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GeoInformatica

, Volume 22, Issue 2, pp 269–305 | Cite as

A novel computational knowledge-base framework for visualization and quantification of geospatial metadata in spatial data infrastructures

  • Gangothri Rajaram
  • Harish Chandra Karnatak
  • Swaminathan Venkatraman
  • K. R. Manjula
  • Kannan Krithivasan
Article
  • 315 Downloads

Abstract

Advances in Metadata research have been instrumental in predictions and ‘fitness-of-use evaluation’ for the effective Decision-making process. For the past two decades, the model has been developed to provide visual assistance for assessing the quality information in metadata and quantifying the degree of metadata population. Still, there is a need to develop a framework that can be generic to adopt all the standards available for Geospatial Metadata. The computational analysis of metadata for specific applications remains uncharted for investigations and studies. This work proposes a computational framework for Geospatial Metadata by integrating TopicMaps and Hypergraphs (HXTM) based on the elements and their dependency relationships. A purpose-built dataset extracted from schemas of various standardisation organisations and existing knowledge in the discipline is utilised to model the framework and thereby evaluate ranking strategies. Hypergraph-Helly Property based Weight-Assignment Algorithm (HHWA) have been proposed for HXTM framework to calculate Stable weights for Metadata Elements. Recursive use of Helly-property ensures predominant elements, while Rank Order Centroid (ROC) method is used to compute standard weights. A real corpus using case studies from FGDC’s Standard for Geospatial Metadata, INSPIRE Metadata Standards, and ISRO Metadata Content Standard (NSDI 2.0) is used to validate the proposed framework. The observations show that the Information Gain (Entropy) of the proposed model along with the algorithm proves to be computationally smart for quantification purposes and visualises the strength of Metadata Elements for all applications. A prototype tool, ‘MetDEVViz- MetaData Editor, Validator & Visualization’ is designed to exploit the benefits of the proposed algorithm for the case studies that acts as a web service to provide a user interface for editing, validating and visualizing metadata elements.

Keywords

Metadata TopicMap Hypergraph Helly property Computational intelligence MetDEVViz 

Notes

Acknowledgments

The authors thank SASTRA University for their financial and research support. The third and fifth author thanks the Department of Science and Technology - Fund for Improvement of S&T Infrastructure in Universities and Higher Educational Institutions Government of India (SR/FST/MSI-107/2015) for their financial support. The authors thank Dr.P.S.Roy, NASI Senior Scientist Platinum Jubilee Fellow in Centre for Earth & Space Sciences at the University of Hyderabad for the fruitful discussions, ample interactions and advice for the completion of the manuscript. The authors thank Dr. G. Ravishankar, Head, Land Use and Cover Monitoring Division, National Remote Sensing Centre, ISRO, Hyderabad for his valuable discussions.

