References
- Acheson, E., Volpi, M., and Purves, R.S., 2019. Machine learning for cross-gazetteer matching of natural features. International Journal of Geographical Information Science, 34 (4), 708–734.
- Auer, S., Lehmann, J., and Hellmann, S., 2009. Linkedgeodata: adding a spatial dimension to the web of data. International Semantic Web Conference. Washington, DC, USA, 731–746.
- Ballatore, A., Bertolotto, M., and Wilson, D.C., 2015. A structural-lexical measure of semantic similarity for geo-knowledge graphs. ISPRS International Journal of Geo-Information, 4 (2), 471–492. doi:https://doi.org/10.3390/ijgi4020471.
- Balley, S., Parent, C., and Spaccapietra, S., 2004. Modelling geographic data with multiple representations. International Journal of Geographical Information Science, 18 (4), 327–352. doi:https://doi.org/10.1080/13658810410001672881.
- Beeri, C., et al., 2004. Object fusion in geographic information systems. Proceedings of the Thirtieth international conference on Very large data bases-Volume 30. Toronto, Canada, 816–827.
- Bizer, C., Heath, T., and Berners-Lee, T., 2009. Linked data - the story so far. International Journal on Semantic Web and Information Systems (IJSWIS), 5 (3), 1–22. doi:https://doi.org/10.4018/jswis.2009081901.
- Callier, S. and Saito, H., 2012. Automatic road area extraction from printed maps based on linear feature detection. IEICE Transactions on Information and Systems, 95 (7), 1758–1765. doi:https://doi.org/10.1587/transinf.E95.D.1758.
- Chen, -C.-C., Knoblock, C.A., and Shahabi, C., 2008. Automatically and accurately conflating raster maps with orthoimagery. GeoInformatica, 12 (3), 377–410. doi:https://doi.org/10.1007/s10707-007-0033-0.
- Chen, J., Deng, S., and Chen, H., 2017. Crowdgeokg: crowdsourced geo-knowledge graph. China Conference on Knowledge Graph and Semantic Computing. Chendu, China, 165–172.
- Chiang, -Y.-Y., et al. 2009. Automatic and accurate extraction of road intersections from raster maps. GeoInformatica, 13 (2), 121–157. doi:https://doi.org/10.1007/s10707-008-0046-3.
- Chiang, -Y.-Y., 2015. Querying historical maps as a unified, structured, and linked spatiotemporal source: vision paper. Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems. Seattle, Washington, USA, 1–4.
- Chiang, -Y.-Y. and Knoblock, C.A., 2012. Generating named road vector data from raster maps. International Conference on Geographic Information Science. Columbus, Ohio, USA, 57–71.
- Chiang, -Y.-Y. and Knoblock, C.A., 2013. A general approach for extracting road vector data from raster maps. International Journal on Document Analysis and Recognition (IJDAR), 16 (1), 55–81. doi:https://doi.org/10.1007/s10032-011-0177-1.
- Chiang, -Y.-Y., Leyk, S., and knoblock, C.A., 2014. A survey of digital map processing techniques. ACM Computing Surveys (CSUR), 47 (1), 1. doi:https://doi.org/10.1145/2557423.
- Clementini, E. and Di Felice, P., 1997. Approximate topological relations. International Journal of Approximate Reasoning, 16 (2), 173–204. doi:https://doi.org/10.1016/S0888-613X(96)00127-2.
- Cova, T.J. and Goodchild, M.F., 2002. Extending geographical representation to include fields of spatial objects. International Journal of Geographical Information Science, 16 (6), 509–532. doi:https://doi.org/10.1080/13658810210137040.
- Deng, M., Li, Z., and Chen, X., 2007. Extended Hausdorff distance for spatial objects in GIS. International Journal of Geographical Information Science, 21 (4), 459–475. doi:https://doi.org/10.1080/13658810601073315.
- Devogele, T., 2002. A new merging process for data integration based on the discrete Fréchet distance. In: Advances in spatial data handling. Berlin: Springer, 167–181.
- Devogele, T., Trevisan, J., and Raynal, L., 1996. Building a multi-scale database with scale-transition relationships. International symposium on spatial data handling. Delft, Netherlands, 337–351.
- Egenhofer, M.J., Sharma, J., and Mark, D.M., 1993. A critical comparison of the 4-intersection and 9-intersection models for spatial relations: formal analysis. AUTOCARTO 11, Minneapolis, Minnesota, USA, 1-12.
- Fan, H., et al., 2016. A polygon-based approach for matching OpenStreetMap road networks with regional transit authority data. International Journal of Geographical Information Science, 30 (4), 748–764. doi:https://doi.org/10.1080/13658816.2015.1100732.
- Filin, S. and Doytsher, Y., 2000. Detection of corresponding objects in linear-based Map conflation. Surveying and Land Information Systems, 60 (2), 117–128.
