[1]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]
Copy
Spatial keyword personalized and semantic approximate query approach
CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume:
15
Number of periods:
2020 6
Page number:
1163-1174
Column:
学术论文—自然语言处理与理解
Public date:
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
- CLC:
- TP311
- DOI:
- 10.11992/tis.201903033
- 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.
- 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.
- Similar References:
Memo
-
Last Update:
2020-12-25