Constrained Entity Selection under Partial Knowledge for LLM-Based Knowledge Graph QA
[Sztuczna inteligencja] Autorzy: Emanuel Kitzelmann
Large language models are increasingly used for knowledge graph question answering (KGQA), but can fail to correctly ground answers in the underlying graph. Current approaches to LLM-based KGQA either rely on full semantic parsing into executable queries such as SPARQL, which is brittle in practice due to complex schemas or incompleteness of real-world KGs, or on LLM-reasoning and answer generation over KGs, which can be more robust but lacks formal guarantees. In this work, we study a complementary setting in which \emph{candidate} answers are generated by an LLM-based system and subsequently verified using lightweight symbolic constraints derived from the question. We introduce \emph{Constrained Entity Selection under Partial Knowledge (CES-PK)}, a problem formulation that focuses on eliminating invalid answers and providing symbolic support for valid ones without requiring constructi…
- Kategoria arxiv
- cs.AI
- Kategoria
- Sztuczna inteligencja
- Doi
- 10.1007/978-3-032-32335-4_21