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Designing an integrated knowledge graph for smart energy services

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Abstract

The sharp growth of distributed energy-related resources requires an efficient energy management for future grids. The traditional power grid that highly depends on information model standards collects energy data depending on them and creates energy services. Nowadays, decentralized grids utilize information schema generated by reusing standards as well as existing schemas. The schema helps implement smart energy services in the future grids. To meet such requirements, domain experts proposed upper-level schemas that manage a wide range of energy-related knowledge resources. However, their schemas could not conduct an effective reuse of energy-related knowledge resources due to their unsuitable schema development methodologies. Moreover, there is a lack of vocabularies that satisfy critical requirements for decentralized grids. To cope with these problems, we propose an energy knowledge graph (EKG) as an upper schema for the integration of knowledge resources in energy systems. First, we utilize the existing methodology that offers guidelines for reusing existing knowledge resources. Second, EKG supports concepts of energy trading and communities to satisfy the requirements of decentralized grids. Third, EKG presents compliant concepts that are compatible with existing schemas. Fourth, we modeled the use cases using the EKG and evaluated them according to the scenario specification of energy services. Last, to demonstrate the benefits of the EKG, we implemented key components such as semantic mashup and complex event processing.

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Notes

  1. https://www.w3.org/TR/rdf-schema/.

  2. http://www.fi-ppp-finseny.eu/deliverables/.

  3. https://www.oasis-open.org/committees/emix/.

  4. http://www.geonames.org/.

  5. http://wiki.dbpedia.org/.

  6. https://www.oasis-open.org/committees/wsbpel/.

  7. https://www.w3.org/TR/2001/NOTE-wsdl-20010315.

  8. https://jena.apache.org/documentation/tdb/index.html.

  9. https://www.w3.org/TR/vocab-ssn/.

  10. http://www.qudt.org/.

  11. https://wiki.ucar.edu/display/NNEWD.

References

  1. Farhangi H (2010) The path of the smart grid. IEEE Power Energy Mag 8(1):158–172

    Article  MathSciNet  Google Scholar 

  2. Katiraei F, Iravani R, Hatziargyriou N, Dimeas A (2008) Microgrids management. IEEE Power Energy Mag 6(3):4734–4749

    Article  Google Scholar 

  3. Accenture (2013) Realizing the full potential of smart metering, pp 1–24

  4. Sowa JF (2000) Knowledge representation: logical, philosophical, and computational foundations. Pacific Grove, Brooks/Cole, p 13

    Google Scholar 

  5. Berners-Lee T, Hendler J, Lassila O (2001) The semantic web. Sci Am 284(5):28–37

    Article  Google Scholar 

  6. Semantic Web, W3C. https://www.w3.org/standards/semanticweb/. Accessed 10 Mar 2018

  7. OWL, W3C. https://www.w3.org/standards/techs/owl#w3c_allL. Accessed 10 Mar 2018

  8. Baader F, Horrocks I, Sattler U (2008) Description logics. Found. Artif Intell 3:135–179

    Google Scholar 

  9. CIM Standards. http://www.iec.ch/smartgrid/standards. Accessed 10 Mar 2018

  10. Aman S, Simmhan Y, Prasanna VK (2013) Energy management systems: state of the art and emerging trends. Commun Mag 51(1):114–119

    Article  Google Scholar 

  11. Bonino D, Procaccianti G (2014) Exploiting semantic technologies in smart environments and grids: emerging roles and case studies. Sci Comput Program 95:112–134

    Article  Google Scholar 

  12. Zhou Q, Simmhan T, Prasanna V (2012) Incorporating semantic knowledge into dynamic data processing for smart power grids. In: ISWC, pp 257–273

  13. Zhou Q, Natarajan S, Simmhan Y, Prasanna V (2012) Semantic information modeling for emerging applications in smart grid. In: Information Technology: New Generations (ITNG), pp 775–782

  14. Gillani S, Laforest F, Picard G (2014) A generic ontology for prosumer-oriented smart grid. In: EDBT/ICDT Workshops, pp 134–139

  15. Lpez G, Custodio V, Moreno JI, Sikora M, Moura P, Fernndez N (2015) Modeling Smart Grid neighborhoods with the ENERsip ontology. J Comput Ind 70:168–182

    Article  Google Scholar 

  16. Lamanna DD, Maccioni A (2014) Renewable energy data sources in the semantic web with OpenWatt. In: EDBT/ICDT Workshops, pp 128–134

  17. Bonino D, Corno F (2008) Dogont-ontology modeling for intelligent domotic environments. In: ISWC, pp 790–803

  18. Bonino D, Corno F, De Russis L (2015) PowerOnt: an ontology-based approach for power consumption estimation in smart homes. In: User-Centric IoT, pp 3–8

