[2408.07494] QirK: Question Answering via Intermediate Representation on Knowledge Graphs
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Computer Science > Databases

arXiv:2408.07494 (cs)
[Submitted on 14 Aug 2024]

Title:QirK: Question Answering via Intermediate Representation on Knowledge Graphs

Authors:Jan Luca Scheerer, Anton Lykov, Moe Kayali, Ilias Fountalis, Dan Olteanu, Nikolaos Vasiloglou, Dan Suciu
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Abstract:We demonstrate QirK, a system for answering natural language questions on Knowledge Graphs (KG). QirK can answer structurally complex questions that are still beyond the reach of emerging Large Language Models (LLMs). It does so using a unique combination of database technology, LLMs, and semantic search over vector embeddings. The glue for these components is an intermediate representation (IR). The input question is mapped to IR using LLMs, which is then repaired into a valid relational database query with the aid of a semantic search on vector embeddings. This allows a practical synthesis of LLM capabilities and KG reliability.
A short video demonstrating QirK is available at this https URL.
Subjects: Databases (cs.DB); Machine Learning (cs.LG)
Cite as: arXiv:2408.07494 [cs.DB]
  (or arXiv:2408.07494v1 [cs.DB] for this version)
  https://doi.org/10.48550/arXiv.2408.07494

Submission history

From: Dan Olteanu [view email]
[v1] Wed, 14 Aug 2024 12:19:25 UTC (3,013 KB)
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