Ola El Khatib

The work introduces XoG (eXplore-over-Graph), a framework for multi-hop question answering over incomplete knowledge graphs that uses type-level entity-relation statistics to identify candidate relations and KG embeddings to retrieve plausible missing entities, with the LLM acting as semantic selector and reasoner inside an iterative planning-exploration-reasoning process; on WebQSP, CWQ, and the Wikidata-based BRINK benchmark, XoG remains competitive on complete KGs and consistently outperforms comparable methods without task-specific KGQA training under incompleteness, with gains persisting across multiple LLM backbones and LLM token consumption reduced by up to 33% versus a closely related planning-based approach.

The work introduces XoG (eXplore-over-Graph), a framework for multi-hop question answering over incomplete knowledge graphs that uses type-level entity-relation statistics to identify candidate relations and KG embeddings to retrieve plausible missing entities, with the LLM acting as semantic selector and reasoner inside an iterative planning-exploration-reasoning process; on WebQSP, CWQ, and the Wikidata-based BRINK benchmark, XoG remains competitive on complete KGs and consistently outperforms comparable methods without task-specific KGQA training under incompleteness, with gains persisting across multiple LLM backbones and LLM token consumption reduced by up to 33% versus a closely related planning-based approach.