[1]李盼,张霄雁,孟祥福,等.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.[doi:10.11992/tis.201903033]
LI Pan,ZHANG Xiaoyan,MENG Xiangfu,et al.Spatial keyword personalized and semantic approximate query approach[J].CAAI Transactions on Intelligent Systems,2020,15(6):1163-1174.[doi:10.11992/tis.201903033]
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LI Pan,ZHANG Xiaoyan,MENG Xiangfu,et al.Spatial keyword personalized and semantic approximate query approach[J].CAAI Transactions on Intelligent Systems,2020,15(6):1163-1174.[doi:10.11992/tis.201903033]
空间关键字个性化语义近似查询方法
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
15
期数:
2020年第6期
页码:
1163-1174
栏目:
学术论文—自然语言处理与理解
出版日期:
2020-11-05
- Title:
- Spatial keyword personalized and semantic approximate query approach
- Keywords:
- spatial keyword query; word embedding; semantic approximate query; text; numerical attribute; index structure; query matching
- 分类号:
- TP311
- 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%。
- 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.
- 参考文献/References:
-
[1] LU Ying, LU Jiacheng, CONG Gao, et al. Efficient algorithms and cost models for reverse spatial-keyword k-nearest neighbor search[J]. ACM transactions on database systems, 2014, 39(2): 13.
[2] DE FELIPE I, HRISTIDIS V, RISHE N. Keyword search on spatial databases[C]//Proceedings of 2008 IEEE 24th International Conference on Data Engineering. Cancun, Mexico: IEEE, 2008: 656-665.
[3] ZHANG Chengyuan, ZHANG Ying, ZHANG Wenjie, et al. Inverted linear quadtree: efficient top K spatial keyword search[J]. IEEE transactions on knowledge and data engineering, 2016, 28(7): 1706-1721.
[4] BECKMANN N, KRIEGEL H P, SCHNEIDER R, et al. The R*-tree: an efficient and robust access method for points and rectangles[J]. ACM SIGMOD record, 1990, 19(2): 322-331.
[5] ZHANG Dongxiang, OOI B C, TUNG A K H. Locating mapped resources in web 2.0[C]//Proceedings of 2010 IEEE 26th International Conference on Data Engineering (ICDE 2010). Long Beach, USA: IEEE, 2010: 521-532.
[6] LI Feifei, YAO Bin, TANG Mingwang, et al. Spatial approximate string search[J]. IEEE transactions on knowledge and data engineering, 2013, 25(6): 1394-1409.
[7] ROCHA-JUNIOR J B, VLACHOU A, DOULKERIDIS C, et al. Efficient processing of top-k spatial preference queries[J]. Proceedings of the VLDB endowment, 2010, 4(2): 93-104.
[8] YAO Bao, LI Feifei, HADJIELEFTHERIOU M, et al. Approximate string search in spatial databases[C]//Proceedings of 2010 IEEE 26th International Conference on Data Engineering. Long Beach, CA, USA: IEEE, 2010: 545-556.
[9] QIAN Zhihu, XU Jiajie, ZHENG Kai, et al. Semantic-aware top-k spatial keyword queries[J]. World wide web, 2018, 21(3): 573-594.
[10] 胡骏, 范举, 李国良, 等. 空间数据上Top-k关键词模糊查询算法[J]. 计算机学报, 2012, 35(11): 2237-2246
HU Jun, FAN Ju, LI Guoliang, et al. Top-k fuzzy spatial keyword search[J]. Chinese journal of computers, 2012, 35(11): 2237-2246
[11] 刘喜平, 万常选, 刘德喜, 等. 空间关键词搜索研究综述[J]. 软件学报, 2016, 27(2): 329-347
LIU Xiping, WAN Changxuan, LIU Dexi, et al. Survey on spatial keyword search[J]. Journal of software, 2016, 27(2): 329-347
[12] ZHANG Dongxaing, CHEE Y M, MONDAL A, et al. Keyword search in spatial databases: Towards searching by document[C]//Proceedings of 2009 IEEE 25th International Conference on Data Engineering. Shanghai, China: IEEE, 2009: 688-699.
[13] CONG Gao, JENSEN C S, WU Dingming. Efficient retrieval of the top-k most relevant spatial web objects[J]. Proceedings of the VLDB endowment, 2009, 2(1): 337-348.
[14] KWON H Y, WANG Haixun, WHANG K Y. G-index model: a generic model of index schemes for top-k spatial-keyword queries[J]. World wide web, 2015, 18(4): 969-995.
