A Search-Enhanced Path Mining and Ranking Method for Cross-lingual Knowledge Base Question Answering

被引：0

作者：

Wu, Zhanglin ^{[1
]}

Zhu, Ming ^{[1
]}

Zhang, Min ^{[1
]}

Peng, Song ^{[1
]}

Zhang, Weidong ^{[1
]}

Zhu, Ting ^{[1
]}

Zhu, Junhao ^{[1
]}

Li, Peng ^{[1
]}

Hao, Yang ^{[1
]}

Qin, Ying ^{[1
]}

机构：

[1] Huawei Technol Co Ltd, Beijing 100080, Peoples R China

来源：

CCKS 2022 - EVALUATION TRACK | 2022年 / 1711卷

关键词：

KBQA; CCKS2022; CKBQA; Search-enhanced;

D O I：

10.1007/978-981-19-8300-9_4

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：30 / 38

页数：9

共 5 条

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