SCIENTIA SINICA Informationis, Volume 50 , Issue 7 : 1019-1032(2020) https://doi.org/10.1360/SSI-2019-0269

Spatiotemporal features based geographical knowledge graph construction

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  • ReceivedDec 1, 2019
  • AcceptedJan 20, 2020
  • PublishedJul 6, 2020


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[1] James, P. Knowledge graphs. In: Van de Riet, Meersman, eds. Linguistic Instruments in Knowledge Engineering. Amsterdam: Elsevier, 1992. 97-117. Google Scholar

[2] Zhao J, Liu K, He S Z, et al. Knowledge map. Beijing: Higher Education Press, 2018, pp.293. Google Scholar

[3] Chen J, Liu W Z, Wu H, et al. Basic Issues and Research Agenda of Geospatial Knowledge Service. Geomat Inform Sci Wuhan Univ, 2019, 44(01): 38-47. Google Scholar

[4] Chen J Y, Deng S M, Chen H J. CrowdGeoKG: crowdsourced Geo-knowledge graph. In: Proceedings of China Conference on Knowledge Graph and Semantic Computing. Singapore: Springer, 2017. Google Scholar

[5] Ballatore A, Bertolotto M, Wilson D. A Structural-Lexical Measure of Semantic Similarity for Geo-Knowledge Graphs. IJGI, 2015, 4: 471-492 CrossRef ADS Google Scholar

[6] Lu F, Yu L, Qiu P Y. On Geographic Knowledge Graph. J Geo-Inf Sci, 2017, 19(6): 723-734. Google Scholar

[7] Lin H, You L, Hu C B, et al. Prospect of Geo-Knowledge Engineering in the Era of Spatio-Temporal Big Data. Geomat Inform Sci Wuhan Univ, 2018, 43(12): 2205-2211. Google Scholar

[8] Miao Q, Xu P, Li X. The Recognition of the Point Symbols in the Scanned Topographic Maps. IEEE Trans Image Process, 2017, 26: 2751-2766 CrossRef PubMed ADS Google Scholar

[9] Yang D. Research on Map Symbol Recognition Based on Neural Network. Dissertation for MA Degree. Guiyang: Guizhou University, 2007. Google Scholar

[10] Hoermann S, Henzler P, Bach M, et al. Object detection on dynamic occupancy grid maps using deep learning and automatic label generation. In: Li L X, Wang F Y, Loannou P, et al. Proceedings of IEEE Intelligent Vehicles Symposium. Changshu: Institute of Electrical and Electronics Engineers Inc, 2018. 826-833. Google Scholar

[11] Luo X, Zhang J, Cao X. Object-aware power line detection using color and near-infrared images. IEEE Trans Aerosp Electron Syst, 2014, 50: 1374-1389 CrossRef ADS Google Scholar

[12] Tian K. Research on Point Symbols Recognition in Color Topographic Map. Dissertation for MA Degree. Xian: Xidian University, 2017. Google Scholar

[13] Feldman J. Perceptual grouping by selection of a logically minimal model. Int J Comput Vision, 2003, 55(1): 5-25. Google Scholar

[14] Hancer E, Samet R. Advanced contour reconnection in scanned topographic maps. In:Kalagiakos, Panagiotis, Karampelas, et al., eds. Proceedings of International Conference on Application of Information & Communication Technologies, New York:IEEE Computer Society, 2011.1-5. Google Scholar

[15] Pezeshk A, Tutwiler R L. Automatic Feature Extraction and Text Recognition From Scanned Topographic Maps. IEEE Trans Geosci Remote Sens, 2011, 49: 5047-5063 CrossRef ADS Google Scholar

[16] Le X Q, Yang C J, Yu W Y. Spatial Concept Extraction Based on Spatial Semantic Role in Natural Language. Geomat Inform Sci Wuhan Univ, 2005, 30(12): 1100-1103. Google Scholar

[17] Jones C B, Purves R S. Geographical information retrieval. Int J Geographical Inf Sci, 2008, 22: 219-228 CrossRef Google Scholar

[18] Qian J, Zhang Y J, Zhang T. Research on Chinese Person Name and Location Name Recognition Based on Maximum Entropy Model. J Chin Mini-Micro Comput Syst, 2006, 27(09): 1761-1765. Google Scholar

[19] Jiang W M. Research on Extraction of Spatial Orientation Relations for Chinese Texts. Dissertation for Ph.D. Degree. Nanjing: Nanjing Normal University, 2010. Google Scholar

[20] Wu L, Liu L, Li H R, et al. A Chinese Toponym Recognition Method Based on Conditional Random Field. Geomat Inform Sci Wuhan Univ, 2017, 42(02): 150-156. Google Scholar

[21] Feng Y Y, Sun L, Zhang D K, et al. Study on Chinese Named Entity Recognition Using Small Scale Character Tail Hints. Acta Electron Sin, 2008 (09): 1833-1838. Google Scholar

[22] Zhang C J. Study on the analysis method of event space-time and attribute information in Chinese text. Dissertation for Ph.D. Degree. Nanjing: Nanjing Normal University, 2013. Google Scholar

