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Interoperable framework for improving data quality using semantic approach: use case on biodiversity

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Abstract

Today the Internet growing exponentially and revolutionizing everything with increasing number of users everywhere in order to meet the superfluous demand has triggered an unprecedented wave of various kinds of digital data on the Web. Among them much of the data is relevant and can be turned into actionable insights but difficulties to face are that handling such a hype of data on the Web and due to its unstructured format can not meet the pre-set requirements of professionals and end users. In the context of biodiversity domain, a conceptual approach of data science has been proposed in this paper to extract and structure data seamlessly, which makes sense of all biodiversity-rich data and multiple-record documents by saving time and energy. The major drawback in manual extraction and storage of biodiversity data is that it gives rise to several errors (such as spelling errors, skipping of some data fields etc.) which can be difficult to improve during the processing stage, thereafter can not meet the research demands. However, such drawbacks can be dealt if data science approach is applied within the system and this automated approach will be fast, flexible, reliable and accurate. Nevertheless, the only thing to be taken care in the extraction approach is regular monitoring and analysis of Hypertext Markup Language (HTML) structure, documents, and links of target sources. Such a huge set of data contains many error and noisy characters; to eliminate these errors, data cleaning algorithm has been used to make data error-free and ready for further systematic research. Due to the wide variety of data formats, achieving interoperability is a daunting task, since some of the datasets do not follow their own schema structure. To cope with this demand, semantic interoperability has proved to be helpful by exchanging data through web services between different independent loosely coupled systems. This paper presents an overview of semantic interoperability and case studies on various projects that implemented it for biodiversity data sharing.

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References

  • Adali S, Candan KS, Papakonstantinou Y, Subrahmanian VS (1996) Query caching and optimization in distributed mediator systems. ACM SIGMOD Rec 10(1145/235968):233327

    Google Scholar 

  • Adelberg B (1998) NoDoSE—a tool for semi-automatically extracting structured and semistructured data from text documents. ACM SIGMOD Rec 10(1145/276305):276330

    Google Scholar 

  • Apers PM (1995) Identifying internet-related database research. In East/west database workshop. Springer, London, pp 183–193

    Book  Google Scholar 

  • Arasu A, Garcia-Molina H, University S (2003) Extracting structured data from Web pages. In: Proceedings of the 2003 ACM SIGMOD international conference on on Management of data—SIGMOD’03. https://doi.org/10.1145/872797.872799

  • Arens Y, Knoblock Ca., Shen WM (1996) Query reformulation for dynamic information integration. J Intell Inform Syst. https://doi.org/10.1007/BF00122124

  • Arocena GO, Mendelzon AO (1999) WebOQL: Restructuring documents, databases, and webs. Theory and practice of object systems. https://doi.org/10.1002/(SICI)1096-9942(1999)5:3%3c127::AID-TAPO2%3e3.0.CO;2-X

  • Arora NK (2018a) Environmental sustainability—necessary for survival. Environ Sustain 1(1):1–2. https://doi.org/10.1007/s42398-018-0013-3

    Article  Google Scholar 

  • Arora NK (2018b) Biodiversity conservation for sustainable future. Environ Sustain 1(2):109–111. https://doi.org/10.1007/s42398-018-0023-1

    Article  Google Scholar 

  • Ballesteros-Mejia L, Kitching IJ, Jetz W, Nagel P, Beck J (2013) Mapping the biodiversity of tropical insects: Species richness and inventory completeness of African sphingid moths. Global Ecol Biogeogr. https://doi.org/10.1111/geb.12039

  • Batini C, Lenzerini M, Navathe SB (1986) A comparative analysis of methodologies for database schema integration. ACM Comput Surv 10(1145/27633):27634

    Google Scholar 

  • Batista-Navarro R, Zerva C, Nguyen NTH, Ananiadou S (2017) A text mining-based framework for constructing an RDF-compliant biodiversity knowledge repository. In: Communications in computer and information science. https://doi.org/10.1007/978-3-319-55209-5_3

  • Baumgartner R, Baumgartner R, Flesca S, Gottlob G, Flesca S, Gottlob G (2001) Visual web information extraction with lixto. In: Proceedings of the international conference on very large data bases

  • Baumgartner R, Gatterbauer W, Gottlob G (2009) Web data extraction system. In: Encyclopedia of database systems (pp. 3465-3471). Springer, Boston

