Integrating SPARQL and LLMs for Question Answering over Scholarly Data Sources
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866910719508742144 |
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| author | Fondi, Fomubad Borista Fidel, Azanzi Jiomekong Camara, Gaoussou |
| author_facet | Fondi, Fomubad Borista Fidel, Azanzi Jiomekong Camara, Gaoussou |
| contents | The Scholarly Hybrid Question Answering over Linked Data (QALD) Challenge at the International Semantic Web Conference (ISWC) 2024 focuses on Question Answering (QA) over diverse scholarly sources: DBLP, SemOpenAlex, and Wikipedia-based texts. This paper describes a methodology that combines SPARQL queries, divide and conquer algorithms, and a pre-trained extractive question answering model. It starts with SPARQL queries to gather data, then applies divide and conquer to manage various question types and sources, and uses the model to handle personal author questions. The approach, evaluated with Exact Match and F-score metrics, shows promise for improving QA accuracy and efficiency in scholarly contexts. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_18969 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Integrating SPARQL and LLMs for Question Answering over Scholarly Data Sources Fondi, Fomubad Borista Fidel, Azanzi Jiomekong Camara, Gaoussou Information Retrieval Artificial Intelligence The Scholarly Hybrid Question Answering over Linked Data (QALD) Challenge at the International Semantic Web Conference (ISWC) 2024 focuses on Question Answering (QA) over diverse scholarly sources: DBLP, SemOpenAlex, and Wikipedia-based texts. This paper describes a methodology that combines SPARQL queries, divide and conquer algorithms, and a pre-trained extractive question answering model. It starts with SPARQL queries to gather data, then applies divide and conquer to manage various question types and sources, and uses the model to handle personal author questions. The approach, evaluated with Exact Match and F-score metrics, shows promise for improving QA accuracy and efficiency in scholarly contexts. |
| title | Integrating SPARQL and LLMs for Question Answering over Scholarly Data Sources |
| topic | Information Retrieval Artificial Intelligence |
| url | https://arxiv.org/abs/2409.18969 |