  19. Surez-Figueroa MC, Gmez-Prez A, Fernndez-Lpez M (2012) The NeOn methodology for ontology engineering. In: Ontology Engineering in a Networked World, pp 9–34

  20. Hitzler P, Krtzsch M, Parsia B, Patel-Schneider PF, Rudolph S (2009) OWL 2 web ontology language primer. W3C Recomm 27(1):123

  21. Noy NF, McGuinness DL (2001) Ontology development 101: a guide to creating your first ontology

  22. Fernndez-Lpez M, Gmez-Prez A, Juristo N (1997) Methontology: from ontological art towards ontological engineering. In: AAAI, pp 33–40

  23. Sure Y, Staab S, Studer R (2004) On-to-knowledge methodology (OTKM). In: Handbook on Ontologies, pp 117–132

  24. Tempich C, Pinto HS, Sure Y, Staab S (2005) An argumentation ontology for DIstributed, Loosely-controlled and evolvInG Engineering processes of oNTologies (DILIGENT). In: European Semantic Web Conference, pp 241–256

  25. Bassi A, Bauer M, Fiedler M, Kramp T, Van Kranenburg R, Lange S, Meissner S (2016) Enabling things to talk. Springer, Berlin

    Google Scholar 

  26. OWL-S, W3C. https://www.w3.org/Submission/2004/07/. Accessed 10 Mar 2018

  27. Eom S, Shin S, Lee K-H (2015) Spatiotemporal query processing for semantic data stream. In: International Conference on Semantic Computing (ICSC), pp 290–297

  28. Chun S, Jung J, Jin X, Yoon S, Lee K-H (2016) Proactive replication of dynamic linked data for scalable RDF stream processing. In: ISWC

  29. RDF, W3C. https://www.w3.org/RDF/. Accessed 10 Mar 2018

  30. Yu J, Lee N, Pyo CS, Lee YS (2016) WISE: web of object architecture on IoT environment for smart home and building energy management. J Supercomput 74(9):4403–4418

    Article  Google Scholar 

  31. Wagner A, Anicic D, Sthmer R, Stojanovic N, Harth A, Studer R (2010) Linked data and complex event processing for the smart energy grid. In: Linked Data in the Future Internet at the Future Internet Assembly

  32. Ro W, Park G, Chun S, Lee K-H (2015) Complex sensor mashups for linking sensors and formula-based knowledge bases. In: International Conference on Information Reuse and Integration (IRI), pp 126–133

  33. Paulheim H (2017) Knowledge graph refinement: a survey of approaches and evaluation methods. Semantic Web 8(3):489–508

    Article  Google Scholar 

  34. Shi B, Weninger T (2017) Open-world knowledge graph completion. In: AAAI 2018:1957–1964

    Google Scholar 

  35. Wang Q, Mao Z, Wang B, Guo L (2017) Knowledge graph embedding: a survey of approaches and applications. IEEE Trans Knowl Data Eng 29(12):2724–2743

    Article  Google Scholar 

  36. Garca-Saiz D, Zorrilla M, Bosque JL (2017) A clustering-based knowledge discovery process for data centre infrastructure management. J Supercomput 73(1):215–226

    Article  Google Scholar 

  37. Zhang M, Wang Q, Xu W, Li W, Sun S (2018) Discriminative path-based knowledge graph embedding for precise link prediction. European Conference on Information Retrieval. Springer, Cham, pp 276–288

    Chapter  Google Scholar 

  38. Song JJ, Lee W (2017) Relevance maximization for high-recall retrieval problem: finding all needles in a haystack. J Supercomput 1–24

  39. Ploennigs J, Anika S, Freddy L (2014) Adapting semantic sensor networks for smart building diagnosis. ISWC 2014:308–323

    Google Scholar 

  40. Chun S, Jin X, Seo S, Lee K-H, Shin Y, Lee I (2018) Knowledge graph modeling for semantic integration of energy services. In: Workshop on Big Data Analysis for Smart Energy (BigData4SmartEnergy)

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Acknowledgements

This research was supported by Korea Electric Power Corporation. (Grant number: R18XA05).

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Authors and Affiliations

  1. Department of Computer Science, Yonsei University, Seoul, South Korea

    Sejin Chun, Jooik Jung, Xiongnan Jin, Seungmin Seo & Kyong-Ho Lee

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  1. Sejin Chun
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  2. Jooik Jung
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Correspondence to Kyong-Ho Lee.

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Chun, S., Jung, J., Jin, X. et al. Designing an integrated knowledge graph for smart energy services. J Supercomput 76, 8058–8085 (2020). https://doi.org/10.1007/s11227-018-2672-3

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