The PERCCOM program aims at combining ICT with environmental awareness to build cleaner, greener and energy efficient cyber-physical systems [65]. As a part of PERCCOM program, this research work adopts the concept of sustainability. A sustainable development process meets the requirements of present without compromising the ability of future generations to meet their own requirements [66].
Sustainable development is built on three main pillars: Economic, Social and Environmental (Fig. 18). Each of the pillars is highly interdependent. The environmental aspect focuses on meeting the present need without disrupting global environmental ecosystem.
36
Fig. 18. Three pillars of sustainability.
The economic pillar emphases of the current economic activity not to be disproportionately burden to future generations. Definition of social pillar of a sustainable development highlights on growing a sense of community ownership by participation of citizens to transmit awareness of social sustainability [67].
37
Fig. 19. Sustainability analysis pentagon.
Proposed solution (UKENV-KG) is applied for building a knowledge graph that encompasses UK environmental legislations related to building construction and maintenance. The solution provides stakeholders (constructors, engineers, architects, planner, and dwellers) an ease-of-access platform to go through legislations and compliance guidelines and influences the social pillar of sustainability. Also, complying with environmental guidelines and legislations affects environmental aspects of sustainability. From community perspective, it offers equity of access to services.
Christoph et al. [68] highlights on sustainability analysis of software system in five dimensions (Economic, Social, Technical, Environment, Individual) with immediate, enabling and structural effect. Fig.19 illustrates the pentagon of sustainability analysis for this project. The diagram identifies the immediate, enabling and structural effect of the
38
proposed solution in Economic, Social, Technical, Environmental and Individual paradigm.
Discussion can be made on direct and indirect effect of proposed software system. To talk about direct effects, as the system would provide an ease of use platform for the stakeholder, the compliance rate would definitely rise which will definitely have a positive impact on environment. Software systems used today cannot be considered as isolated system, rather part of the socio-technical system where the software is deployed [69].
Software solution made based on proposed framework will definitely become a part of socio-technical system with definite direct and indirect impact.
39
6 CONCLUSION AND FUTURE WORK
This final chapter contains overall conclusions of the thesis work and possible future work.
6.1 Conclusions
The objective of this research work was to leverage idea of knowledge graph in domain of UK environmental legislation related to building construction and maintenance. Due to vastness of the domain of interest, implementation was delimited to service compliance sub-domain. Nevertheless, the proposed framework can be used for extension of graph coverage in legislation domain.
In alignment with predefined requirements, UKENV-KG was developed with reuse and extension of existing ontology which is responsible for conceptually describing the domain. The evaluation of implemented solution gives scope to draw several conclusions.
Knowledge graph is a popular concept in web domain whereas it can also be used in other areas. Usage of semantic technologies in legislation domain is yet not popular due to certain reasons. Ontology construction requires lots of effort and expertise. Also, data lifting in manual fashion is slower and resource consuming.
6.2 Future Work
There are several definite improvements that the system needs. First, the knowledge creation is done in curated approach where data lifting is done manually. Automated data lifting is necessary in large scale knowledge graph development.
One of the big challenges in legislation information management is dealing with temporal information. In this research temporal issue is not handled whereas it can be achieved by versioning or extending ontology with support to temporal information.
40
The implementation only covers the Domestic Building Service Compliance Guideline of UK. To achieve maximum from knowledge graph, it needs to be extended and cover other related domains.
41
7 REFERENCES
[1] A.-L. Kor et al., “Education in green ICT and control of smart systems : A first hand experience from the International PERCCOM masters programme,” 2019.
[2] “Climate change - UKGBC - UK Green Building Council.” [Online]. Available:
https://www.ukgbc.org/climate-change/. [Accessed: 07-Jun-2019].
[3] “2010 to 2015 government policy: greenhouse gas emissions - GOV.UK,” 2015.
[Online]. Available: https://www.gov.uk/government/publications/2010-to-2015- government-policy-greenhouse-gas-emissions/2010-to-2015-government-policy-greenhouse-gas-emissions. [Accessed: 28-May-2019].
[4] HM Government, “The UK Low Carbon Transition Plan,” 2009.
[5] “Climate Change Act 2008,” 2008.
