Hybrid-SQuAD: Hybrid Scholarly Question Answering Dataset
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866912144860119040 |
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| author | Taffa, Tilahun Abedissa Banerjee, Debayan Assabie, Yaregal Usbeck, Ricardo |
| author_facet | Taffa, Tilahun Abedissa Banerjee, Debayan Assabie, Yaregal Usbeck, Ricardo |
| contents | Existing Scholarly Question Answering (QA) methods typically target homogeneous data sources, relying solely on either text or Knowledge Graphs (KGs). However, scholarly information often spans heterogeneous sources, necessitating the development of QA systems that integrate information from multiple heterogeneous data sources. To address this challenge, we introduce Hybrid-SQuAD (Hybrid Scholarly Question Answering Dataset), a novel large-scale QA dataset designed to facilitate answering questions incorporating both text and KG facts. The dataset consists of 10.5K question-answer pairs generated by a large language model, leveraging the KGs DBLP and SemOpenAlex alongside corresponding text from Wikipedia. In addition, we propose a RAG-based baseline hybrid QA model, achieving an exact match score of 69.65 on the Hybrid-SQuAD test set. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_02788 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Hybrid-SQuAD: Hybrid Scholarly Question Answering Dataset Taffa, Tilahun Abedissa Banerjee, Debayan Assabie, Yaregal Usbeck, Ricardo Computation and Language Artificial Intelligence Existing Scholarly Question Answering (QA) methods typically target homogeneous data sources, relying solely on either text or Knowledge Graphs (KGs). However, scholarly information often spans heterogeneous sources, necessitating the development of QA systems that integrate information from multiple heterogeneous data sources. To address this challenge, we introduce Hybrid-SQuAD (Hybrid Scholarly Question Answering Dataset), a novel large-scale QA dataset designed to facilitate answering questions incorporating both text and KG facts. The dataset consists of 10.5K question-answer pairs generated by a large language model, leveraging the KGs DBLP and SemOpenAlex alongside corresponding text from Wikipedia. In addition, we propose a RAG-based baseline hybrid QA model, achieving an exact match score of 69.65 on the Hybrid-SQuAD test set. |
| title | Hybrid-SQuAD: Hybrid Scholarly Question Answering Dataset |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2412.02788 |