Earth Science Informatics

, Volume 9, Issue 1, pp 47–65 | Cite as

Knowledge-based environmental research infrastructure: moving beyond data

  • Markus Stocker
  • Mauno Rönkkö
  • Mikko Kolehmainen
Research Article

Abstract

Over the past decades, sensor networks have been deployed around the world to monitor over time and space a large number of properties appertaining to various environmental phenomena. A popular example is the monitoring of particulate matter and gases in ambient air undertaken, for instance, to assess air quality and inform decision makers and the public. Such infrastructure can generate large amounts of data, which must be processed to derive useful information. Infrastructure may be for environmental research, specifically. In order to reduce duplication and improve interoperability, efforts have been initiated more recently that aim at abstract architectural descriptions of infrastructure that supports the acquisition, curation, access, and processing of measurement and observation data. The ENVRI Reference Model (ENVRI-RM) is an example for an abstract architectural description of infrastructure tailored for environmental research. We briefly summarize ENVRI-RM and provide an overview of its subsystems, functionality, and viewpoints. We highlight that its primary focus is on the data life-cycle in environmental research infrastructure. As our contribution, weextend ENVRI-RM with functionality for the acquisition of knowledge from data, and the curation, access, and processing of knowledge. The extension, which we name +K, aims at addressing the knowledge life-cycle in environmental research infrastructure. We present the +K subsystems and functionality, and discuss the extension from ENVRI-RM viewpoints. We argue that the +K extension can be superimposed on ENVRI-RM to form the ENVRI-RM+K model for the ‘archetypical’ knowledge-based environmental research infrastructure that addresses both data and knowledge life-cycles. We demonstrate the application of the extension to a concrete use case in aerosol science.

Keywords

Environmental research infrastructure Knowledge-based systems Knowledge acquisition Knowledge representation and reasoning Ontology Semantic web technologies 

Notes

Acknowledgments

This research is funded by the Academy of Finland project “FResCo: High-quality Measurement Infrastructure for Future Resilient Control Systems” (Grant number 264060).