[6] M. Bell et al., “Next steps for UK heat policy,” 2016.
[7] M. Nickel, K. Murphy, V. Tresp, and E. Gabrilovich, “A review of relational machine learning for knowledge graphs,” Proc. IEEE, vol. 104, no. 1, pp. 11–33, 2016.
[8] F. Van Harmelen, V. Lifschitz, and B. Porter, Handbook of knowledge representation. Elsevier, 2008.
[9] T. Steiner, R. Verborgh, R. Troncy, J. Gabarro, and R. Van De Walle, “Adding Realtime Coverage to the Google Knowledge Graph.”
[10] J. M. Gomez-Perez, J. Z. Pan, G. Vetere, and H. Wu, “Enterprise Knowledge Graph:
An Introduction,” in Exploiting Linked Data and Knowledge Graphs in Large Organisations, Cham: Springer International Publishing, 2017, pp. 1–14.
[11] J. Yan, C. Wang, W. Cheng, M. Gao, and A. Zhou, “A retrospective of knowledge graphs,” Front. Comput. Sci., vol. 12, no. 1, pp. 55–74, 2018.
[12] R. Angles and C. Gutierrez, “Survey of graph database models,” ACM Comput.
Surv., vol. 40, no. 1, pp. 1–39, Feb. 2008.
[13] X. Wilcke, P. Bloem, and V. de Boer, “The knowledge graph as the default data model for learning on heterogeneous knowledge,” Data Sci., pp. 1–19, Oct. 2017.
[14] E. Montiel-Ponsoda, V. Rodríguez-Doncel, and J. Gracia, “Building the Legal Knowledge Graph for Smart Compliance Services in Multilingual Europe.”
[15] T. Heath and C. Bizer, “Linked Data: Evolving the Web into a Global Data Space,”
42
Synth. Lect. Semant. Web Theory Technol., vol. 1, no. 1, pp. 1–136, Feb. 2011.
[16] Z. O. Repository, “University of Zurich Applied temporal RDF : efficient temporal querying of RDF data with SPARQL Applied temporal RDF : efficient temporal querying of RDF data with SPARQL,” no. June, pp. 308–322, 2009.
[17] Claudio Gutierrez, Carlos A. Hurtado, and Alejandro Vaisman, “Introducing Time into RDF,” IEEE Trans. Knowl. Data Eng., vol. 19, no. 2, pp. 207–218, 2007.
[18] HM Government, “Domestic Building Services,” 2018.
[19] M. Schmidt, T. Hornung, G. Lausen, and C. Pinkel, “SP^2Bench: A SPARQL Performance Benchmark,” in 2009 IEEE 25th International Conference on Data Engineering, 2009, pp. 222–233.
[20] “Programming with GraphDB — GraphDB Free 8.9 documentation.” [Online].
Available: http://graphdb.ontotext.com/free/devhub/programming.html. [Accessed:
07-Jun-2019].
[21] “Eclipse rdf4j | The Eclipse Foundation.” [Online]. Available:
https://rdf4j.eclipse.org/. [Accessed: 29-May-2019].
[22] Expert Spring MVC and Web Flow. Apress, 2006.
[23] G. KONG, D.-L. XU, and J.-B. YANG, “Clinical Decision Support Systems: a Review of Knowledge Representation and Inference under Uncertainties,” Int. J.
Comput. Intell. Syst., vol. 1, no. 2, p. 159, 2008.
[24] I. B. Arpinar, R. Zhang, B. Aleman-Meza, and A. Maduko, “Ontology-driven Web services composition platform,” Inf. Syst. E-bus. Manag., vol. 3, no. 2, pp. 175–199, Jul. 2005.
[25] I. Jurisica, J. Mylopoulos, and E. Yu, “Ontologies for Knowledge Management: An Information Systems Perspective,” Knowl. Inf. Syst., vol. 6, no. 4, pp. 380–401, Jul.
2004.
[26] C. Asamoah, L. Tao, K. Gai, and N. Jiang, “Powering Filtration Process of Cyber Security Ecosystem Using Knowledge Graph,” in 2016 IEEE 3rd International Conference on Cyber Security and Cloud Computing (CSCloud), 2016, pp. 240–
246.