- Fonseca, F.T., et al. 2002. Using ontologies for integrated geographic information systems. Transactions in GIS, 6 (3), 231–257. doi:https://doi.org/10.1111/1467-9671.00109.
- Freeman, H. and Pieroni, G.G., 1980. Map Data Processing: Proceedings of a NATO Advanced Study Institute on Map Data Processing,Maratea, Italy, June 18–29. Academic Press.
- Goodchild, M.F., Yuan, M., and Cova, T.J., 2007. Towards a general theory of geographic representation in GIS. International Journal of Geographical Information Science, 21 (3), 239–260. doi:https://doi.org/10.1080/13658810600965271.
- Hastings, J., 2008. Automated conflation of digital gazetteer data. International Journal of Geographical Information Science, 22 (10), 1109–1127. doi:https://doi.org/10.1080/13658810701851453.
- Hitzler, P., Krotzsch, M., and Rudolph, S., 2009. Foundations of semantic web technologies. Boca Raton, Florida, USA: CRC Press.
- Hope, S. and Kealy, A., 2008. Using topological relationships to inform a data integration process. Transactions in GIS, 12 (2), 267–283. doi:https://doi.org/10.1111/j.1467-9671.2008.01098.x.
- Kaffes, V., et al., 2019. Learning domain specific models for toponym interlinking. Proceedings of the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, Chicago, Illinois, USA, 504–507.
- Kaim, D., et al., 2016. Broad scale forest cover reconstruction from historical topographic maps. Applied Geography, 67, 39–48. doi:https://doi.org/10.1016/j.apgeog.2015.12.003.
- Leyk, S., Boesch, R., and Weibel, R., 2006. Saliency and semantic processing: extracting forest cover from historical topographic maps. Pattern Recognition, 39 (5), 953–968. doi:https://doi.org/10.1016/j.patcog.2005.10.018.
- Li, L. and Goodchild, M.F., 2011. An optimisation model for linear feature matching in geographical data conflation. International Journal of Image and Data Fusion, 2 (4), 309–328. doi:https://doi.org/10.1080/19479832.2011.577458.
- Li, W., Raskin, R., and Goodchild, M.F., 2012. Semantic similarity measurement based on knowledge mining: an artificial neural net approach. International Journal of Geographical Information Science, 26 (8), 1415–1435. doi:https://doi.org/10.1080/13658816.2011.635595.
- Lin, C., et al., 2018. Building linked data from historical maps. Proceedings of the ISWC 2018 Workshop on Enabling Open Semantic Science (SemSci 2018).Monterey, California, USA, 59–67.
- Liu, T., et al., 2016. A contour-line color layer separation algorithm based on fuzzy clustering and region growing. Computers & Geosciences, 88, 41–53. doi:https://doi.org/10.1016/j.cageo.2015.12.017.
- Liu, T., Xu, P., and Zhang, S., 2018. A review of recent advances in scanned topographic map processing. Neurocomputing, 328, 75–87. doi:https://doi.org/10.1016/j.neucom.2018.02.102
- Lupien, A.E. and Moreland, W.H., 1987. A general approach to map conflation. In: Proceedings of 8th International Symposium on Computer Assisted Cartography (AutoCarto 8). Baltimore, Maryland, USA, 1–10.
- Lynch, M.P. and Saalfeld, A.J., 1985. Conflation: automated map compilation—a video game approach. Proceedings of Auto-Carto 7, Washington, D.C., USA, 343–352.
- Martins, B., 2011. A supervised machine learning approach for duplicate detection over gazetteer records. International Conference on GeoSpatial Sematics. Brest, France, 34–51.
- McKenzie, G., Janowicz, K., and Adams, B., 2014. A weighted multi-attribute method for matching user-generated points of interest. Cartography and Geographic Information Science, 41 (2), 125–137. doi:https://doi.org/10.1080/15230406.2014.880327.
- Miao, Q., et al. 2016. The recognition of the point symbols in the scanned topographic maps. IEEE Transactions on Image Processing, 26 (6), 2751–2766. doi:https://doi.org/10.1109/TIP.2016.2613409.
- Monge, A.E. and Elkan, C., 1996. The field matching problem: algorithms and applications. KDD. Portland, Oregon, USA, 267–270.
- Muhs, S., et al., 2016. Automatic delineation of built-up area at urban block level from topographic maps. Computers, Environment and Urban Systems, 58, 71–84. doi:https://doi.org/10.1016/j.compenvurbsys.2016.04.001.
- Mustière, S. and Devogele, T., 2008. Matching networks with different levels of detail. GeoInformatica, 12 (4), 435–453. doi:https://doi.org/10.1007/s10707-007-0040-1.