Supplementary material

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References

  1. 1.
    Gill T (2008) Metadata and the web. Introduction to metadata 3:20–38Google Scholar
  2. 2.
    Priyank J, Gyanchandani M, Khare N (2016) Big data privacy: a technological perspective and review. Journal of Big Data 3.1:25.2Google Scholar
  3. 3.
    Timpf S, Raubal M, Kuhn W (1997) Experiences with metadata. Proceedings of the 7th International Symposium on Spatial Data Handling. Technical University of Vienna, Department for GeoinformationGoogle Scholar
  4. 4.
    Kalantari M, Rajabifard A, Olfat H, Williamson I (2014) Geospatial metadata 2.0 – an approach for volunteered geographic information. Comput Environ Urban Syst 48:35–48.  https://doi.org/10.1016/j.compenvurbsys.2014.06.005 CrossRefGoogle Scholar
  5. 5.
    Rajabifard A, Feeney M-E, Williamson I (2002) Directions for the future of SDI development. Int J Appl Earth Obs Geoinf 4(1):11–22.  https://doi.org/10.1016/S0303-2434(02)00002-8 CrossRefGoogle Scholar
  6. 6.
    Xu B, Yan S, Wang Q, Lian J, Wu X, Ding K (2014) Geospatial data infrastructure: the development of metadata for geo-information in China. IOP Conf Ser Earth Environ Sci 17:12259.  https://doi.org/10.1088/1755-1315/17/1/012259 CrossRefGoogle Scholar
  7. 7.
    Han W, Di L, Yu G, Shao Y, Kang L (2016) Investigating metrics of geospatial web services: the case of a CEOS federated catalog service for earth observation data. Comput Geosci 92:1–8.  https://doi.org/10.1016/j.cageo.2016.04.005 CrossRefGoogle Scholar
  8. 8.
    Swaminathan V, Rajaram G, Abhishek V, Reddy BS, Kannan K (2017) A novel hypergraph-based genetic algorithm (HGGA) built on unimodular and anti-homomorphism properties for DNA sequencing by hybridization. Interdiscip Sci Comput Life Sci 1–15.  https://doi.org/10.1007/s12539-017-0267-y
  9. 9.
    Ochoa X (2011) Learnometrics: metrics for learning objects. Proceedings of the 1st International Conference on Learning Analytics and Knowledge. ACMGoogle Scholar
  10. 10.
    Ochoa X, Duval E (2006) Towards automatic evaluation of learning object metadata quality. In International Conference on Conceptual Modeling. Springer, Berlin, Heidelberg, pp 372–381Google Scholar
  11. 11.
    Bertini E, Tatu A, Keim D (2011) Quality metrics in high-dimensional data visualization: an overview and systemization. IEEE Trans Vis Comput Graph 17(6):2203–2212.  https://doi.org/10.1109/TVCG.2011.229 CrossRefGoogle Scholar
  12. 12.
    Sanz-Rodriguez J, Dodero JM, Sanchez-Alonso S (2011) Metrics-based evaluation of learning object reusability. Softw Qual J 19(1):121–140.  https://doi.org/10.1007/s11219-010-9108-5 CrossRefGoogle Scholar
  13. 13.
    Xia J (2012) Metrics to measure open geospatial data quality. Issues Sci Technol Librariansh 68:1–9Google Scholar
  14. 14.
    Margaritopoulos T, Margaritopoulos M, Mavridis I, Manitsaris A (2009) A fine-grained metric system for the completeness of metadata. Commun Comput Inf Sci 46:83–94Google Scholar
  15. 15.
    Ellouze N, Lammari N, Métais E (2012) CITOM: an incremental construction of multilingual topic maps. Data Knowl Eng 7:46–62CrossRefGoogle Scholar
  16. 16.
    Bouzid S, Cauvet C, Pinaton J (2012) A topic-map-based framework to enhance components’ retrieval in a process control. Proc. 14th Int. Conf. Enterp. Inf Syst:146–149Google Scholar
  17. 17.
    Rath H (2002) Topic maps and the ontological world. DevelopmentGoogle Scholar
  18. 18.
    Kannan R (2010) Topic map: an ontology framework for information retrieval. arXiv preprint arXiv:1003.3530, pp 195–198Google Scholar
  19. 19.
    Garshol LM (2003) Living with topic maps and RDF – topic maps, RDF, DAML, OIL, OWL, TMCL XML Eur 2003Google Scholar
