Skip to main content
SpringerLink
Log in

DORIC: discovering topological relations based on spatial link composition

  • Regular Paper
  • Published:
Knowledge and Information Systems Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

Notes

  1. http://wiki.dbpedia.org/about.

  2. www.yago-knowledge.org/.

  3. https://developers.google.com/freebase.

  4. https://virtuoso.openlinksw.com.

  5. https://jena.apache.org/documentation/fuseki2.

  6. https://lod-cloud.net.

  7. http://linkedgeodata.org.

  8. https://www.openstreetmap.org.

  9. http://www.linkedopendata.gr/dataset.

  10. http://nuts.geovocab.org/data/0.91/.

  11. http://linkedopendata.gr/dataset/greek-administrative-geography.

  12. https://datahub.ckan.io/dataset/corine-land-cover12.

References

  1. Ahmed AF, Sherif MA, Ngomo ACN (2018a) On the effect of geometries simplification on geo-spatial link discovery. Procedia Comput Sci 137:139–150

    Article  Google Scholar 

  2. 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

  3. 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

  4. 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

    Article  Google Scholar 

  5. 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

  6. Clementini E, Sharma J, Egenhofer MJ (1994) Modelling topological spatial relations: strategies for query processing. Comput Graph 18(6):815–822

    Article  Google Scholar 

  7. Della Penna G, Magazzeni D, Orefice S (2017) A formal framework to represent spatial knowledge. Knowl Inf Syst 51(1):311–338

    Article  Google Scholar 

  8. 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

    Article  Google Scholar 

  9. 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

  10. 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

  11. 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

  12. 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

  13. 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

  14. Lashkari F, Bagheri E, Ghorbani AA (2019) Neural embedding-based indices for semantic search. Inf Process Manag 56(3):733–755

    Article  Google Scholar 

  15. Li S, Ying M (2003) Region connection calculus: its models and composition table. Artif Intell 145(1–2):121–146

    Article  MathSciNet  Google Scholar 

  16. Li S, Long Z, Liu W, Duckham M, Both A (2015) On redundant topological constraints. Artif Intell 225:51–76

    Article  MathSciNet  Google Scholar 

  17. Nentwig M, Hartung M, Ngonga Ngomo AC, Rahm E (2017) A survey of current link discovery frameworks. Semantic Web 8(3):419–436

    Article  Google Scholar 

  18. 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

  19. 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

  20. 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

  21. 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

  22. 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)

  23. O’Rourke J (1985) Finding minimal enclosing boxes. Int J Comput Inf Sci 14(3):183–199

  24. Otegi A, Arregi X, Ansa O, Agirre E (2015) Using knowledge-based relatedness for information retrieval. Knowl Inf Syst 44(3):689–718

    Article  Google Scholar 

  25. Pan JZ (2009) Resource description framework. In: Handbook on ontologies. Springer, pp 71–90

  26. 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

  27. Perry M, Herring J (2019-04) OGC geosparql-a geographic query language for rdf data. https://www.opengeospatial.org/standards/geosparql

  28. Prud E, Seaborne A, et al. (2019-04) Sparql query language for rdf. https://www.w3.org/TR/rdf-sparql-query

  29. 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

  30. 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

  31. Santipantakis G, Doulkeridis C, Vouros GA, Vlachou A (2018) Masklink: efficient link discovery for spatial relations via masking areas. arXiv:1803.01135

  32. 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

  33. 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

  34. 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

  35. 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

  36. 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

  37. Sherif MA, Dreßler K, Smeros P, Ngomo ACN (2016) Annex: Radon-rapid discovery of topological relations. arXiv:1611.06128

  38. 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

  39. Shin S, Jin X, Jung J, Lee KH (2019) Predicate constraints based question answering over knowledge graph. Inf Process Manag 56(3):445–462

    Article  Google Scholar 

  40. Smeros P, Koubarakis M (2016) Discovering spatial and temporal links among RDF data. In: Proceedings of the web conference (WWW) workshop

  41. Stell JG (2000) Boolean connection algebras: a new approach to the region-connection calculus. Artif Intellig 122(1–2):111–136

    Article  MathSciNet  Google Scholar 

  42. 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

    Article  Google Scholar 

  43. 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

  44. 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

  45. 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

    Article  Google Scholar 

  46. 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

    Article  Google Scholar 

  47. 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

  48. 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

Download references

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

  1. Department of Computer Science, Yonsei University, Seoul, Republic of Korea

    Xiongnan Jin, Sungkwang Eom, Sangjin Shin & Kyong-Ho Lee

  2. College of Computer and Information Engineering, Xiamen University of Technology, Xiamen, China

    Chaoqun Hong

Authors
  1. Xiongnan Jin
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sungkwang Eom
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sangjin Shin
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Kyong-Ho Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Chaoqun Hong
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Kyong-Ho Lee.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

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

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10115-021-01603-2

Keywords

Search

Navigation