  • Beneventano D, Bergamaschi S, Guerra F, Vincini M (2003) Synthesizing an integrated ontology. In: IEEE Internet Computing. https://doi.org/10.1109/MIC.2003.1232517

  • Berners-Lee T (1998) Web Architecture from 50,000 feet. W3C. https://www.w3.org/DesignIssues/Architecture.html. Accessed 23 September 2018

  • Berners-Lee T, M F (1999) Weaving the web, the original design and ultimate destiny of the World Wide Web by its inventor. Harper Business San Francisco. https://doi.org/10.1109/TPC.2000.843652

  • Bernstein PA, Haas LM (2008) Information integration in the enterprise. Commun ACM 10(1145/1378727):1378745

    Google Scholar 

  • Blagoderov V, Kitching IJ, Livermore L, Simonsen TJ, Smith VS (2012) No specimen left behind: Industrial scale digitization of natural history collections. ZooKeys. https://doi.org/10.3897/zookeys.209.3178

  • Blakeley JA (1997) Universal data access with OLE DB. In: Proceedings IEEE COMPCON 97. https://doi.org/10.1109/CMPCON.1997.584662

  • Bonczek RH, Holsapple CW, Whinston AB (1978) Aiding decision makers with a generalized data base management system: an application to inventory management. Decision Sci. https://doi.org/10.1111/j.1540-5915.1978.tb01381.x

  • Brin S, Motwani R, Page L, Winograd T (1998) What can you do with a web in your pocket? IEEE Data Eng Bull 21(2):37–47

    Google Scholar 

  • Califf ME, Mooney RJ (1999) Relational learning of pattern-match rules for information extraction. Comput Linguist. https://doi.org/10.1162/153244304322972685

  • Calvanese D, De Giacomo G, Lenzerini M (2001) A Framework for Ontology Integration. In: Proc. of the 2001 Int. Semantic Web Working Symposium (SWWS 2001)

  • Ceccarelli T (1997) Towards a planning support system for communal areas in the Zambezi Valley, Zimbabwe: a multi criteria evaluation linking farm household analysis, land evaluation and geographic information systems

  • Chang CH, Kuo SC (2004) OLERA: Semisupervised Web-data extraction with visual support. IEEE Intell Syst. https://doi.org/10.1109/MIS.2004.71

  • Chang C, Lui SC (2001) IEPAD: Information extraction based on pattern discovery. In:Proceedings of the 10th international conference on World Wide Web—WWW. https://doi.org/10.1145/371920.372182

  • Chawathe S, Garcia-Molina H, Hammer J, Ireland K, Papakonstantinou Y, Ullman J, Widom J (1994) The TSIMMIS project: integration of heterogenous information sources. In:Proceedings of IPSJ conference

  • Chen PPS (1976) The entity-relationship model—toward a unified view of data. ACM Trans Datab Syst. https://doi.org/10.1145/320434.320440

  • Chidlovskii B, Ragetli J, de Rijke M (2000) Automatic wrapper generation for web search engines. In: International conference on web-age information management (pp. 399-410). Springer, Berlin

  • Choi N, Song I-Y, Han H (2006) A survey on ontology mapping. ACM SIGMOD Record 10(1145/1168092):1168097

    Google Scholar 

  • Cohen WW (1998) Integration of heterogeneous databases without common domains using queries based on textual similarity. In: Proceedings of the 1998 ACM SIGMOD International Conference on Management of Data. https://doi.org/10.1145/276305.276323

  • Crescenzi V, Mecca G (1998) Grammars have exceptions. Inform Syst. https://doi.org/10.1016/S0306-4379 (98)00028-3

  • Crescenzi V, Mecca G, Merialdo P (2001) Roadrunner: towards automatic data extraction from large web sites. In: Proceedings of the 27th International Conference on Very Large Data Bases

  • Date CJ (1995) An introduction to database systems. In: An introduction to database systems. https://doi.org/10.3145/epi.2009.jul.14

  • Doan A, Domingos P, Halevy A (2003) Learning to match the schemas of data sources: a multistrategy approach. Mach Learn. https://doi.org/10.1023/A:1021765902788

  • Drew P, King R, McLeod D, Rusinkiewicz M, Silberschatz A (1993) Report of the workshop on semantic heterogeneity and interpolation in multidatabase systems. {ACM} {SIGMOD} Record. https://doi.org/10.1145/163090.163098