  20. 20.
    Pepper S (2007) Expressing Dublin core metadata in topic maps. International Conference on Topic Map Research and Applications. Springer, Berlin, HeidelbergGoogle Scholar
  21. 21.
    Auillans P et al (2002) A formal model for topic maps. International semantic web conference. Springer, Berlin, HeidelbergGoogle Scholar
  22. 22.
    Dong Y, Li M (2004) HyO-XTM: a set of hyper-graph operations on XML topic map toward knowledge management. Futur Gener Comput Syst 20(1):81–100.  https://doi.org/10.1016/S0167-739X(03)00166-3 CrossRefGoogle Scholar
  23. 23.
    An L, Chen X, Yang S (2016) Person re-identification via hypergraph-based matching. Neurocomputing 182:247–254.  https://doi.org/10.1016/j.neucom.2015.12.029 CrossRefGoogle Scholar
  24. 24.
    Zhu Y et al (2016) Heterogeneous hypergraph embedding for document recommendation. Neurocomputing 216:150–162CrossRefGoogle Scholar
  25. 25.
    Xiong S, Ji D (2016) Query-focused multi-document summarization using hypergraph-based ranking. Inf Process Manag 52(4):670–681.  https://doi.org/10.1016/j.ipm.2015.12.012 CrossRefGoogle Scholar
  26. 26.
    Liu H et al (2011) A hypergraph-based method for discovering semantically associated itemsets. Data Mining (ICDM), 2011 I.E. 11th International Conference on. IEEEGoogle Scholar
  27. 27.
    Zhang Z, Bai L, Liang Y, Hancock E (2017) Joint hypergraph learning and sparse regression for feature selection. Pattern Recogn 63:291–309.  https://doi.org/10.1016/j.patcog.2016.06.009 CrossRefGoogle Scholar
  28. 28.
    Kannan K, Kanna BR, Aravindan C (2010) Root mean square filter for noisy images based on hypergraph model. Image Vis Comput 28(9):1329–1338.  https://doi.org/10.1016/j.imavis.2010.01.013 CrossRefGoogle Scholar
  29. 29.
    Jing P et al (2018) HyperSSR: a hypergraph based semi-supervised ranking method for visual search reranking. Neurocomputing 274:50–57CrossRefGoogle Scholar
  30. 30.
    Theodoridis A, Kotropoulos C, Panagakis Y (2013) Music recommendation using hypergraphs and group sparsity. Acoustics, Speech and Signal Processing (ICASSP), 2013 I.E. International Conference on. IEEEGoogle Scholar
  31. 31.
    L. Zhen and Z. Jiang, “Hy-SN: hyper-graph based semantic network,” Knowledge-Based Syst, vol. 23, no. 8, pp. 809–816, 2010, dois:  https://doi.org/10.1016/j.knosys.2010.05.005
  32. 32.
    Batcheller JK (2008) Automating geospatial metadata generation-An integrated data management and documentation approach. Comput Geosci 34(4):387–398.  https://doi.org/10.1016/j.cageo.2007.04.001 CrossRefGoogle Scholar
  33. 33.
    Longhorn RA (2005) Geospatial standards, interoperability, metadata semantics and spatial data infrastructure. NIEeS Work Act Metadata 2005:23Google Scholar
  34. 34.
    Chen YN, Wen CY, Chen HP, Lin YH, Sum HC (2011) Metrics for metadata quality assurance and their implications for digital libraries. Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes bioinformatics), vol. 7008 LNCS, pp 138–147Google Scholar
  35. 35.
    Ochoa X, Duval E (2009) Automatic evaluation of metadata quality in digital repositories. Int J Digit Libr 10(2):67–91.  https://doi.org/10.1007/s00799-009-0054-4 CrossRefGoogle Scholar
  36. 36.
    Margaritopoulos M, Margaritopoulos T, Mavridis I, Manitsaris A (2012) Quantifying and measuring metadata completeness. J Am Soc Inf Sci Technol 63(4):724–737.  https://doi.org/10.1002/asi.21706 CrossRefGoogle Scholar
  37. 37.
    Ochoa X, Duval E (2006) Quality metrics for learning object metadata. World Conf Educ Multimedia, Hypermedia Telecommun 2004:1004–1011Google Scholar
  38. 38.
    Tolosana-calasanz R et al (2006) “On the Problem of Identifying the Quality of Geographic Metadata,” 10th Eur. Conf Digit Libr 4172:232–243Google Scholar
