| 空间关键字个性化语义近似查询方法 |
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| 引用本文： | 李盼,张霄雁,孟祥福,赵路路,齐雪月.空间关键字个性化语义近似查询方法[J].智能系统学报,2020,15(6):1163-1174. |
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| 作者姓名： | 李盼 张霄雁 孟祥福 赵路路 齐雪月 |
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| 作者单位： | 辽宁工程技术大学 电子与信息工程学院, 辽宁 葫芦岛 125105 |
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| 摘 要： | 现有的空间关键字查询处理模式大都仅支持位置相近和文本相似匹配，但不能将语义相近但形式上不匹配的对象提供给用户；并且，当前的空间-文本索引结构也不能对空间对象中的数值属性进行处理。针对上述问题，本文提出了一种支持语义近似查询的空间关键字查询方法。首先，利用词嵌入技术对用户原始查询进行扩展，生成一系列与原始查询关键字语义相关的查询关键字；然后，提出了一种能够同时支持文本和语义匹配，并利用Skyline方法对数值属性进行处理的混合索引结构AIR-Tree；最后，利用AIR-Tree进行查询匹配，返回top-k个与查询条件最为相关的有序空间对象。实验分析和结果表明，与现有同类方法相比，本文方法具有较高的执行效率和较好的用户满意度；基于AIR-Tree索引的查询效率较IRS-Tree索引提高了3.6%，在查询结果准确率上较IR-Tree和IRS-Tree索引分别提高了10.14%和16.15%。
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| 关 键 词： | 空间关键字查询 词嵌入 语义近似查询 文本 数值属性 索引结构 查询匹配 |
Spatial keyword personalized and semantic approximate query approach |
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| LI Pan,ZHANG Xiaoyan,MENG Xiangfu,ZHAO Lulu,QI Xueyue.Spatial keyword personalized and semantic approximate query approach[J].CAAL Transactions on Intelligent Systems,2020,15(6):1163-1174. |
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| Authors: | LI Pan ZHANG Xiaoyan MENG Xiangfu ZHAO Lulu QI Xueyue |
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| Affiliation: | School of Electronic and Information Engineering, Liaoning Technical University, Huludao 125105, China |
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| Abstract: | Most spatial keyword query processing models only support the location proximity and text similarity matching. However, in terms of text information processing, spatial objects with similar semantics but mismatched forms cannot be filtered out and provided to query users. Furthermore, the current spatial-text index structure cannot process the numerical attributes. To solve the above problem, this paper proposes a spatial keyword query method that can support the semantic approximate query processing. Word embedding technology is used to expand the users’ original queries and generate a series of query keywords semantically related to the original query keywords. Then, a hybrid index structure AIR-tree that can support text and semantic matching and use the Skyline method to process numerical attributes is proposed. Finally, AIR-tree is used for query matching to return the top-k ordered spatial objects most closely related to the query conditions. Experimental analysis and results show that compared with similar methods, this method has a higher execution efficiency and better user satisfaction. The query efficiency based on the AIR-tree index is 3.6% higher than that of the IRS-tree index. In terms of accuracy, IR-tree and IRS-tree are increased by 10.14% and 16.15%, respectively, compared with AIR-tree. |
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| Keywords: | spatial keyword query word embedding semantic approximate query text numerical attribute index structure query matching |
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