  • Elmagarmid AK (1992) Database transaction models for advanced applications. Database

  • Embley DW, Campbell DM, Jiang YS, Liddle SW, Lonsdale DW, Ng YK, Smith RD (1999) Conceptual-model-based data extraction from multiple-record Web pages. Data Knowl Eng. https://doi.org/10.1016/S0169-023X(99)00027-0

  • Fayyad U, Piatetsky-Shapiro G, Smyth P (1996) Knowledge discovery and data mining: towards a unifying framework. In: Int Conf on Knowledge Discovery and Data Mining

  • Fensel D, Van Harmelen F, Klein M, Akkermans H, Broekstra J, Fluit C, Krohn U (2000) On-to-knowledge: ontology-based tools for knowledge management. J Bus Ethic

  • Ferrara E, De Meo P, Fiumara G, Baumgartner R (2014) Web data extraction, applications and techniques: A survey. Knowl Based Syst. https://doi.org/10.1016/j.knosys.2014.07.007

  • Finkelstein C (1989) An introduction to information engineering: from strategic planning to information systems. Addison-Wesley, Sydney, p 52

    Google Scholar 

  • Florescu D, Levy AY, Mendelzon AO (1998) Database techniques for the World-Wide Web: a survey. SIGMOD Rec 27(3):59–74

    Article  Google Scholar 

  • Freitag D (2000) Machine learning for information extraction in informal domains. Mach Learn. https://doi.org/10.1023/A:1007601113994

  • Fridman N, Musen M (2000) PROMPT: Algorithm and tool for automated ontology merging and alignment. Proc. AAAI’00

  • Friedman M, Weld DS (1997) Efficiently executing information-gathering plans. In: In Proc. of the Int. Joint Conf. of AI (IJCAI

  • Fuxman A, Hernandez MA, Ho H, Miller RJ, Papotti P, Roma Tre U, Popa L (2006) Nested mappings: schema mapping reloaded. VLDB

  • Gangemi A, Guarino N, Masolo C, Oltramari A (2003) Sweetening WORDNET with DOLCE. AI magazine. https://doi.org/10.1007/3-540-45810-7

  • Gennari JH, Musen MA, Fergerson RW, Grosso WE, Crubezy M, Eriksson H, Tu SW (2003) The evolution of Protégé: An environment for knowledge-based systems development. International J Hum Comput Stud. https://doi.org/10.1016/S1071-5819(02)00127-1

  • Georgakopoulos D, Rusinkiewicz M, Sheth AP (1994) Using tickets to enforce the serializability of multidatabase Transactions. In: IEEE Transactions on Knowledge and Data Engineering. https://doi.org/10.1109/69.273035

  • Ghawi R, Cullot N (2007) Database-to-ontology mapping generation for semantic interoperability. VDBL’07 Conference, VLDB Endowment ACM

  • Haas LM, Kossmann D, Wimmers EL, Yang J (1997) Optimizing queries across diverse data sources. Vldb

  • Halevy AY (2001) Answering queries using views: a survey. VLDB J. https://doi.org/10.1007/s007780100054

  • Halevy A, Ordille J (2006) Data integration : the teenage years. artificial intelligence. integration : the teenage yea

  • Hammer M, McLeod D (1979) On Database Management System Architecture (No. MIT/LCS/TM-141). MASSACHUSETTS INST OF TECH CAMBRIDGE LAB FOR COMPUTER SCIENCE

  • Hammer J, McHugh J, Garcia-Molina H (1997) Semistructured data: the TSIMMIS experience. In: Proceedings of the 1st east-european symposium on advances in databases and information systems (ADBIS)

  • Hardisty AR, Bacall F, Beard N, Balcázar-Vargas MP, Balech B, Barcza Z, Yilmaz P (2016) BioVeL: a virtual laboratory for data analysis and modelling in biodiversity science and ecology. BMC Ecol. https://doi.org/10.1186/s12898-016-0103-y

  • Heimbigner D, McLeod D (1985) A federated architecture for information management. ACM Trans Inform Syst 10(1145/4229):4233

    Google Scholar 

  • Hogue A, Karger D (2005) Thresher : automating the unwrapping of semantic content from the World Wide Web. WWW’05: In: Proceedings of the 14th international conference on World Wide Web. https://doi.org/10.1145/1060745.1060762