  39. 39.
    Gangothri R (2016) Hybrid model based uncertainty analysis for geospatial metadata supporting decision making for spatial explorationGoogle Scholar
  40. 40.
    Garshol LM (2002) XML.com: what are topic maps. XML.com , Available: http://www.xml.com/lpt/a/1029
  41. 41.
    Le Grand B, Soto M. Visualisation of the semantic web: topic maps visualisation. Information Visualisation, 2002. Proceedings. Sixth International Conference on. IEEEGoogle Scholar
  42. 42.
    Le Grand B, Soto M (2000) Information management - topic maps visualization introduction:basictopicmapsconcepts. In XML Europe vol 2000Google Scholar
  43. 43.
    Garshol LM (2004) Metadata? Thesauri? Taxonomies? Topic maps! Making sense of it all. J Inf Sci 30(4):378–391.  https://doi.org/10.1177/0165551504045856 CrossRefGoogle Scholar
  44. 44.
    Hunting S, Hunting SB-EDC (2003) XML topic maps: creating and using topic maps for the Web. Addison-Wesley, BostonGoogle Scholar
  45. 45.
    Wu Y, Dunaway DJ (2013) Creating a large topic map by integrating Wandora and Ontopia. Libr Hi Tech 31(1):64–75.  https://doi.org/10.1108/07378831311303930 CrossRefGoogle Scholar
  46. 46.
    Xiao G et al (2016) Hypergraph modelling for geometric model fitting. Pattern Recogn 60:748–760Google Scholar
  47. 47.
    Köbler J, Kuhnert S, Verbitsky O (2016) On the isomorphism problem for Helly circular-arc graphs. Inf Comput 247:266–277.  https://doi.org/10.1016/j.ic.2016.01.006 CrossRefGoogle Scholar
  48. 48.
    Goldmann C, Klar B, Meintanis SG (2015) Data transformations and goodness-of-fit tests for type-II right censored samples. Metrika 78(1):59–83.  https://doi.org/10.1007/s00184-014-0490-z CrossRefGoogle Scholar
  49. 49.
    Gingrich P (1992) Chapter 10 chi-square test. Introductory statistics for the social sciences. Department of Sociology and Social Sciences, University of ReginaGoogle Scholar
  50. 50.
    Services C, Profile A, Senkler K (2004) ISO19115 / ISO19119 application profile for CSW 2. 0. pp 1–89Google Scholar
  51. 51.
    Stvilia B, Gasser L (2008) Value-based metadata quality assessment. Libr Inf Sci Res 30(1):67–74.  https://doi.org/10.1016/j.lisr.2007.06.006 CrossRefGoogle Scholar
  52. 52.
    Goodchild MF (2009) The quality of geospatial context. Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 5786 LNCS, pp 15–24Google Scholar
  53. 53.
    Federal Geographic Data Committee (1998) Content standard for geospatial metadata. https://www.fgdc.gov/metadata/csdgm/. Accessed 3 Feb 2018
  54. 54.
    Drafting Team Metadata and European Commission Joint Research Centre (2007) INSPIRE Metadata Implementing Rules: Technical Guidelines based on EN ISO 19115 and EN ISO 19119 - V.1.3. p 99Google Scholar
  55. 55.
    Venugopal Rao B, Kamini (2015) Bhuvan geospatial content standards. Remote Sens Appl Area NRSC/ISRO, no. August, 2015Google Scholar
  56. 56.
    Munzner T (2009) A nested process model for visualization design and validation. IEEE Trans Vis Comput Graph 15(6):921–928.  https://doi.org/10.1109/TVCG.2009.111 CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Gangothri Rajaram
    • 1
  • Harish Chandra Karnatak
    • 2
  • Swaminathan Venkatraman
    • 3
  • K. R. Manjula
    • 1
  • Kannan Krithivasan
    • 4
  1. 1.School of ComputingSASTRA UniversityThanjavurIndia
  2. 2.Remote Sensing and Geoinformatics GroupIndian Institute of Remote Sensing, Indian Space Research OrganizationDehradunIndia
  3. 3.Discrete Mathematics Research LaboratorySrinivasa Ramanujan Centre, SASTRA UniversityKumbakonamIndia
  4. 4.School of Humanities & SciencesSASTRA UniversityThanjavurIndia

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