References

  1. Aamodt A, Nygård M (1995) Different roles and mutual dependencies of data, information, and knowledge – An AI perspective on their integration. Data Knowledge Eng 16(3):191–222. doi:10.1016/0169-023X(95)00017-M. http://www.sciencedirect.com/science/article/pii/0169023X9500017M CrossRefGoogle Scholar
  2. Baader F, Calvanese D, McGuinness D, Nardi D, Patel-Schneider P (2007) The Description Logic Handbook Theory Implementation and Applications, 2nd Edition. Cambridge University PressGoogle Scholar
  3. Barwise J, Perry J (1980) The Situation Underground. In: Barwise J, Sag I (eds) Stanford Working Papers in Semantics vol 1, Stanford Cognitive Science Group, pp 1–55Google Scholar
  4. Barwise J, Perry J (1981) Situations and Attitudes. The Journal of Philosophy 78(11):668–691. http://www.jstor.org/stable/2026578 CrossRefGoogle Scholar
  5. Berners-Lee T, Hendler J, Lassila O (2001) The Semantic Web. Scientific American 284(5):29–37CrossRefGoogle Scholar
  6. Brickley D, Guha R (2004) RDF Vocabulary Description Language 1.0: RDF Schema. Recommendation, W3C. http://www.w3.org/TR/2004/REC-rdf-schema-20040210/
  7. Brickley D, Guha R (2014) RDF Schema 1.1. Recommendation, W3C. http://www.w3.org/TR/2014/REC-rdf-schema-20140225/
  8. Chen Y, Hardisty A, Preece A, Martin P, Atkinson M, Zhao Z, Magagna B, Schentz H, Legré Y (2013a) Analysis of Common Requirements for Environmental Science Research Infrastructures. In: Proceedings of the International Symposium on Grids and Clouds (ISGC), Proceedings of Science (SISSA), Academia Sinica, Taipei, TaiwanGoogle Scholar
  9. Chen Y, Martin P, Magagna B, Schentz H, Zhao Z, Hardisty A, Preece A, Atkinson M, Huber R, Legré Y (2013b) A Common Reference Model for Environmental Science Research Infrastructures . In: Page B, Fleischer AG, Göbel J, Wohlgemuth V (eds) 27th International Conference on Environmental Informatics for Environmental Protection, Sustainable Development and Risk Management, Hamburg, Germany, pp 665–673Google Scholar
  10. Clemente S, Loia V, Veniero M (2013) Applying cognitive situation awareness to collision avoidance for harbour last-mile area safety. J Ambient Intell Humanized Comput :1–5. doi:10.1007/s12652-013-0187-6
  11. Compton M, Barnaghi P, Bermudez L, Garca-Castro R, Corcho O, Cox S, Graybeal J, Hauswirth M, Henson C, Herzog A, Huang V, Janowicz K, Kelsey WD, Phuoc DL, Lefort L, Leggieri M, Neuhaus H, Nikolov A, Page K, Passant A, Sheth A, Taylor K (2012) The SSN ontology of the W3C semantic sensor network incubator group Web Semantics Science. Serv Agents World Wide Web 17(0):25–32. doi:10.1016/j.websem.2012.05.003 CrossRefGoogle Scholar
  12. Cyganiak R, Reynolds D, Tennison J (2014a) The RDF Data Cube Vocabulary. Recommendation, W3C. http://www.w3.org/TR/2014/REC-vocab-data-cube-20140116/
  13. Cyganiak R, Wood D, Lanthaler M (2014b) RDF 1.1 Concepts and Abstract Syntax Recommendation W3CGoogle Scholar
  14. Dal Maso M, Kulmala M, Riipinen I, Wagner R, Hussein T, Aalto P, Lehtinen K (2005) Formation and growth of fresh atmospheric aerosols: eight years of aerosol size distribution data from SMEAR II, Hyytiala, Finland. Boreal EnviroN Res 5:323–336Google Scholar
  15. De Maio C, Fenza G, Furno D, Loia V (2012) Swarm-based semantic fuzzy reasoning for situation awareness computing. In: Fuzzy Systems (FUZZ-IEEE), 2012 IEEE International Conference on, pp 1–7. doi:10.1109/FUZZ-IEEE.2012.6251159
  16. Devlin K (1991) Logic and Information. Cambridge University PressGoogle Scholar
  17. Doulaverakis C, Konstantinou N, Knape T, Kompatsiaris I, Soldatos J (2011) An Approach to Intelligent Information Fusion in Sensor Saturated Urban Environments. In: Intelligence and Security Informatics Conference (EISIC), 2011 European, pp 108–115Google Scholar
