors the projected question at the topic entity, and ranks candidates by similarity. Raw triples and relation embeddings never leave a silo. Comparing the FedV-KGQA experiments with one another yields three results. First, federated fusion recovers most of the centralized accuracy, while a single silo recovers little. Second, anchoring and enrichment matter more than the choice of embedding model. Third, the cheapest encoder depends on the target accuracy rather than on parameter count. This poster paper contributes that cross-experiment comparison, four design lessons drawn from it, and an interactive prototype that runs real inference and traces the full pipeline, per question, on released checkpoints.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Federated Learning
Knowledge Graph Embedding
Multi-hop Question Answering
Vertical Partitioning
🔎 Similar Papers
No similar papers found.