[27] M. Iannacone et al., “Developing an Ontology for Cyber Security Knowledge Graphs,” in Proceedings of the 10th Annual Cyber and Information Security Research Conference on - CISR ’15, 2015, pp. 1–4.
43
[28] J. Z. Pan et al., Reasoning Web: Logical Foundation of Knowledge Graph Construction and Query Answering : 12th International Summer School 2016, Aberdeen, UK, September 5-9, 2016, Tutorial Lectures. .
[29] M. R. Quillian, “Word concepts: A theory and simulation of some basic semantic capabilities,” Behav. Sci., vol. 12, no. 5, pp. 410–430, Sep. 1967.
[30] S. Decker et al., “The Semantic Web: the roles of XML and RDF,” IEEE Internet Comput., vol. 4, no. 5, pp. 63–73, 2000.
[31] J. Z. Pan, “DESCRIPTION LOGICS: REASONING SUPPORT FOR THE SEMANTIC WEB,” 2004.
[32] F. Baader, D. Calvanese, D. L. McGuinness, D. Nardi, and P. F. Patel-Schneider, Eds., The Description Logic Handbook. Cambridge: Cambridge University Press, 2007.
[33] B. Villazon-Terrazas et al., “Knowledge Graph Foundations,” in Exploiting Linked Data and Knowledge Graphs in Large Organisations, Cham: Springer International Publishing, 2017, pp. 17–55.
[34] D. Raynolds, “The Organization Ontology.” [Online]. Available:
https://www.w3.org/TR/vocab-org/. [Accessed: 27-May-2019].
[35] M. Hepp, “GoodRelations: The Professional Web Vocabulary for E-Commerce.”
[Online]. Available: http://www.heppnetz.de/projects/goodrelations/. [Accessed: 28-May-2019].
[36] D. Brickley and L. Miller, “FOAF Vocabulary Specification.” [Online]. Available:
http://xmlns.com/foaf/spec/. [Accessed: 28-May-2019].
[37] The code4lib journal. Code4Lib Journal, 2007.
[38] J. Huang et al., “OmniSearch: a semantic search system based on the Ontology for MIcroRNA Target (OMIT) for microRNA-target gene interaction data.,” J. Biomed.
Semantics, vol. 7, p. 25, 2016.
[39] Ozonat Mehmet and Bartolini Claudio, “Building a semantics graph for an enterprise communication network,” Jan. 2013.
[40] A. J. G. Gray, N. Gray, and I. Ounis, “Can RDB2RDF Tools Feasibily Expose Large Science Archives for Data Integration?,” Springer, Berlin, Heidelberg, 2009, pp. 491–505.
[41] E. Montiel-Ponsoda, V. Rodríguez-Doncel, and J. Gracia, “Building the legal
44
knowledge graph for smart compliance services in multilingual Europe,” CEUR Workshop Proc., vol. 2049, no. December 2017, pp. 15–17, 2018.
[42] “Representing enterprise data in a knowledge graph,” May 2013.
[43] G. Weikum and M. Theobald, “From information to knowledge,” in Proceedings of the twenty-ninth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems of data - PODS ’10, 2010, p. 65.
[44] R. Denaux, Y. Ren, B. Villazon-Terrazas, P. Alexopoulos, A. Faraotti, and H. Wu,
“Knowledge Architecture for Organisations,” in Exploiting Linked Data and Knowledge Graphs in Large Organisations, Cham: Springer International Publishing, 2017, pp. 57–84.
[45] B. Villazon-Terrazas et al., “Construction of Enterprise Knowledge Graphs (I),” in Exploiting Linked Data and Knowledge Graphs in Large Organisations, Cham:
Springer International Publishing, 2017, pp. 87–116.
[46] T. Heath and C. Bizer, “Linked Data: Evolving the Web into a Global Data Space,”
Synth. Lect. Semant. Web Theory Technol., vol. 1, no. 1, pp. 1–136, Feb. 2011.
[47] M. C. Suárez-Figueroa, “NeOn Methodology for Building Ontology Networks:Specification, Scheduling and Reuse,” Jun. 2010.
