Skip to main content
SpringerLink
Log in
Book cover

International Conference on Smart Vehicular Technology, Transportation, Communication and Applications

VTCA 2018: Advances in Smart Vehicular Technology, Transportation, Communication and Applications pp 11–18Cite as

Geo-spatial Ontology Matching Through Compact Evolutionary Algorithm

  • Conference paper
  • First Online:

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 128))

Abstract

Geo-spatial ontologies can provide a formal description of concepts, relationships, activities, features and rules in GIS domain. However, simply use them only allows to partially solve semantic conflicts, and does not completely solve heterogeneity issues that are caused by themselves. Geo-spatial ontology matching technique can find the correspondences between semantic identical entities, and solve the heterogeneous problem between two geo-spatial ontologies. Be inspired by the successful application of Evolutionary Algorithm (EA) in instance matching domain, in this paper, it is utilized to match the heterogeneous geo-spatial ontologies. To reduce the runtime and memory consumption required by EA, a compact version of it is presented, which does not work on the whole population but a probability representation on it. In addition, a geo-spatial similarity measure is presented to determine the identical geo-spatial entities, and an optimal model is constructed for geo-spatial ontology matching problem. The experimental results show that cEA-based geo-spatial ontology matching technique can efficiently determine the alignment.

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

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD   169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Notes

  1. 1.

    https://project-hobbit.eu/challenges/om2017/.

References

  1. Achichi, M., et al.: Results of the ontology alignment evaluation initiative 2017. In: OM 2017-12th ISWC Workshop on Ontology Matching, pp. 61–113 (2017). No commercial editor

    Google Scholar 

  2. An, Y., Zhao, B.: Geo ontology design and comparison in geographic information integration. In: 2007 Fourth International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2007, vol. 4, pp. 608–612. IEEE (2007)

    Google Scholar 

  3. Isele, R., Jentzsch, A., Bizer, C.: Efficient multidimensional blocking for link discovery without losing recall. In: WebDB (2011)

    Google Scholar 

  4. Jaziri, W., Chaabane, S., Sassi, N.: GeOnt: geo-ontologies integration tool. Int. J. Metadata Semant. Ontol. 12(2–3), 155–166 (2017)

    Article  Google Scholar 

  5. Miller, G.A.: WordNet: a lexical database for english. Commun. ACM 38(11), 39–41 (1995)

    Article  Google Scholar 

  6. Ngomo, A.C.N.: Orchid-reduction-ratio-optimal computation of geo-spatial distances for link discovery, pp. 395–410. Springer (2013)

    Google Scholar 

  7. Rijsberge, C.J.V.: Information Retrieval. University of Glasgow, Butterworth (1975)

    Google Scholar 

  8. Siricharoen, W.V., Pakdeetrakulwong, U.: A survey on ontology-driven geographic information systems. In: 2014 Fourth International Conference on Digital Information and Communication Technology and it’s Applications (DICTAP), pp. 180–185. IEEE (2014)

    Google Scholar 

  9. Stoilos, G., Stamou, G., Kollias, S.: A string metric for ontology alignment, pp. 623–637, Galway, Ireland, November 2005

    Google Scholar 

  10. Winkler, W.: The state record linkage and current research problems. Technical report. RR99-04, Statistics of Income Division, Washington DC, USA (1999)

    Google Scholar 

  11. Xue, X., Wang, Y.: Optimizing ontology alignments through a memetic algorithm using both matchfmeasure and unanimous improvement ratio. Artif. Intell. 223, 65–81 (2015)

    Article  MathSciNet  Google Scholar 

  12. Xue, X., Wang, Y.: Using memetic algorithm for instance coreference resolution. IEEE Trans. Knowl. Data Eng. 28(2), 580–591 (2016)

    Article  Google Scholar 

Download references

Acknowledgment

This work is supported by the National Natural Science Foundation of China (No. 61503082), Natural Science Foundation of Fujian Province (No. 2016J05145), Scientific Research Development Foundation of Fujian University of Technology (Nos. GY-Z17162 and GY-Z15007) and Fujian Province Outstanding Young Scientific Researcher Training Project (No. GY-Z160149).

Author information

Authors and Affiliations

  1. College of Information Science and Engineering, Fujian University of Technology, Fuzhou, 350118, Fujian, China

    Xingsi Xue & Jianhua Liu

  2. Intelligent Information Processing Research Center, Fujian University of Technology, Fuzhou, 350118, Fujian, China

    Xingsi Xue & Jianhua Liu

  3. Fujian Provincial Key Laboratory of Big Data Mining and Applications, Fujian University of Technology, Fuzhou, 350118, Fujian, China

    Xingsi Xue & Jianhua Liu

  4. Fujian Key Lab for Automotive Electronics and Electric Drive, Fujian University of Technology, Fuzhou, 350118, Fujian, China

    Xingsi Xue

Authors
  1. Xingsi Xue
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jianhua Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xingsi Xue .

Editor information

Editors and Affiliations

  1. College of Physics and Energy, Fujian Normal University, Fuzhou, Fujian, China

    Yong Zhao

  2. College of Information Science and Engineering, Fujian University of Technology, Fuzhou, Fujian, China

    Tsu-Yang Wu

  3. School of Transportation, Fujian University of Technology, Fuzhou, Fujian, China

    Tang-Hsien Chang

  4. College of Information Science and Engineering, Fujian University of Technology, Fuzhou, Fujian, China

    Jeng-Shyang Pan

  5. University of Technology Sydney, Sydney, NSW, Australia

    Lakhmi C. Jain

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Xue, X., Liu, J. (2019). Geo-spatial Ontology Matching Through Compact Evolutionary Algorithm. In: Zhao, Y., Wu, TY., Chang, TH., Pan, JS., Jain, L. (eds) Advances in Smart Vehicular Technology, Transportation, Communication and Applications. VTCA 2018. Smart Innovation, Systems and Technologies, vol 128. Springer, Cham. https://doi.org/10.1007/978-3-030-04585-2_2

Download citation

Publish with us

Policies and ethics

Search

Navigation