Skip to main content

Advertisement

SpringerLink
Log in

Ocean knowledge representation through integration of big data employing semantic web technologies

  • Research Article
  • Published:
Earth Science Informatics Aims and scope Submit manuscript

Abstract

Implementation of ocean observation sensors are booming in recent years for encouraging research among coastal areas all over the world. This results in a copious amount of big data which makes it difficult for traditional data processing applications to manage them. The complexity in ocean observing community is heterogeneity and interpretation, which directs to a high-end information retrieval system. World Wide Web Consortium (W3C) spreads the usage of Semantic Web (SW) that provide easier way to search, reuse, combine and share information by integrating the data into a single platform. The use of semantic web in big data management helps to increase end-users ability for self-management of data from various sources, to handle the concepts and relationships of a domain and to manage the terminologies while connecting data from a varied data sources. This paper focuses on integrating big data with semantic web technology by developing a knowledge base system through ontology to solve the problem of heterogeneity in ocean observing communities. Ontology refers to a set of machine-readable controlled vocabularies which interprets big data by combining the data concepts with ontology classes. The proposed data model upgrades the information system in terms of improvising data analysis, discovery, retrieval and decision making. In addition to that, this paper also evaluates the quality of proposed ontology and found to be 39.28% improved in completeness, 45.29% reduced in structural complexity, 11% and 37.7% decreased in conciseness and correctness, respectively.

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
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14

Similar content being viewed by others

Data availability

The datasets analyzed during the current study are available in the Meteorological & Oceanographic Satellite Data Archival Centre (MOSDAC) repository, [https://www.mosdac.gov.in/open-data].

Code availability

Not applicable.

References

  • Abburu S, Golla SB (2013) Survey on Ontology Construction Tools. Int J Sci Eng Res 4:1748–1752

    Google Scholar 

  • Abburu S, Dube N, Miyar RN, Golla SB (2015) An Ontology Based Methodology for Satellite Data Semantic Interoperability. Adv Electr Comput Eng 15:105–110

    Article  Google Scholar 

  • Ahmed J, Ahmed M (2018) Big data and semantic web, challenges and opportunities a survey. Int J Eng Technol 7:631–633

    Article  Google Scholar 

  • Ali F, Kwak D, Khan P, EI-Sappagh S, Islam SMR, Park D, Kwak KS (2017) Merged Ontology and SVM-based Information Extraction and Recommendation System for Social Robots. IEEE Access 5:12364–12379

    Article  Google Scholar 

  • Atemezing G, Corcho O, Garijo D, Mora J, Poveda-Villalón M, Rozas P, Vila-Suero D, Villazón-Terrazas B (2011) Transforming Meteorological Data into Linked Data. Semantic Web Journal 1:1–5

    Google Scholar 

  • Bai Y, Yan A, Lv B (2012) Modeling Earth Observation Satellite Data Semantically. Proceedings of the 8th IEEE International Conference on eScience (eScience 2012) 1:8–12

  • Bally J, Boneh T, Nicholson AE, Korb KB (2004) Developing an ontology for the meteorological forecasting process. Decision Support in an Uncertain and Complex World: Proceedings of the 2004 IFIP International Conference on Decision Support Systems (DSS 2004) 70–81.

  • Bansal SK (2014) Towards a Semantic Extract-Transform-Load (ETL) framework for Big Data Integration. IEEE International Congress on Big Data, 2014

  • Bansal SK, Kagemann S (2014) Integrating big data: a semantic extract-transform-load framework. Computer 48(3):42–50

    Article  Google Scholar 

  • Bansal SK, Kagemann S (2015) Integrating Big Data: A Semantic Extract Transform-Load Framework. IEEE Comput Soc 48(3):42–50

    Article  Google Scholar 

  • Beaufort F Beaufort scale: Royal Meteorological Society. https://en.wikipedia.org/wiki/Beaufort_scale. Accessed 20 June 2018 

  • Belhadi H, Akli-Astouati K, Djenouri Y, Lin JCW (2019) Exploring pattern mining for solving the ontology matching problem. Proc Eur Conf Adv Databases Inf Syst 1:85–93

    Google Scholar 

  • Belhadi H, Akli-Astouati K, Djenouri Y, Lin JCW (2020) Data mining-based approach for ontology matching problem. Appl Intell 50:1204–1221

    Article  Google Scholar 

  • Bonstrom V, Hinze A, Schweppe H (2003) ‘Storing RDF as a Graph’, Proceedings of the first Latin American Web Congress, pp. 27–36