- Olteanu-Raimond, A.-M. and Mustière, S., 2008. Data matching–a matter of belief. In: Headway in spatial data handling. Berlin: Springer, 501–519.
- Olteanu-Raimond, A.-M., Mustiere, S., and RUAS, A., 2015. Knowledge formalization for vector data matching using belief theory. Journal of Spatial Information Science, 2015 (10), 21–46.
- Pei, S., et al., 2019. Semi-supervised entity alignment via knowledge graph embedding with awareness of degree difference. The World Wide Web Conference. San Francisco, California, USA, 3130–3136.
- Pezeshk, A. and Tutwiler, R.L., 2011. Automatic feature extraction and text recognition from scanned topographic maps. IEEE Transactions on Geoscience and Remote Sensing, 49 (12), 5047–5063. doi:https://doi.org/10.1109/TGRS.2011.2157697.
- Rada, R., et al. 1989. Development and application of a metric on semantic nets. IEEE Transactions on Systems, Man, and Cybernetics, 19 (1), 17–30. doi:https://doi.org/10.1109/21.24528.
- Regalia, B., Janowicz, K., and McKenzie, G., 2019. Computing and querying strict, approximate, and metrically refined topological relations in linked geographic data. Transactions in GIS, 23 (3), 601–619. doi:https://doi.org/10.1111/tgis.12548.
- Rosen, B. and Saalfeld, A., 1985. Match criteria for automatic alignment. Proceedings of 7th international symposium on computer-assisted cartography (Auto-Carto 7). Washington, D.C., USA, 1–20.
- Ruiz, J.J., et al. 2011. Digital map conflation: a review of the process and a proposal for classification. International Journal of Geographical Information Science, 25 (9), 1439–1466. doi:https://doi.org/10.1080/13658816.2010.519707.
- Saalfeld, A., 1988. Conflation automated map compilation. International Journal of Geographical Information System, 2 (3), 217–228. doi:https://doi.org/10.1080/02693798808927897.
- Saeedimoghaddam, M. and Stepinski, T., 2019. Automatic extraction of road intersection points from USGS historical map series using deep convolutional neural networks. International Journal of Geographical Information Science, 34 (5), 947–968.
- Samal, A., Seth, S., and Cueto, K., 2004. A feature-based approach to conflation of geospatial sources. International Journal of Geographical Information Science, 18 (5), 459–489. doi:https://doi.org/10.1080/13658810410001658076.
- Santos, R., et al. 2018a. Toponym matching through deep neural networks. International Journal of Geographical Information Science, 32 (2), 324–348. doi:https://doi.org/10.1080/13658816.2017.1390119.
- Santos, R., Murrieta-Flores, P., and Martins, B., 2018b. Learning to combine multiple string similarity metrics for effective toponym matching. International Journal of Digital Earth, 11 (9), 913–938. doi:https://doi.org/10.1080/17538947.2017.1371253.
- Scheider, S., Ballatore, A., and Lemmens, R., 2019. Finding and sharing GIS methods based on the questions they answer. International Journal of Digital Earth, 12 (5), 594–613. doi:https://doi.org/10.1080/17538947.2018.1470688.
- Schwering, A., 2008. Approaches to semantic similarity measurement for geo‐spatial data: a survey. Transactions in GIS, 12 (1), 5–29. doi:https://doi.org/10.1111/j.1467-9671.2008.01084.x.
- Sehgal, V., Getoor, L., and Viechnicki, P.D., 2006. Entity resolution in geospatial data integration. Proceedings of the 14th annual ACM international symposium on Advances in geographic information systems. Arlington, Virginia, USA, 83–90.
- Seo, S. and O’Hara, C.G., 2009. Quality assessment of linear data. International Journal of Geographical Information Science, 23 (12), 1503–1525. doi:https://doi.org/10.1080/13658810802231456.
- Shimizu, E. and Fuse, T., 2003. Rubber-sheeting of historical maps in GIS and its application to landscape visualization of old-time cities: focusing on Tokyo of the past. Proceedings of the 8th international conference on computers in urban planning and urban management. Sendai, Japan, 3–8.
- Singhal, A., 2012. Introducing the knowledge graph: things, not strings. Official google blog, 5.
- Song, J., et al. 2016. The reconnection of contour lines from scanned color images of topographical maps based on GPU implementation. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10 (2), 400–408. doi:https://doi.org/10.1109/JSTARS.2016.2580903.
- Song, W., et al. 2008. Automated geospatial conflation of vector road maps to high resolution imagery. IEEE Transactions on Image Processing, 18 (2), 388–400. doi:https://doi.org/10.1109/TIP.2008.2008044.
- Stadler, C., et al. 2012. Linkedgeodata: A core for a web of spatial open data. Semantic Web, 3 (4), 333–354. doi:https://doi.org/10.3233/SW-2011-0052.