  • Hsu CN, Dung MT (1998) Generating finite-state transducers for semi-structured data extraction from the Web. Inform Syst. https://doi.org/10.1016/S0306-4379

  • Huber GP (1990) A theory of the effects of advanced information technologies on organizational design, intelligence, and decision making. Acad Manag Rev. https://doi.org/10.2307/258105

  • Hull R (1997) Managing semantic heterogeneity in databases: a theoretical prospective. In: Proceedings of the sixteenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems (pp. 51-61). ACM

  • International Business Machines Corporation (1978) Business systems planning: Information systems planning guide. IBM

  • Irmak U, Suel T (2006) Interactive wrapper generation with minimal user effort. In: Proceedings of the 15th international conference on World Wide Web—WWW’06. https://doi.org/10.1145/1135777.1135859

  • IUCN Red list (2018) Numbers of threatened species by major groups of organisms (1996–2018). http://cmsdocs.s3.amazonaws.com/summarystats/2018-1_Summary_Stats_Page_Documents/2018_1_RL_Stats_Table_1.pdf. Accessed 20 September 2018

  • Jones AC (2006) Applying computer science research to biodiversity informatics: Some experiences and lessons. Trans Comput Syst Biol. https://doi.org/10.1007/11732488_4

  • Jones A, Xu X, Pittas N, Gray W, Fiddian N, White RJ, Brandt S (2000) SPICE: a flexible architecture for integrating autonomous databases to comprise a distributed catalogue of life. In: Database and expert systems applications, lecture notes in computer science. https://doi.org/10.1007/3-540-44469-6_92

  • Kadam VB, Pakle GK (2014) A survey on HTML structure aware and tree based web data scraping technique. Int J Comput Sci Inform Technol 5(2):1655–1658

    Google Scholar 

  • Kalfoglou Y, SchorlemmeR M (2003) Ontology mapping: the state of the art. Knowl Eng Rev. https://doi.org/10.1017/S0269888903000651

  • Kayed M, Chang CH (2010) FiVaTech: Page-level web data extraction from template pages. In: IEEE Transactions on Knowledge and Data Engineering. https://doi.org/10.1109/TKDE.2009.82

  • Kossmann D (2000) The state of the art in distributed query processing. ACM Comput Surv 10(1145/371578):371598

    Google Scholar 

  • Kushmerick N (2000) Wrapper induction: efficiency and expressiveness. Artif Intell. https://doi.org/10.1016/S0004-3702(99)00100-9

  • Laender AHF, Ribeiro-Neto BA, da Silva AS, Teixeira JS (2002a) A brief survey of web data extraction tools. ACM SIGMOD Rec 10(1145/565117):565137

    Google Scholar 

  • Laender AHF, Ribeiro-Neto B, De Silva AS (2002) DEByE—Data extraction by example. Data Knowl Eng. https://doi.org/10.1016/S0169-023X(01)00047-7

  • Lage JP, Da Silva AS, Golgher PB, Laender AHF (2004) Automatic generation of agents for collecting hidden Web pages for data extraction. In : Data and Knowledge Engineering. https://doi.org/10.1016/j.datak.2003.10.003

  • Levy Y, Rajaraman A, Ordille J (1996) Querying heterogeneous information sources using source descriptions. In: Proceeding VLDB’96 proceedings of the 22th international conference on very large data bases. https://doi.org/10.1049/tpe.1981.0030

  • Litwin W, Abdellatif A (1986) Multidatabase interoperability. Computer. https://doi.org/10.1109/MC.1986.1663123

  • Litwin W, Mark L, Roussopoulos N (1990) Interoperability of multiple autonomous databases. ACM Comput Surv 10(1145/96602):96608

    Google Scholar 

  • Liu L, Pu C, Han W (2000) XWRAP : An XML—enabled wrapper construction system for web information sources. In: Proceedings of the 16th international conference on data engineering. https://doi.org/10.1109/ICDE.2000.839475

  • Malone TW, Yates J, Benjamin RI (1987) Electronic markets and electronic hierarchies. Commun ACM 10(1145/214762):214766

    Google Scholar 

  • Martin J, Finkelstein C (1989) Information engineering. Prentice Hall, Englewood Cliffs

    Google Scholar 

  • Mathew C, Güntsch A, Obst M, Vicario S, Haines R, Williams A, Goble C (2014) A semi-automated workflow for biodiversity data retrieval, cleaning, and quality control. Biodiv Data J. https://doi.org/10.3897/BDJ.2.e4221

