| 贝叶斯网络查询语义扩展的专家发现方法 |
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| 引用本文： | 郑伟,侯宏旭,班志杰.贝叶斯网络查询语义扩展的专家发现方法[J].计算机工程与应用,2020,56(13):194-198. |
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| 作者姓名： | 郑伟 侯宏旭 班志杰 |
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| 作者单位： | 1.内蒙古大学 计算机学院，呼和浩特 010021
2.河北北方学院 理学院，河北 张家口 075000 |
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| 基金项目： | 河北省社会科学基金;国家自然科学基金;内蒙古自治区自然科学基金 |
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| 摘 要： | 专家发现是实体检索领域的一个研究热点,针对经典专家发现模型存在索引术语独立性假设与检索性能低的缺陷,提出一种基于贝叶斯网络模型的专家发现方法。该方法模型采用四层网络结构,能够实现图形化的概率推理,同时运用词向量技术能够实现查询术语的语义扩展。实验结果显示该模型在多个评价指标上均优于经典专家发现模型,能够有效实现查询术语语义扩展,提高专家检索性能。
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| 关 键 词： | 专家发现方法 贝叶斯网络 查询术语 |
Expert Finding Method Using Baysian Network on Query Semantic Extension |
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| ZHENG Wei,HOU Hongxu,BAN Zhijie.Expert Finding Method Using Baysian Network on Query Semantic Extension[J].Computer Engineering and Applications,2020,56(13):194-198. |
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| Authors: | ZHENG Wei HOU Hongxu BAN Zhijie |
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| Affiliation: | 1.College of Computer Science, Inner Mongolia University, Hohhot 010021, China
2.College of Science, Hebei North University, Zhangjiakou, Hebei 075000, China |
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| Abstract: | Expert finding is a research hotspot in the field of entity retrieval. Aiming at the shortcomings of the classical expert discovery model, such as the assumption of indexing term independence and the low retrieval performance, and an expert discovery method of Bayesian network with query semantic extension is proposed. The model adopts four-layer network structure, which can realize graphical probabilistic inference, and the semantic extension of query terms can be realized by word vector technology. Experimental results show that the new model is superior to the classical expert discovery model in terms of multiple evaluation indexes, indicating that the new model can effectively extend the semantics of query terms and improve the performance of expert retrieval. |
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| Keywords: | expert finding method Bayesian network query term |
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