  • Catherine R, Bernard S, André G, Corcho O, Sousa DG, Boffety D, Chanet JP (2014) Weather station data publication at Irstea: An implementation report. Context-Aware Approach for e-Agriculture 1401:89–104

    Google Scholar 

  • Chen M, Plale B (2013) From Metadata to Ontology Representation: A Case of Converting Severe Weather Forecast Metadata to an Ontology. Proc Am Soc Inf Sci Technol 49:1–4

    Google Scholar 

  • Chughtai MW, Elahi F, Akosu N (2017) Semantic web requirement engineering boundaries. J Softw Eng Intell Syst 2:57–61

    Google Scholar 

  • Cruz IF, Ganesh VR, Mirrezaei SI (2013) Semantic extraction of geographic data from web tables for big data integration. In: Proceedings of the 7th Workshop on Geographic Information Retrieval, Orlando, FL, USA, pp. 19–26. ACM

  • Devaraju A, Kauppinen T (2012) Sensors Tell More than They Sense: Modeling and Reasoning about Sensor Observations for Understanding Weather Events. Int J Sensors 2:14–26

    Google Scholar 

  • Engineering Tool Box. Recommended Relative Humidity. https://www.engineeringtoolbox.com/relative-humidity-d_895.html. Accessed 03 Jul 2018

  • Gillies A (1997) Software quality: Theory and Management. International Thomson Computer press

  • Gollapudi S (2015) Aggregating financial services data without assumptions: a semantic data reference architecture. In: 2015 IEEE International Conference on Semantic Computing (ICSC), Anaheim, CA, USA, pp. 312–315

  • Gupta S, Jain I, Johari P, Lal M (2019) Impact of Climate Change on Tropical Cyclones Frequency and Intensity on Indian Coasts. Proceedings of International Conference on Remote Sensing for Disaster Management 359–365.

  • Haby J (2014) 1001 Haby Hints for Meteorology and Weather Prediction. The Weather Prediction. http://www.theweatherprediction.com/habyhintsbook/. Accessed 18 Jul 2018

  • Haines S, Subramanian V, Mayorga E, Snowden D, Ragsdale R, Rueda C, Howard M (2012) IOOS Vocabulary and Ontology Strategy for Observed Properties. Oceans 1(9):14–19

    Google Scholar 

  • Hasany N, Alwatban B (2017) Inference Based Interpretation of Keyword Queries for OWL Ontology. Int J Web Semant Technol 8:1–9

    Article  Google Scholar 

  • IT Research Sector in Satellite Data Processing. Open Weather Map Ontology. https://openweathermap.org. Accessed 10 Jan 2020

  • Kaladevi R, Revathi A, Sridhar S (2020) Development of Background Ontology for Weather Systems through Ontology Learning. Int J Recent Technol Eng 8:168–172

    Google Scholar 

  • Kalibatiene D, Vasilecas O (2011) Survey on Ontology Languages. Proceedings of 10th International Conference on Perspectives in Business Informatics Research 124–141.

  • Kambli K (2020) Throwback to a Stormy Year: A Look at the 8 North Indian Ocean Cyclones of 2019. The Times of India. https://weather.com/en-IN/india/news/news/2020-01-03-stormy-year-8-north-indian-ocean-cyclones-2019. Accessed 11 Dec 2020

  • Kang L, Yi L, Dong L (2014) Research on construction methods of big data semantic model. In: Proceedings of the World Congress on Engineering (WCE 2014), vol. 1, London, UK

  • Kantamneni A, Brown LE (2018) An Ontology for Solar Irradiation Forecast Models. Proceedings of the 10th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management 2:263–270.

  • Kastner W (2013) Weather ontology developed by Automation Systems Group, The Institute of Computer Aided Automation. https://algo4rail.auto.tuwien.ac.at/~k/thinkhome/ontologies.html. Accessed 05 Apr 2020

  • Khelkar P (2018) Watch | Huge water tornado at Nazare Dam in Pune stuns locals. India Today. https://www.indiatoday.in/india/story/watch-huge-water-tornado-at-nazare-dam-in-pune-stuns-locals-1256546-2018-06-10. Accessed 08 Jan 2019

  • Koll RM, Murtugudde R (2019) New IPCC report warns of marine heat waves, extremely severe cyclones. India Science Wire. https://vigyanprasar.gov.in/isw/New-IPCC-report-warns-of-marine-heat-waves-extremely-severe-cyclones.html. Accessed 09 Dec 2020

  • Kossin JP, Knapp KR, Olander TL, Velden CS (2020) Global increase in major tropical cyclone exceedance probability over the past four decades. PNAS 117:11975–11980

