KIF-QA: Using Off-the-shelf LLMs to Answer Simple Questions over Heterogeneous Knowledge Bases

We present KIF-QA, a semantic parsing-based approach for answering simple questions over heterogeneous knowledge bases. KIF-QA uses off-the-shelf pre-trained large language models (LLMs) and in-context (few-shot) learning to transform questions into interpretable logical forms (queries) without requiring any fine-tuning. Because it uses KIF (the knowledge integration framework) to mediate all access to the underlying knowledge base, KIF-QA can be easily adapted to target any base accessible through KIF (which out-of-the-box includes Wikidata, DBpedia, and PubChem). We evaluate KIF-QA over the Wikidata and DBpedia versions of the SimpleQuestions benchmark using Llama 3.3, Llama 4 Maverick, and Mistral Medium 3. The results show competitive performance to comparable state-of-the-art methods. KIF-QA implementation is made available under an open-source license.