[23] Tang X R, Chen X H, Zhang X Y. Research on Toponym Resolution in Chinese Text. Geomat Inform Sci Wuhan Univ, 2010, 35(08): 930-935. Google Scholar

[24] Zhang C J, Zhang X Y, Chen Y T, et al. Extraction of geographical attribute-values in natural language. In: Lee G, eds. Advances in Computational Environment Science. Melbourne: Springer Verlag, 2012.51-59. Google Scholar

[25] Zhang X Y, Ye P, Wang S, et al. Geological entity recognition method based on Deep Belief Networks. Acta Petrol Sin. 2018, 34(2): 343-351. Google Scholar

[26] Wang S, Zhang X Y, Ye P, et al. Deep belief networks based toponym recognition for Chinese text. Isprs Int J Geo-Inf, 2018, 7. Google Scholar

[27] Zhang X Y. Spatio-temporal Information Extraction in Chinese Text. Commun China Comput Feder, 2015, 11(11):33-39. Google Scholar

[28] Zhang C J, Zhang X Y, Li M, et al. Interpretation of Temporal Information in Chinese text. Geogr Geo-Inf Sci, 2014, 30(6): 1-7. Google Scholar

[29] Ma L L, Li H C, Wei Y, et al. Chinese Text Temporal Expression Recognition and Normalization Method Based on Rules. J Inf Eng Univ, 2017, 18(5): 560-565. Google Scholar

[30] Yan Z F, Ji D H. Exploraton of Chinese temporal information extraction based on CRF and semi-supervised learning. Comput Eng Desig, 2015,36(6): 1642-164. Google Scholar

[31] Liu Z J, Tang B Z, Wang X L, et al. CMedTEX: A rule-based temporal expression extraction and normalization system for Chinese clinical notes. Annu Symp Proc, 2017: 818-826. Google Scholar

[32] Jin B W. Study on Chinese Temporal Expression Recognition and Normalization. Dissertation for MA Degree. Shenyang: Shenyang University of Aeronautics and Astronautics, 2018. Google Scholar

[33] Hao T, Pan X, Gu Z. A pattern learning-based method for temporal expression extraction and normalization from multi-lingual heterogeneous clinical texts.. BMC Med Inform Decis Mak, 2018, 18: 22 CrossRef PubMed Google Scholar

[34] Montello D R, Goodchild M F., Gottsegen J, et al. Where's downtown? Behavioral Methods for determining referents of vague spatial queries. Spat Cogn Comput, 2003, 3: 185-204. Google Scholar

[35] Liu Y, Yuan Y H, Zhang Y.A Cognitive Approach to Modeling Vague Geographical Features: A Case Study of Zhongguancun. J Remote Sens, 2008, 2: 370-377. Google Scholar

[36] Yan F. Estimation of Spatial Range of Fuzzy Location Based on Interest Points. Dissertation for MA Degree. Shenyang: Shenyang University of Aeronautics and Astronautics, 2018. Google Scholar

[37] Vogele T, Schlieder C, Visser U. Intuitive modelling of place name regions for spatial information retrieval. In: Kuhn W, Michael W, Timpf S, eds. Spatial Information Theory Foundations of Geographic Information Science. Germany: Springer-Verlag, 2003. 239-252. Google Scholar

[38] Zhang X Y, Zhu S N. Contextual spatial relations based spatial modelling of vague place names. In: Luo Q, eds. 2010 2nd IITA International Conference on Geoscience and Remote Sensing. New York: IEEE Computer Society, 2010. 23-26. Google Scholar

[39] Liu S Y. Extracting Landslide Disaster Information from Web Pages. Dissertation for MA Degree. Chengdu: Southwest Jiaotong University, 2015. Google Scholar

[40] Imran M, Elbassuoni S, Castillo C, et al. Extracting information nuggets from disaster-related messages in social media. In:Comes T, Fiedrich F, Fortier S and et al.,eds. 10th International Conference on Information Systems for Crisis response and management. Germany:KIT,2013.791-801. Google Scholar

[41] Yu L, Lu F, Zhang H C. Extracting Geographic Information from Web Texts:Status and Development. J Geo-Inf Sci, 2015,17(02): 127-134. Google Scholar

[42] Matsuo Y, Sakaki T, Uchiyama K, et al. Graph-based word clustering using a web search engine. In: Jurafsky D, Gaussier E, eds. Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing. Sydney: ACT, 2006.542-550. Google Scholar

[43] Su F L, Xie Q H, Qiu J Y, et al. Study on word clustering for attribute extraction based on deep learning. Micro comput ITS Appl, 2016,35 (01): 53-55 + 59. Google Scholar

[44] Jiang H J. Slot Filling Via Deep Learning. Dissertation for MA Degree. Zhejiang: Zhejiang University, 2017. Google Scholar

[45] Allen J F. Towards a general theory of action and time. Artificial Intelligence, 1984, 23: 123-154 CrossRef Google Scholar