    Article  Google Scholar 

  • Ma X (2017) Linked Geoscience Data in practice: where W3C standards meet domain knowledge, data visualization and OGC standards. Earth Sci Inform 10:429–441. https://doi.org/10.1007/s12145-017-0304-8

    Article  Google Scholar 

  • Ma L, Mei J, Pan Y, Kulkarni K, Fokoue A, Ranganathan A (2007) Semantic Web Technologies and Data Management. IBM China Research Laboratory

  • Measurement of Precipitation (2018) Guide to Instruments and Methods of Observation. World Meteorological Organization 214–245

  • Mohsen W, Aref M, ElBahnasy K (2020) Cooperative Domain Ontology Reduction Based on Rough Sets. Int J Comput Sci Mob Comput 9:163–174

    Google Scholar 

  • Morsey M, Lehmann J, Auer S, Stadler C, Hellmann S (2012) DBpedia and the live extraction of structured data from Wikipedia. Program Electron Libr Inf Syst 46:157–181

    Google Scholar 

  • Naidu TA (2020) Waterspout leaves locals puzzled in Yanam. The Hindu. https://www.thehindu.com/news/national/andhra-pradesh/waterspout-leaves-locals-puzzled-in-yanam/article32118473.ece. Accessed 05 Dec 2020

  • Neches R, Fikes R, Finin TW, Gruber TR, Patil RS, Senator TE, Swartout WR (1991) Enabling Technology for Knowledge Sharing‟. AI Mag 12(3):36–56

    Google Scholar 

  • Nishanbaev I, Champion E, McMeekin DA (2019) A Survey of Geospatial Semantic Web for Cultural Heritage. Heritage 2:1471–1498

    Article  Google Scholar 

  • Peng P, Zou L, Özsu MT, Lei CL, Zhao D (2016) Processing SPARQL queries over distributed RDF graphs. VLDB J Int J Very Large Data Bases 25:243–268

    Article  Google Scholar 

  • Pokharel S, Sherif MA, Lehmann J (2014) Ontology Based Data Access and Integration for Improving the Effectiveness of Farming in Nepal. Proceedings of WI-IAT '14: IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT) 2:319–326.

  • Pouchard LC, Branstetter ML, Cook RB et al (2013) A Linked Science investigation: enhancing climate change data discovery with semantic technologies. Earth Sci Inform 6:175–185. https://doi.org/10.1007/s12145-013-0118-2

    Article  Google Scholar 

  • Poveda-Villaló M, Espinoza-Arias P, Garijo D, Corcho O (2020) Coming to Terms with FAIR Ontologies. Proc Knowl Eng Knowl Manag 12387:255–270. https://doi.org/10.1007/978-3-030-61244-3_18

    Article  Google Scholar 

  • Raad J, Cruz C (2015) A Survey on Ontology Evaluation Methods. Proceedings of 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2015) 631:16–23

  • Rio J, Toma DM, Martinez E, Thomas CR, Delory E, Pearlman JS, Waldmann C, Jirka S (2018) A Sensor Web Architecture for Integrating Smart Oceanographic Sensors into the Semantic Sensor Web. IEEE J Oceanic Eng 43:830–842

    Article  Google Scholar 

  • Roy RK (2017) Weather Phenomenon Prediction Using Semantic Web. Dissertation, East West University.

  • Rudnicki R (2019) An Overview of the Common Core Ontologies (Report No. 716–204–5208). CUBRC, Inc

  • Saber A, Al-Zoghby AM, Elmougy S (2018) Big-Data Aggregating, Linking, Integrating and Representing Using Semantic Web Technologies. In: Hassanien A., Tolba M., Elhoseny M., Mostafa M. (eds) The International Conference on Advanced Machine Learning Technologies and Applications (AMLTA2018). AMLTA 2018. Advances in Intelligent Systems and Computing, vol 723. Springer, Cham. https://doi.org/10.1007/978-3-319-74690-6_33

  • Sarkar S (2020) Cyclones rise as climate change heats up Indian Ocean. India Climate Dialogue. https://indiaclimatedialogue.net/2020/06/05/cyclones-rise-as-climate-change-heats-up-indian-ocean/. Accessed 20 Dec 2020

  • Shao C, Hu LM, Li JZ, Wang ZC, Chung T, Xia JB (2019) RiMOM-IM: a novel iterative framework for instance matching. J Comput Sci Technol 31:185–197