[48] L. Ding et al., “Swoogle,” in Proceedings of the Thirteenth ACM conference on Information and knowledge management - CIKM ’04, 2004, p. 652.
[49] P.-Y. Vandenbussche, G. A. Atemezing, M. Poveda-Villalón, and B. Vatant,
“Linked Open Vocabularies (LOV): A gateway to reusable semantic vocabularies on the Web,” Semant. Web, vol. 8, no. 3, pp. 437–452, Dec. 2016.
[50] A. Dimou, M. Vander Sande, P. Colpaert, R. Verborgh, E. Mannens, and R. Van De Walle, “RML: A Generic Language for Integrated RDF Mappings of Heterogeneous Data.”
[51] A. Langegger and W. Wöß, “XLWrap – Querying and Integrating Arbitrary Spreadsheets with SPARQL,” Springer, Berlin, Heidelberg, 2009, pp. 359–374.
[52] “Apache Jena.” [Online]. Available: http://jena.apache.org/. [Accessed: 29-May-2019].
[53] “OpenLink Virtuoso.” [Online]. Available: https://virtuoso.openlinksw.com/.
[Accessed: 29-May-2019].
[54] A. Hogan, A. Harth, A. Passant, S. Decker, and A. Polleres, “Weaving the Pedantic
45 Web LDOW 2010,” 2009.
[55] Z. Zhao, S.-K. Han, and I.-M. So, “Architecture of Knowledge Graph Construction Techniques,” Int. J. Pure Appl. Math., vol. 118, no. 19, pp. 1869–1883, 2018.
[56] M. Uschold and M. Gruninger, “Ontologies: principles, methods and applications,”
Knowl. Eng. Rev., vol. 11, no. 2, pp. 93–136, Jun. 1996.
[57] Y. Ren, A. Parvizi, C. Mellish, J. Z. Pan, K. van Deemter, and R. Stevens, “Towards Competency Question-Driven Ontology Authoring,” Springer, Cham, 2014, pp.
752–767.
[58] S. R. (Stephen R. Palmer and J. M. Felsing, A practical guide to feature-driven development. Prentice Hall PTR, 2002.
[59] C. R. Kothari, Research methodology : methods & techniques. New Age International (P) Ltd, 2004.
[60] N. B. Ruparelia and N. B., “Software development lifecycle models,” ACM SIGSOFT Softw. Eng. Notes, vol. 35, no. 3, p. 8, May 2010.
[61] “Data Catalog Vocabulary (DCAT).” [Online]. Available:
https://www.w3.org/TR/vocab-dcat/. [Accessed: 07-Jun-2019].
[62] “WebVOWL - Web-based Visualization of Ontologies.” [Online]. Available:
http://vowl.visualdataweb.org/webvowl.html. [Accessed: 07-Jun-2019].
[63] R. Hecht and S. Jablonski, “NoSQL evaluation: A use case oriented survey,” in 2011 International Conference on Cloud and Service Computing, 2011, pp. 336–
341.
[64] M. A. Hernández, S. J. Stolfo, M. A. Hernández, and S. J. Stolfo, “The merge/purge problem for large databases,” in Proceedings of the 1995 ACM SIGMOD
international conference on Management of data - SIGMOD ’95, 1995, vol. 24, no.
2, pp. 127–138.
[65] A. Klimova, E. Rondeau, K. Andersson, J. Porras, A. Rybin, and A. Zaslavsky, “An international Master’s program in green ICT as a contribution to sustainable
development,” J. Clean. Prod., vol. 135, pp. 223–239, Nov. 2016.
[66] L. M. Hilty and M. D. Hercheui, “ICT and Sustainable Development,” Springer, Berlin, Heidelberg, 2010, pp. 227–235.
[67] J. Morelli, “Environmental Sustainability: A Definition for Environmental Professionals,” J. Environ. Sustain., vol. 1.
46
[68] C. Becker et al., “Requirements: The Key to Sustainability,” IEEE Softw., vol. 33, no. 1, pp. 56–65, Jan. 2016.
[69] L. Duboc et al., “Do we really know what we are building? Raising awareness of potential Sustainability Effects of Software Systems in Requirements Engineering.”
IEEE Computer Society, 22-Jun-2019.