- Sun, K., Zhu, Y., and SONG, J., 2019. Progress and challenges on entity alignment of geographic knowledge bases. ISPRS International Journal of Geo-Information, 8 (2), 77–101. doi:https://doi.org/10.3390/ijgi8020077.
- Sun, Z., et al., 2018. Bootstrapping entity alignment with knowledge graph embedding. IJCAI. Stockholm, Sweden, 4396–4402.
- Tong, X., Shi, W., and Deng, S., 2009. A probability-based multi-measure feature matching method in map conflation. International Journal of Remote Sensing, 30 (20), 5453–5472. doi:https://doi.org/10.1080/01431160903130986.
- Trisedya, B.D., Qi, J., and Zhang, R., 2019. Entity alignment between knowledge graphs using attribute embeddings. Proceedings of the AAAI Conference on Artificial Intelligence, Honolulu, Hawaii, USA, 297–304.
- Uhl, J., et al. 2018. Map archive mining: visual-analytical approaches to explore large historical map collections. ISPRS International Journal of Geo-Information, 7 (4), 148. doi:https://doi.org/10.3390/ijgi7040148.
- Van Wijngaarden, F., et al., 1997. Map integration—update propagation in a multi-source environment. Proceedings of the 5th ACM international workshop on Advances in geographic information systems. Las Vegas, Nevada, USA, 71–76.
- Varanka, D.E. and Usery, E.L., 2018. The map as knowledge base. International Journal of Cartography, 4 (2), 201–223. doi:https://doi.org/10.1080/23729333.2017.1421004.
- Volz, S., 2006. An iterative approach for matching multiple representations of street data. International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 36 (Part 2/W40), 101–110.
- Walter, V. and Fritsch, D., 1999. Matching spatial data sets: a statistical approach. International Journal of Geographical Information Science, 13 (5), 445–473. doi:https://doi.org/10.1080/136588199241157.
- Wang, C., et al., 2018. Information extraction and knowledge graph construction from geoscience literature. Computers & Geosciences, 112, 112–120. doi:https://doi.org/10.1016/j.cageo.2017.12.007.
- Wang, S., et al. 2019. Geographic knowledge graph (GeoKG): a formalized geographic knowledge representation. ISPRS International Journal of Geo-Information, 8 (4), 184–207. doi:https://doi.org/10.3390/ijgi8040184.
- White, M.S., Jr and Griffin, P., 1985. Piecewise linear rubber-sheet map transformation. The American Cartographer, 12 (2), 123–131. doi:https://doi.org/10.1559/152304085783915135.
- Xavier, E.M., Ariza-López, F.J., and Ureña-Cámara, M.A., 2016. A survey of measures and methods for matching geospatial vector datasets. ACM Computing Surveys (CSUR), 49 (2), 1–34. doi:https://doi.org/10.1145/2963147.
- Xu, P., et al., 2016. Graphic-based character grouping in topographic maps. Neurocomputing, 189, 160–170. doi:https://doi.org/10.1016/j.neucom.2015.12.094.
- Yan, B., et al. 2019. A spatially explicit reinforcement learning model for geographic knowledge graph summarization. Transactions in GIS, 23 (3), 620–640. doi:https://doi.org/10.1111/tgis.12547.
- Yu, L., et al. 2018. A holistic approach to aligning geospatial data with multidimensional similarity measuring. International Journal of Digital Earth, 11 (8), 845–862. doi:https://doi.org/10.1080/17538947.2017.1359688.
- Yuan, M., 2001. Representing complex geographic phenomena in GIS. Cartography and Geographic Information Science, 28 (2), 83–96. doi:https://doi.org/10.1559/152304001782173718.
- Yuan, S. and Tao, C., 1999. Development of conflation components. Proceedings of Geoinformatics, 99, 1–13.
- Zhang, M., 2009. Methods and implementations of road-network matching. München: Technische Universität München.
- Zhang, M. and Meng, L., 2007. An iterative road-matching approach for the integration of postal data. Computers, Environment and Urban Systems, 31 (5), 597–615. doi:https://doi.org/10.1016/j.compenvurbsys.2007.08.008.
- Zhang, N., et al. 2018. Structured knowledge base as prior knowledge to improve urban data analysis. ISPRS International Journal of Geo-Information, 7 (7), 264. doi:https://doi.org/10.3390/ijgi7070264.
- Zhang, X., et al., 2012. Pattern classification approaches to matching building polygons at multiple scales. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume I-2, XXII ISPRS Congress, August-September. Melbourne, Australia, 19–24.
- Zheng, W., et al., 2019. Interactive natural language question answering over knowledge graphs. Information Sciences, 481, 141–159. doi:https://doi.org/10.1016/j.ins.2018.12.032.