Abstract
With the proliferation of the Semantic Web technologies, more and more spatial knowledge bases are being published on the Web. Discovering spatial links among spatial knowledge bases is crucial in achieving real-time applications such as reasoning and question answering over spatial linked data. However, existing approaches rely on numerous high-cost Dimensionally Extended Nine-Intersection Model (DE-9IM) computations which lead to inefficient spatial link discovery. To address this problem, we propose a novel approach for discovering topological relations based on the spatial link composition, namely DORIC. Different from conventional spatial link discovery methods, DORIC further reduces the required number of DE-9IM computations by composing existing spatial links. Specifically, we first propose a spatial link composition (SLC) model to infer new spatial links of topological relations from existing or intermediate links. We replace part of high-cost DE-9IM computations with relatively low-cost SLC, and it leads to reduced spatial link discovery time. Then to maximize the utility of SLC during the process of DORIC, we design two effective strategies for deciding the discovery and access orders. Experiments on three real-world datasets show that the proposed DORIC outperforms the state-of-the-art approaches in terms of the spatial link discovery time.







Similar content being viewed by others
Notes
References
Ahmed AF, Sherif MA, Ngomo ACN (2018a) On the effect of geometries simplification on geo-spatial link discovery. Procedia Comput Sci 137:139–150
Ahmed AF, Sherif MA, Ngomo ACN (2018b) Radon2: a buffered-intersection matrix computing approach to accelerate link discovery over geo-spatial rdf knowledge bases. In: Proceedings of international workshop on ontology matching (OM), p 197
Araujo S, Hidders J, de Vries AP, Schwabe D (2011) Serimi: resource description similarity, rdf instance matching and interlinking. In: Proceedings of the interantional conference on ontology matching (OM), pp 246–247
Chang L, Li W, Qin L, Zhang W, Yang S (2017) pscan: fast and exact structural graph clustering. IEEE Trans Knowl Data Eng 29(2):387–401
Chu X, Ilyas IF, Papotti P, Ye Y (2014) Ruleminer: data quality rules discovery. In: Proceedings of the interantional conference on data engineering (ICDE). IEEE, pp 1222–1225
Clementini E, Sharma J, Egenhofer MJ (1994) Modelling topological spatial relations: strategies for query processing. Comput Graph 18(6):815–822
Della Penna G, Magazzeni D, Orefice S (2017) A formal framework to represent spatial knowledge. Knowl Inf Syst 51(1):311–338
Dellal I, Jean S, Hadjali A, Chardin B, Baron M (2019) Query answering over uncertain RDF knowledge bases: explain and obviate unsuccessful query results. Knowl Inf Syst. https://doi.org/10.1007/s10115-019-01332-7
Faria D, Balasubramani BS, Shivaprabhu VR, Mott I, Pesquita C, Couto FM, Cruz IF (2017) Results of AML in OAEI 2017. In: Proceedings of the international workshop on ontology matching (OM), pp 122–128
Hartung M, Groß A, Rahm E (2013) Composition methods for link discovery. In: Proceedings of the datenbanksysteme for business, technologie and web (BTW). Citeseer, pp 261–277
Koubarakis M, Kyzirakos K (2010) Modeling and querying metadata in the semantic sensor web: the model strdf and the query language stsparql. In: Proceedings of the extended semantic web conference (ESWC). Springer, pp 425–439
Kyzirakos K, Karpathiotakis M, Koubarakis M (2012) Strabon: a semantic geospatial dbms. In: Proceedings of the international semantic web conference (ISWC). Springer, pp 295–311
Kyzirakos K, Vlachopoulos I, Savva D, Manegold S, Koubarakis M (2014) Geotriples: a tool for publishing geospatial data as RDF graphs using r2rml mappings. In: Proceedings of the international semantic web conference (ISWC) posters & demos, pp 393–396
Lashkari F, Bagheri E, Ghorbani AA (2019) Neural embedding-based indices for semantic search. Inf Process Manag 56(3):733–755
Li S, Ying M (2003) Region connection calculus: its models and composition table. Artif Intell 145(1–2):121–146
Li S, Long Z, Liu W, Duckham M, Both A (2015) On redundant topological constraints. Artif Intell 225:51–76
Nentwig M, Hartung M, Ngonga Ngomo AC, Rahm E (2017) A survey of current link discovery frameworks. Semantic Web 8(3):419–436
Ngomo ACN (2012) Link discovery with guaranteed reduction ratio in affine spaces with minkowski measures. In: Proceedings of the international semantic web conference (ISWC). Springer, pp 378–393
Ngomo ACN (2013) Orchid–reduction-ratio-optimal computation of geo-spatial distances for link discovery. In: Proceedings of the international semantic web conference (ISWC). Springer, pp 395–410
Ngomo ACN, Auer S (2011) Limes-a time-efficient approach for large-scale link discovery on the web of data. In: Proceedings of the international joint conference on artificial intelligence (IJCAI), pp 2312–2317
Nikolov A, Uren V, Motta E (2007) Knofuss: a comprehensive architecture for knowledge fusion. In: Proceedings of the international conference on knowledge capture (K-CAP). ACM, pp 185–186
Niu X, Rong S, Zhang Y, Wang H (2011) Zhishi. links results for oaei 2011. In: Proceedings of the international conference on ontology matching (OM)