[15] LEVY O, GOLDBERG Y. Linguistic regularities in sparse and explicit word representations[C]//Proceedings of the Eighteenth Conference on Computational Natural Language Learning. Michigan, USA, 2014: 171-180.
[16] MORSE M, PATEL J M, JAGADISH H V. Efficient skyline computation over low-cardinality domains[C]//Proceedings of the 33rd International Conference on Very large Data Bases. Vienna, Austria: VLDB Endowment, 2007: 267-278.
[17] LIU Xiping, CHEN Lei, WAN Changxuan. LINQ: a framework for location-aware indexing and query processing[J]. IEEE transactions on knowledge and data engineering, 2015, 27(5): 1288-1300.
[18] BORZSONY S, KOSSMANN D, STOCKER K. The skyline operator[C]//Proceedings 17th International Conference on Data Engineering. Heidelberg, Germany: IEEE, 2001: 421-430.
[19] LEBRET R, COLLOBERT R. Word embeddings through Hellinger PCA[C]//Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics. Gothenburg, Sweden, 2014: 482-490.
[20] LEVY O, GOLDBERG Y. Neural word embedding as implicit matrix factorization[C]//Proceedings of the 27th International Conference on Neural Information Processing Systems. Montreal, Canada: ACM, 2014: 2177-2185.
[21] LI Yitan, XU Linli, TIAN Fei, et al. Word embedding revisited: a new representation learning and explicit matrix factorization perspective[C]//Proceedings of the Twenty-Fourth International Conference on Artificial Intelligence. Buenos Aires, Argentina, 2015: 3650-3656.
[22] GLOBERSON A, CHECHIK G, PEREIRA F, et al. Euclidean embedding of co-occurrence data[J]. The journal of machine learning research, 2007, 8: 2265-2295.
[23] QURESHI M A, GREENE D. EVE: explainable vector based embedding technique using Wikipedia[J]. Journal of intelligent information systems, 2019, 53(1): 137-165.
[24] SOCHER R, BAUER J, MANNING C D, et al. Parsing with compositional vector grammars[C]//Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics. Sofia, Bulgaria, 2013: 455-465.
[25] SOCHER R, PERELYGIN A, WU J, et al. Recursive deep models for semantic compositionality over a sentiment Treebank[C]//Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing. Washington, USA, 2013: 1631-1642.
[26] MIKOLOV T, SUTSKEVER I, CHEN Kai, et al. Distributed representations of words and phrases and their compositionality[C]//Proceedings of the 26th International Conference on Neural Information Processing Systems. Lake Tahoe, Nevada, USA, 2013: 3111-3119.
[27] GODFREY P, SHIPLEY R, GRYZ J. Maximal vector computation in large data sets[C]//Proceedings of the 31st International Conference on Very Large Data Bases. Trondheim, Norway, 2005: 229-240.
[28] ASUDEH A, THIRUMURUGANATHAN S, ZHANG Nan. Discovering the skyline of web databases[J]. Proceedings of the VLDB endowment, 2016, 9(7): 600-611.
[29] ILYAS I F, BESKALES G, SOLIMAN M A. A survey of top-k query processing techniques in relational database systems[J]. ACM computing surveys, 2008, 40(4): 11.
[30] KOSSMANN D, RAMSAK F, ROST S. Shooting stars in the sky: an online algorithm for skyline queries[C]//Proceedings of the 28th International Conference on Very Large Data Bases. Hong Kong, China, 2002: 275-286.
备注/Memo
收稿日期:2019-03-25。
基金项目:国家自然科学基金面上项目(61772249)
作者简介:李盼，硕士研究生，主要研究方向为空间关键词查询和空间推荐系统;张霄雁，博士研究生，主要研究方向为空间数据分析、城市计算和深度学习。提出了空间对象的聚类分析方法、空间?文本数据的语义近似查询和多样性推荐方法，主持辽宁省教育厅科学研究项目1项。发表学术论文10余篇;孟祥福，教授，博士生导师，主要研究方向为空间数据管理、推荐系统和大数据可视化等。提出了空间对象的耦合关系分析模型，多样性兴趣点推荐方法和Web数据库top-k个性化检索方法，主持国家自然科学基金2项、辽宁省自然科学基金项目等3项， 发表学术论文30余篇。
通讯作者:孟祥福.E-mail:marxi@126.com
更新日期/Last Update:
2020-12-25