Abstract
Knowledge Base Question Answering (KBQA) is a popular research direction in knowledge graphs, and most of KBQA work focuses on natural language parsing. In order to enhance the application of cross-lingual knowledge, the 16th China Conference on Knowledge Graph and Semantic Computing (CCKS2022) has released a cross-lingual knowledge base question answering (CKBQA) task. This paper presents the submission of our team (HW-TSC) to the CKBQA task, in which we propose a search-enhanced path mining and ranking method. The method divides the process of CKBQA into four parts: question classification, principal entity extraction, search-enhanced candidate path mining and candidate path ranking. Finally, both the preliminary and final evaluation results prove the effectiveness of our method.
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Wu, Z. et al. (2022). A Search-Enhanced Path Mining and Ranking Method for Cross-lingual Knowledge Base Question Answering. In: Zhang, N., Wang, M., Wu, T., Hu, W., Deng, S. (eds) CCKS 2022 - Evaluation Track. CCKS 2022. Communications in Computer and Information Science, vol 1711. Springer, Singapore. https://doi.org/10.1007/978-981-19-8300-9_4
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DOI: https://doi.org/10.1007/978-981-19-8300-9_4
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