O’Rourke J (1985) Finding minimal enclosing boxes. Int J Comput Inf Sci 14(3):183–199
Otegi A, Arregi X, Ansa O, Agirre E (2015) Using knowledge-based relatedness for information retrieval. Knowl Inf Syst 44(3):689–718
Pan JZ (2009) Resource description framework. In: Handbook on ontologies. Springer, pp 71–90
Patroumpas K, Giannopoulos G, Athanasiou S (2014) Towards geospatial semantic data management: strengths, weaknesses, and challenges ahead. In: Proceedings of the international conference on advances in geographic information systems (SIGSPATIAL). ACM, pp 301–310
Perry M, Herring J (2019-04) OGC geosparql-a geographic query language for rdf data. https://www.opengeospatial.org/standards/geosparql
Prud E, Seaborne A, et al. (2019-04) Sparql query language for rdf. https://www.w3.org/TR/rdf-sparql-query
Randell DA, Cui Z, Cohn AG (1992) A spatial logic based on regions and connection. In: Proceedings of the international conference on principles of knowledge representation and reasoning (KR), pp 165–176
Salas J, Harth A (2011) Finding spatial equivalences accross multiple rdf datasets. In: Proceedings of the terra cognita workshop on foundations, technologies and applications of the geospatial web. Citeseer, pp 114–126
Santipantakis G, Doulkeridis C, Vouros GA, Vlachou A (2018) Masklink: efficient link discovery for spatial relations via masking areas. arXiv:1803.01135
Santipantakis GM, Glenis A, Doulkeridis C, Vlachou A, Vouros GA (2019) STLD: towards a spatio-temporal link discovery framework. In: Proceedings of the International workshop on semantic big data (SBD), pp 1–6
Santipantakis GM, Doulkeridis C, Vlachou A, Vouros GA (2020) Integrating data by discovering topological and proximity relations among spatiotemporal entities. In: Big data analytics for time-critical mobility forecasting. Springer, pp 155–179
Saveta T, Fundulaki I, Flouris G, Ngonga-Ngomo AC (2018) SPGEN: a benchmark generator for spatial link discovery tools. In: Proceedings of the international semantic web conference (ISWC). Springer, pp 408–423
Schmachtenberg M, Bizer C, Paulheim H (2014) Adoption of the linked data best practices in different topical domains. In: Proceedings of the international semantic web conference (ISWC). Springer, pp 245–260
Sehgal V, Getoor L, Viechnicki PD (2006) Entity resolution in geospatial data integration. In: Proceedings of the international conference on advances in geographic information systems (SIGSPATIAL). ACM, pp 83–90
Sherif MA, Dreßler K, Smeros P, Ngomo ACN (2016) Annex: Radon-rapid discovery of topological relations. arXiv:1611.06128
Sherif MA, Dreßler K, Smeros P, Ngomo ACN (2017) Radon-rapid discovery of topological relations. In: Proceedings of the AAAI conference on artificial intelligence (AAAI), pp 175–181
Shin S, Jin X, Jung J, Lee KH (2019) Predicate constraints based question answering over knowledge graph. Inf Process Manag 56(3):445–462
Smeros P, Koubarakis M (2016) Discovering spatial and temporal links among RDF data. In: Proceedings of the web conference (WWW) workshop
Stell JG (2000) Boolean connection algebras: a new approach to the region-connection calculus. Artif Intellig 122(1–2):111–136
Tang X, Chen L, Cui J, Wei B (2019) Knowledge representation learning with entity descriptions, hierarchical types, and textual relations. Inf Process Manag 56(3):809–822
Vilches-Blázquez LM, Saquicela V, Corcho O (2012) Interlinking geospatial information in the web of data. In: Bridging the geographic information sciences. Springer, pp 119–139
Wang S, Tang J, Morstatter F, Liu H (2016) Paired restricted boltzmann machine for linked data. In: Proceedings of the international conference on Information and knowledge management (CIKM), ACM, pp 1753–1762
Yu J, Tao D, Wang M, Rui Y (2015) Learning to rank using user clicks and visual features for image retrieval. IEEE Trans Cybern 45(4):767–779
Yu J, Yang X, Gao F, Tao D (2017) Deep multimodal distance metric learning using click constraints for image ranking. IEEE Trans Cybern 47(12):4014–4024
Zhang J, Chen J, Zhi S, Chang Y, Philip SY, Han J (2017) Link prediction across aligned networks with sparse and low rank matrix estimation. In: Proceedings of the international conference on data engineering (ICDE). IEEE, pp 971–982
Zheng W, Cheng H, Zou L, Yu JX, Zhao K (2017) Natural language question/answering: Let users talk with the knowledge graph. In: Proceedings of the international conference on information and knowledge management (CIKM). ACM, pp 217–226
Acknowledgements
This work was supported by the BK21 international joint research fund by Yonsei Graduate School and the National Research Foundation of Korea(NRF) grant funded by the Korea government (MSIP; Ministry of Science, ICT & Future Planning) (No. NRF-2019R1A2B5B01070555), Open Project Program of State Key Laboratory of Virtual Reality Technology and Systems, Beihang University (No. VRLAB2021C01), and Xiamen Youth Innovation Fund Project (No. 3502Z20206072).
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Jin, X., Eom, S., Shin, S. et al. DORIC: discovering topological relations based on spatial link composition. Knowl Inf Syst 63, 2645–2669 (2021). https://doi.org/10.1007/s10115-021-01603-2
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10115-021-01603-2