  18. Dürst M, Suignard M (2005) Internationalized Resource Identifiers (IRIs). RFC 3987, IETF,. http://www.ietf.org/rfc/rfc3987.txt
  19. Endsley MR (1995) Toward a theory of situation awareness in dynamic systems Human Factors. J Hum Factors Ergon Soc 37(1):32–64CrossRefGoogle Scholar
  20. ENVRI (2013) ENVRI Reference Model V1.1. Tech. Rep. RDTI-RI-283465, ENVRI. http://envri.eu/rm
  21. Fenza G, Furno D, Loia V, Veniero M (2010) Agent-based Cognitive approach to Airport Security Situation Awareness. In: Proceedings of the 2010 International Conference on Complex Intelligent and Software Intensive Systems IEEE Computer Society, CISIS ’10, pp 1057–1062Google Scholar
  22. Gruber TR (1993) A translation approach to portable ontology specifications. Knowl Acquis 5(2):199–220. doi:10.1006/knac.1993.1008. http://www.sciencedirect.com/science/article/pii/S1042814383710083 CrossRefGoogle Scholar
  23. Hari P, Kulmala M (2005) Station for Measuring Ecosystem-Atmosphere Relations (SMEAR II). Boreal Environ Res 10:315–322Google Scholar
  24. Hart JK, Martinez K (2006) Environmental Sensor Networks A revolution in the earth system science?. Earth Sci Rev 78(3-4):177–191. doi:10.1016/j.earscirev.2006.05.001 CrossRefGoogle Scholar
  25. Hobbs JR, Pan F (2006) Time Ontology in OWL Working draft W3C. http://www.w3.org/TR/owl-time/
  26. Horrocks I, Patel-Schneider PF, Boley H, Tabet S, Grosof B, Dean M (2004) SWRL: A Semantic Web Rule Language Combining OWL and RuleML. Tech. rep., W3C. http://www.w3.org/Submission/SWRL/
  27. Junninen H, Lauri A, Keronen P, Aalto P (2009) Smart-SMEAR: on-line data exploration and visualization tool for SMEAR stations. Boreal Environ Res 14(4):447–457Google Scholar
  28. Keller M, Schimel DS, Hargrove WW, Hoffman FM (2008) A continental strategy for the National Ecological Observatory Network. Front Ecol Environ 6(5):282–284. doi:10.1890/1540-9295(2008)6%5B282:ACSFTN%5D2.0.CO;2 CrossRefGoogle Scholar
  29. Kokar MM, Matheus CJ, Baclawski K (2009) Ontology-based situation awareness. Inf Fusion 10(1):83–98CrossRefGoogle Scholar
  30. Kotamäki N, Thessler S, Koskiaho J, Hannukkala AO, Huitu H, Huttula T, Havento J, Järvenpää M (2009) Wireless in-situ Sensor Network for Agriculture and Water Monitoring on a River Basin Scale in Southern Finland: Evaluation from a Data User’s Perspective. Sens 9(4):2862–2883. doi:10.3390/s90402862. http://www.mdpi.com/1424-8220/9/4/2862 CrossRefGoogle Scholar
  31. Kratz TK, Arzberger P, Benson BJ, Chiu CY, Chiu K, Ding L, Fountain T, Hamilton D, Hanson PC, Hu YH, Lin FP, McMullen DF, Tilak S, Wu C (2006) Toward a Global Lake Ecological Observatory Network. Publ Karelian Inst 145:51–63Google Scholar
  32. Kulmala M, Vehkamäki H, Petäjä T, Dal Maso M, Lauri A, Kerminen V, Birmili W, McMurry P (2004) Formation and growth rates of ultrafine atmospheric particles: a review of observations. J Aerosol Sci 35(2):143–176CrossRefGoogle Scholar
  33. Lassila O, Swick RR (1999) Resource Description Framework (RDF) Model and Syntax Specification Recommendation W3C. http://www.w3.org/TR/1999/REC-rdf-syntax-19990222/
  34. Lebo T, Sahoo S, McGuinness D (2013) PROV-O: The PROV Ontology W3C Recommendation W3C. http://www.w3.org/TR/prov-o/
  35. Li W, Bhatia V, Cao K (2014) Intelligent polar cyberinfrastructure: enabling semantic search in geospatial metadata catalogue to support polar data discovery, Earth Science Informatics pp 1–13Google Scholar
  36. Llaves A, Kuhn W (2014) An event abstraction layer for the integration of geosensor data. International Journal of Geographical Information ScienceGoogle Scholar
  37. Luckham DC (2002) The Power of Events, vol 204. Addison-Wesley ReadingGoogle Scholar
  38. Martinez K, Hart JK, Ong R (2004) Environ Sens Netw Comput 37(8):50–56. doi:10.1109/MC.2004.91 Google Scholar
