Towards a Transferable Foundation Retriever for Multi-Hop Reasoning over Knowledge Graphs

Foundation retrievers aim to reuse retrieval knowledge across knowledge graphs (KGs), reducing the need to train a separate model for each graph. For multi-hop reasoning, this requires identifying relevant relations and ranking answers under changes in graph structure and question distribution. Yet strong in-domain performance does not necessarily translate into effective cross-KG retrieval. Our analysis of KGFR reveals a substantial ranking gap: correct answers often remain reachable but receive low ranks after transfer. We propose KGSeek, a multi-source foundation retriever that uses semantic relevance to guide retrieval and graph structure to distinguish candidate answers. KGSeek learns question–relation relevance from source questions and answers. It aggregates this relevance along paths to form a semantic ranking reference. A shared scorer incorporates neighborhood context through bounded adjustments. Length-normalized path scoring preserves this bound at the entity level, allowing contextual refinement while retaining strong semantic preferences. Joint training across source KGs learns shared scoring functions that apply to new graphs through their relation labels and structure. Experiments on Freebase and DBpedia show that one source-trained model improves cross-KG retrieval and downstream question answering (QA) over strong baselines with different large language model (LLM) readers. The same architecture also improves in-domain retrieval and maintains competitive QA performance. These results show the value of semantic guidance for transferable multi-hop retrieval.