[46] 朱少楠, 张雪英, 张春菊. 地理空间关系描述的句法模式识别. In:Tan H H,eds.Proceedings of 2010 International Conference on Broadcast Technology and Multimedia Communication(Volume 4). Hong Kong: International Communication Sciences Association, 2010. 367-370. Google Scholar

[47] Ma L B, Gong J Y. Research on Spatial Database Query Oriented Natural Language. Comput Eng Appl, 2003 (22): 16-19. Google Scholar

[48] Zheng Y, Long Y, Ming X N, et al. Natural Language Description of Geographic Location Considering Various Spatial Relations with Different References. J Geo-Inf Sci, 2011,13 (04): 465-471. Google Scholar

[49] Yu L, Lu F, Liu X L. A Bootstrapping Based Approach for Open Geo-entity Relation Extraction. Acta Geod Cartogr Sin, 2016, 45(05): 616-622. Google Scholar

[50] Zhang X Y, Zhu S N, Zhang C J. Annotation of Geographical Named Entities of Chinese Text. Acta Geod Cartogr Sin, 2012a, 41 (01): 115-120. Google Scholar

[51] Zhang X Y, Zhang C J, Zhu S N. Annotation of Geographical Named Entities of Chinese Text. Acta Geod Cartogr Sin, 2012b, 41(03): 468-474. Google Scholar

[52] Zhang X Y, Zhang C J, Du C L, et al. SVM based extraction of spatial relations in text. In:Leung L, Zhou C H, Lees B, et al.,eds.Proceedings of Spatial Data Mining and Geographical Knowledge Services. Beijing: Institute of Electrical and Electronics Engineers 2011.529-533. Google Scholar

[53] Wang J B, Lu F, Wu S, et al. Constructing the corpus of Geographical entity relations based on automatic annotation. J Geo-inf Sci, 2018,20(07): 871-879. Google Scholar

[54] Jin Y. Research and implementation of geographical relationship extraction system based on deep learning. Dissertation for MA Degree. Beijing: Beijing University of Posts and Telecommunications,2019. Google Scholar

[55] Gao J L, Yu L, Qiu P Y, et al. A knowledge-based method for filtering Geo-entity relations. J Geo-Inf Sci, 2019, 21(9): 1392-1401. Google Scholar

[56] Brodt A, Nicklas D, Mitschang B. Deep integration of spatial query processing into native RDF triple stores. In: Agrawal D, Zhang P S, Abbadi A E, et al., eds. Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems, California: ACM, 2010, 33-42. Google Scholar

[57] Liagouris J, Mamoulis N, Bouros P. An effective encoding scheme for spatial RDF data. Proc VLDB Endow, 2014, 7: 1271-1282 CrossRef Google Scholar

[58] Wang D, Zou L, Feng Y, et al. S-store: An engine for large RDF graph integrating spatial information. In: Meng W Y, Feng L, Bressan S, et al., eds. Proceedings of the 18th International Conference on Database Systems for Advanced Applications (DASFAA 2013), Berlin: Springer Verlag, 2013, 31-47. Google Scholar

[59] Fu B J. Geography: From Knowledge, Science to decision making support. Acta Geogr Sin, 2017,72(11): 1923-1932. Google Scholar

[60] Commission on Geographical Education, International Geography Union. International Charter on Geography Education. 1992. Google Scholar

[61] Claramunt C, Parent C, Theriault M. Design patterns for spatio-temporal processes.In:Mitchell R, Jezequel J M, Bosch J, et al., eds. Proceedings Technology of Object-Oriented Languages and Systems. California: IEEE, 1999. 2-15. Google Scholar

[62] Haarslev V. A description logic with concrete domains and a role-forming predicate operator. J Logic Computation, 1999, 9: 351-384 CrossRef Google Scholar

[63] Wang S. Research on Construction of Geographic Knowledge Graph Driven by Natural Language. Dissertation for Ph.D. Degree. Nanjing: Nanjing Normal University, 2018. Google Scholar

[64] Frank, Andrew U. Qualitative spatial reasoning: cardinal directions as an example. Int J Geogr Inform Syst, 1996, 10(3): 269-290. Google Scholar

[65] Kondaveeti A, Runger G C, Liu H, et al. Extracting geographic knowledge from sensor intervention data using spatial association rules. In: Proceedings of IEEE International Conference on Spatial Data Mining & Geographical Knowledge Services. Beijing: Institute of Electrical and Electronics Engineers, 2011.127-130. Google Scholar

[66] Yu C, Peuquet D J. A GeoAgent?based framework for knowledge?øriented representation: Embracing social rules in GIS. Int J Geographical Inf Sci, 2009, 23: 923-960 CrossRef Google Scholar

[67] Otero-Cerdeira L, Rodríguez-Martínez F J, Gómez-Rodríguez A. Ontology matching: A literature review. Expert Syst Appl, 2015, 42: 949-971 CrossRef Google Scholar

[68] Jiang B C, Wan G, Xu J, et al. Geographic Knowledge Graph Building Extracted Multi-sourced Heterogeneous Data. Acta Geod Cartogr Sin, 2018, 47(8): 1051-1061. Google Scholar