  • McCarthy WE (1982) The REA accounting model—a generalized framework for accounting systems in a shared data environment. The Account Rev

  • Meersman R (2005) The use of lexicons and other computer-linguistic tools in semantics, design and cooperation of database systems. Star (2005)

  • Miller RJ, Haas LM, Hernández Ma (2000) Schema mapping as query discovery. In: Proceedings of the 26th international conference on very large data bases

  • Murphy J, Hashim NH, O’Connor P (2007) Take me back: validating the wayback machine. J Comput Med Commun. https://doi.org/10.1111/j.1083-6101.2007.00386.x

  • Muslea I, Minton S, Knoblock CA (2001) Hierarchical wrapper induction for semistructured information sources. Autonom Agent Multi-Agent Syst. https://doi.org/10.1023/A:1010022931168

  • Niles I, Pease A (2001) Towards a standard upper ontology. In: Proceedings of the international conference on formal ontology in information systems—FOIS’01. https://doi.org/10.1145/505168.505170

  • Nowak J, Nogueras-Iso J, Peedell S (2005) Issues of multilinguality in creating a European SDI-The perspective for spatial data interoperability. In: 11th ECGI GIS workshop ESDI setting the framework Alghero Sardinia

  • O’Sullivan B, Keady S, Keane E, Irwin S, O’Halloran J (2010). Data mining for biodiversity prediction in forests. In: Frontiers in artificial intelligence and applications. https://doi.org/10.3233/978-1-60750-606-5-289

  • Ouksel AM, Sheth A (1999) Semantic interoperability in global information systems. ACM Sigmod Record 28(1):5–12

    Article  Google Scholar 

  • Page RDM (2011) Extracting scientific articles from a large digital archive: BioStor and the biodiversity heritage library. BMC Bioinform. https://doi.org/10.1186/1471-2105-12-187

  • Raghavan S, Garcia-Molina H (2001) Integrating diverse information management systems: a brief survey. Technical Report, Stanford

    Google Scholar 

  • Reis DC, Golgher PB, Silva AS, Laender AF (2004) Automatic web news extraction using tree edit distance. In: Proceedings of the 13th conference on World Wide Web - WWW’04. https://doi.org/10.1145/988672.988740

  • Ribeiro-Neto B, Laender aHF, Da Silva aS (1999) Extracting semi-structured data through examples. In: Proceedings of the Eighth International Conference on Information and Knowledge Management. https://doi.org/10.1145/319950.319962

  • Roy PS, Karnatak H, Kushwaha SPS, Roy A, Saran S (2012) India’s plant diversity database at landscape level on geospatial platform: prospects and utility in today’s changing climate. Curr Sci 102(8):1136–1142

    Google Scholar 

  • Sahuguet A, Azavant F (2001) Building intelligent Web applications using lightweight wrappers. Data Knowl Eng. https://doi.org/10.1016/S0169-023X(00)00051-3

  • Saran S, Kushwaha SPS, Ganeshaiah KN, Roy PS, Murthy YK (2012) Indian Bioresource Information Network (IBIN): a distributed bioresource national portal. ISG Newslett 18(3):6

    Google Scholar 

  • Selkow SM (1977) The tree-to-tree editing problem. Inform Proces Lett. https://doi.org/10.1016/0020-0190(77)90064-3

  • Shanmughavel P (2007) An overview on biodiversity information in databases. Bioinformation. https://doi.org/10.6026/97320630001367

  • Shekhar S (2004) Spatial data mining and geo-spatial interoperability. In Report of the NCGIA specialist meeting on spatial webs, Santa Barbara, December 2–4 2004

  • Sheth AP (1999) Changing focus on interoperability in information systems:from system, syntax, structure to semantics. In: Interoperating geographic information systems. https://doi.org/10.1007/978-1-4615-5189-8_2

  • Sheth A, Kashyap V (1993) So far (schematically) yet so near (semantically). In interoperable database. Systems 5:283–312

    Google Scholar 

  • Sheth AP, Larson JA (1990) Federated database systems for managing distributed, heterogeneous, and autonomous databases. ACM Comput Surv 10(1145/96602):96604

    Google Scholar 

  • Silva N, Rocha J (2003) Ontology mapping for interoperability in semantic web. In: ICWI, pp. 603–610

