A Method for Multi-Hop Question Answering on Persian Knowledge Graph

• URL: http://arxiv.org/abs/2501.16350v1
• Date: Sat, 18 Jan 2025 18:11:29 GMT
• Title: A Method for Multi-Hop Question Answering on Persian Knowledge Graph
• Authors: Arash Ghafouri, Mahdi Firouzmandi, Hasan Naderi,
• Abstract summary: Major challenges persist in answering multi-hop complex questions, particularly in Persian. One of the main challenges is the accurate understanding and transformation of these multi-hop complex questions into semantically equivalent SPARQL queries. In this study, a dataset of 5,600 Persian multi-hop complex questions was developed, along with their forms based on the semantic representation of the questions. An architecture was proposed for answering complex questions using a Persian knowledge graph.
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• Abstract: Question answering systems are the latest evolution in information retrieval technology, designed to accept complex queries in natural language and provide accurate answers using both unstructured and structured knowledge sources. Knowledge Graph Question Answering (KGQA) systems fulfill users' information needs by utilizing structured data, representing a vast number of facts as a graph. However, despite significant advancements, major challenges persist in answering multi-hop complex questions, particularly in Persian. One of the main challenges is the accurate understanding and transformation of these multi-hop complex questions into semantically equivalent SPARQL queries, which allows for precise answer retrieval from knowledge graphs. In this study, to address this issue, a dataset of 5,600 Persian multi-hop complex questions was developed, along with their decomposed forms based on the semantic representation of the questions. Following this, Persian language models were trained using this dataset, and an architecture was proposed for answering complex questions using a Persian knowledge graph. Finally, the proposed method was evaluated against similar systems on the PeCoQ dataset. The results demonstrated the superiority of our approach, with an improvement of 12.57% in F1-score and 12.06% in accuracy compared to the best comparable method.

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