    Article  Google Scholar 

  • Shen YT, Lai JW, Leu LG, Lu YC, Chen JM, Shao HJ, Chen HW, Chang KT, Terng CT, Chang YC, Tseng RS (2019) Applications of ocean currents data from high-frequency radars and current profilers to search and rescue missions around Taiwan. J Oper Oceanogr 12:126–136

    Google Scholar 

  • Shenoi SSC, Nasnodkar N, Rajesh G, Joseph KJ, Suresh I, Almeida AM (2009) On the diurnal ranges of Sea Surface Temperature (SST) in the north Indian Ocean. J Earth Syst Sci 118:483–496

    Article  Google Scholar 

  • Staroch P (2013) A Weather Ontology for Predictive Control in Smart Homes [Master's thesis, Vienna University of Technology]. TU wein repositum. https://repositum.tuwien.at/bitstream/20.500.12708/8079/2. Accessed 02 Feb 2019

  • Stegmaier F, Gröbner U, Döller M, Kosch H, Baese G (2009) Evaluation of current RDF database solutions. Proceedings of the 10th International Workshop on Semantic Multimedia Database Technologies 1–17.

  • Thenmalar S, Geetha TV (2014) Enhanced ontology-based indexing and searching. Aslib J Inf Manag 66(6):678–696. https://doi.org/10.1108/AJIM-08-2014-0098

    Article  Google Scholar 

  • Wilson A, Cox M, Elsborg D et al (2015) A semantically enabled metadata repository for scientific data. Earth Sci Inform 8:649–661. https://doi.org/10.1007/s12145-014-0175-1

    Article  Google Scholar 

  • World Weather Online Developer. World Wide Marine Online. https://www.worldweatheronline.com. Accessed 13 Jan 2020

  • World Wide Web Consortium (2004) Resource Description Framework (RDF)/ Extensible Markup Language (XML) Syntax Specification. Available from: <www.w3.org/TR/REC-rdf-syntax/>. Accessed 8 Feb 2018

  • Xiong J, Liu Y, Liu W (2014) Ontology-based integration and sharing of big data educational resources. In: IEEE 11th Web Information System and Application Conference (WISA), Tianjin, China, pp. 245–248

  • Yahoo Weather Developer Network. Weather Developer ontology. https://developer.yahoo.com/weather. Accessed 21 Jan 2020

  • Zavitsanos E, Paliouras G, Vouros GA (2011) Gold Standard Evaluation of Ontology Learning Methods through Ontology Transformation and Alignment. IEEE Trans Knowl Data Eng 23:58–71

    Article  Google Scholar 

  • Zhang F, Li Z, Peng D et al (2021) RDF for temporal data management – a survey. Earth Sci Inform 14:563–599. https://doi.org/10.1007/s12145-021-00574-w

    Article  Google Scholar 

  • Zhang Y, Chiang YY, Szekely P, Knoblock CA (2013) A semantic approach to retrieving, linking, and integrating heterogeneous geospatial data. In: Joint Proceedings of the Workshop on AI Problems and Approaches for Intelligent Environments and Workshop on Semantic Cities, pp. 31–37. ACM

  • Zidan RA, Almustaf MM, Tahawi M (2019) Ontology2rdb for Storing Ontology in Relational Database. J Theor Appl Inf Technol 97:2229–2240

    Google Scholar 

  • Zu H, Liu D, Bayley L, Aldea A, Yang Y, Ying C (2017) Quality Model and Metrics of Ontology for Semantic Descriptions of Web Services. Tsinghua Science and Technology 22:254–272

    Article  Google Scholar 

Download references

Funding

The authors declare that the research work is financially supported by Ministry of Earth Sciences (MoES), Government of India under the grant number MoES/36/OOIS/Extra/45/2015.

Author information

Authors and Affiliations

  1. Department of Electronics and Communication Engineering, Adhiyamaan College of Engineering, Hosur, 635 109, Tamil Nadu, India

    Anitha Velu & Menakadevi Thangavelu

Authors
  1. Anitha Velu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Menakadevi Thangavelu
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Anitha Velu1 carried out all the research steps including finding the problem, analyzing the related works, implementing the proposed method as well as writing the manuscript. Menakadevi Thangavelu2 has contributed in analyzing the results and editing the manuscript. All authors have read and approved the manuscript.

Corresponding author

Correspondence to Anitha Velu.

Ethics declarations

Conflicts of Interest/Competing interests

The authors declare that they have no conflicts or competing interests.

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

Velu, A., Thangavelu, M. Ocean knowledge representation through integration of big data employing semantic web technologies. Earth Sci Inform 15, 1563–1585 (2022). https://doi.org/10.1007/s12145-022-00813-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12145-022-00813-8

Keywords

Search

Navigation