  • Silvertown J (2009) A new dawn for citizen science. Trends Ecol Evol. https://doi.org/10.1016/j.tree.2009.03.017

  • Singh P, Saran S, Kumar D, Padalia H, Srivastava A, Kumar AS (2018) Species mapping using citizen science approach through IBIN portal: use case in foothills of Himalaya. J Indian Soc Remote Sens, 1–13

  • Smedt TD, Daelemans W (2012) Pattern for python. J Mach Learn Res 13:2063–2067

    Google Scholar 

  • Soderland S (1999) Learning information extraction rules for semi-structured and free text. Mach Learn. https://doi.org/10.1023/A:1007562322031

  • Sonsilphong S, Arch-int N, Arch-int S (2012) Rule-based semantic web services annotation for healthcare information integration. In: Computing and networking technology (iccnt), 2012 8th international conference on (pp. 147-152). IEEE

  • Species 2000 Secretariat (2009) Species 2000. http://www.sp2000.org/index.php?option=com_content&task=view&id=40&Itemid=49. Accessed 23 September 2018

  • Stonebraker M, Aoki PM, Litwin W, Pfeffer A, Sah A, Sidell J, Yu A (1996) Mariposa: a wide-area distributed database system. VLDB J. https://doi.org/10.1007/s007780050015

  • Tomasic A, Raschid L, Valduriez P (1998) Scaling access to heterogeneous data sources with DISCO. In: IEEE transactions on knowledge and data engineering. https://doi.org/10.1109/69.729736

  • Ullman JD (2000) Information integration using logical views. Theor Comput Sci. https://doi.org/10.1016/S0304-3975(99)00219-4

  • Veltman KH (2001) Syntactic and semantic interoperability: new approaches to knowledge and the semantic web. N Rev Inform Netw 7(1):159–183

    Article  Google Scholar 

  • Wache H, Vögele T, Visser U, Stuckenschmidt H, Schuster G, Neumann H, Hübner S (2001) Ontology-based integration of information-a survey of existing approaches. IJCAI Workshop: Ontologies and Information Sharing

  • Wang J, Lochovsky FH (2003) Data extraction and label assignment for web databases. In: Proceedings of the twelfth international conference on World Wide Web—WWW’03. https://doi.org/10.1145/775152.775179

  • Wilson PS (2007) What mapping and modeling means to the HIM professional. Perspectives in Health Information Management/AHIMA, American Health Information Management Association, Chicago, p 4

    Google Scholar 

  • Woelk D, Bohrer B, Jacobs N, Ong K, Tomlinson C, Unnikrishnan C (1995) Carnot and InfoSleuth: database technology and the world wide web. In: ACM SIGMOD Record (Vol. 24, No. 2, pp. 443-444). ACM

  • Yang W (1991) Identifying syntactic differences between two programs. software: practice and experience. https://doi.org/10.1002/spe.4380210706

  • Yesson C, Brewer PW, Sutton T, Caithness N, Pahwa JS, Burgess M, Culham A (2007) How global is the global biodiversity information facility? PLoS One. https://doi.org/10.1371/journal.pone.0001124

  • Zhai Y, Liu B (2005) Web data extraction based on partial tree alignment. In: Proceedings of the 14th international conference on World Wide Web—WWW’05. https://doi.org/10.1145/1060745.1060761

  • Zhai Y, Liu B (2006) Structured data extraction from the web based on partial tree alignment. In: IEEE transactions on knowledge and data engineering. https://doi.org/10.1109/TKDE.2006.197

  • Zhao H, Zhang S, Zhou J, Wang M. (2007). Semantic model based heterogeneous databases integration platform. In: Icnc (pp. 366-370). IEEE

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  1. Geoinformatics Department, Indian Institute of Remote Sensing, Indian Space Research Organisation, #4, Kalidas Road, Dehradun, 248001, India

    Priyanka Singh & Sameer Saran

  2. Mining Engineering Department, Indian Institute of Technology (Indian School of Mines), Dhanbad, Jharkhand, 826004, India

    Priyanka Singh & Dheeraj Kumar

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Singh, P., Kumar, D. & Saran, S. Interoperable framework for improving data quality using semantic approach: use case on biodiversity. Environmental Sustainability 1, 367–381 (2018). https://doi.org/10.1007/s42398-018-00033-1

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