QirK: Question Answering via Intermediate Representation on Knowledge Graphs
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Abstract (arXiv)
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 https://youtu.be/6c81BLmOZ0U.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 7 of its 12 references that are in the map. First posted on arXiv 14 Aug 2024.
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References · 7
- 2020Language Models are Few-Shot Learnerscited by 32,419
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- 2023LLaMA: Open and Efficient Foundation Language Modelscited by 14,132
- 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,170
- 2018Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Taskcited by 1,139
- 2023Head-to-Tail: How Knowledgeable are Large Language Models (LLMs)? A.K.A. Will LLMs Replace Knowledge Graphs?cited by 168
- 2020Machine Knowledge: Creation and Curation of Comprehensive Knowledge Basescited by 55
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