  39. Perry M, Herring J (2012) OGC GeoSPARQL - A Geographic Query Language for RDF Data. Tech. Rep. OGC 11-052r4, Open Geospatial ConsortiumGoogle Scholar
  40. Prud’hommeaux E, Seaborne A (2008) SPARQL Query Language for RDF Recommendation W3C. http://www.w3.org/TR/2008/REC-rdf-sparql-query-20080115/
  41. Riker WH (1957) Events and Situations. J Philos 3:57–70. http://www.jstor.org/stable/2022192 CrossRefGoogle Scholar
  42. Shearer R, Motik B, Horrocks I (2008) HermiT: A highly-efficient OWL reasoner. In: Proceedings of the 5th International Workshop on OWL: Experiences and Directions (OWLED 2008), pp 26–27Google Scholar
  43. Sirin E, Parsia B, Grau B, Kalyanpur A, Katz Y (2007) Pellet: A practical OWL-DL reasoner Web Semantics Science. Serv Agents World Wide Web 5(2):51–53. doi:10.1016/j.websem.2007.03.004 CrossRefGoogle Scholar
  44. Stanton NA, Stewart R, Harris D, Houghton RJ, Baber C, McMaster R, Salmon P, Hoyle G, Walker G, Young MS, Linsell M, Dymott R, Green D (2006) Distributed situation awareness in dynamic systems: theoretical development and application of an ergonomics methodology. Ergon 49(12-13):1288–1311. doi:10.1080/00140130600612762. pMID: 17008257CrossRefGoogle Scholar
  45. Stocker M, Rönkkö M, Villa F, Kolehmainen M (2011) The Relevance of Measurement Data in Environmental Ontology Learning. In: Environmental Software Systems. Frameworks of eEnvironment, IFIP Advances in Information and Communication Technology, vol 359, Springer Boston, pp 445–453Google Scholar
  46. Stocker M, Rönkkö M, Kolehmainen M (2012) Making Sense of Sensor Data Using Ontology: A Discussion for Road Vehicle Classification. In: Seppelt R, Voinov A, Lange S, Bankamp D (eds) International Congress on Environmental Modelling and Software, iEMSs, Leipzig, Germany, pp 2387–2394Google Scholar
  47. Stocker M, Baranizadeh E, Hamed A, Rönkkö M, Virtanen A, Laaksonen A, Portin H, Komppula M, Kolehmainen M (2013) Acquisition and Representation of Knowledge for Atmospheric New Particle Formation. In: Hřebíček J, Schimak G, Kubásek M, Rizzoli AE (eds) Environmental Software Systems. Fostering Information Sharing, IFIP Advances in Information and Communication Technology, vol 413, Springer Berlin Heidelberg, pp 98–108. doi:10.1007/978-3-642-41151-9_10
  48. Stocker M, Baranizadeh E, Portin H, Komppula M, Rönkkö M, Hamed A, Virtanen A, Lehtinen K, Laaksonen A, Kolehmainen M (2014a) Representing situational knowledge acquired from sensor data for atmospheric phenomena. Environ Model Softw 58(0):27–47. doi:10.1016/j.envsoft.2014.04.006. http://www.sciencedirect.com/science/article/pii/S1364815214001108 CrossRefGoogle Scholar
  49. Stocker M, Rönkkö M, Kolehmainen M (2014b) Situational knowledge representation for traffic observed by a pavement vibration sensor network. IEEE Trans Intell Transp Syst 15 (4):1441–1450. doi:10.1109/TITS.2013.2296697 CrossRefGoogle Scholar
  50. Taylor K, Leidinger L (2011) Ontology-Driven Complex Event Processing in Heterogeneous Sensor Networks. In: Antoniou G, Grobelnik M, Simperl E, Parsia B, Plexousakis D, Leenheer P, Pan J (eds) The Semanic Web: Research and Applications, Lecture Notes in Computer Science, vol 6644, Springer Berlin Heidelberg, pp 285–299. doi:10.1007/978-3-642-21064-8_20
  51. W3C OWL Working Group (2009) OWL 2 Web Ontology Language Document Overview Recommendation W3C. http://www.w3.org/TR/2009/REC-owl2-overview-20091027/
  52. W3C OWL Working Group (2012) OWL 2 Web Ontology Language Document Overview Recommendation W3C, Second Edition. http://www.w3.org/TR/2012/REC-owl2-overview-20121211/

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Markus Stocker
    • 1
  • Mauno Rönkkö
    • 1
  • Mikko Kolehmainen
    • 1
  1. 1.Research Group of Environmental InformaticsDepartment of Environmental Science, University of Eastern FinlandKuopioFinland

Personalised recommendations