QirK: Question Answering via Intermediate Representation on Knowledge Graphs
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2024
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866909287041728512 |
|---|---|
| author | Scheerer, Jan Luca Lykov, Anton Kayali, Moe Fountalis, Ilias Olteanu, Dan Vasiloglou, Nikolaos Suciu, Dan |
| author_facet | Scheerer, Jan Luca Lykov, Anton Kayali, Moe Fountalis, Ilias Olteanu, Dan Vasiloglou, Nikolaos Suciu, Dan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_07494 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | QirK: Question Answering via Intermediate Representation on Knowledge Graphs Scheerer, Jan Luca Lykov, Anton Kayali, Moe Fountalis, Ilias Olteanu, Dan Vasiloglou, Nikolaos Suciu, Dan Databases Machine Learning 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. |
| title | QirK: Question Answering via Intermediate Representation on Knowledge Graphs |
| topic | Databases Machine Learning |
| url | https://arxiv.org/abs/2408.07494 |