Abstract
Procurement affects virtually all sectors and organizations particularly in times of slow economic recovery and enhanced transparency. Public spending alone will soon exceed EUR 2 trillion per annum in the EU. Therefore, there is a pressing need for better insight into, and management of government spending. In the absence of data and tools to analyse and oversee this complex process, too little consideration is given to the development of vibrant, competitive economies when buying decisions are made. To this end, in this short paper, we report our ongoing work for enabling procurement data value chains through a knowledge graph based platform with data management, analytics, and interaction.
This work is funded by EU H2020 TheyBuyForYou project (780247).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Alvarez-Rodríguez, J.M., et al.: New trends on e-procurement applying semantic technologies: current status and future challenges. Comput. Ind. 65(5), 800–820 (2014)
Araújo, S., et al.: SERIMI: class-based matching for instance matching across heterogeneous datasets. IEEE Trans. Knowl. Data Eng. 27(5), 1397–1410 (2015)
Biega, J., et al.: Inside YAGO2s: a transparent information extraction architecture. In: Proceedings of the 22nd International Conference on World Wide Web (WWW 2013), pp. 325–328. ACM, New York (2013)
Chandola, V., et al.: Anomaly detection: a survey. ACM Comput. Surv. 41(3), 15:1–15:58 (2009)
Distinto, I., et al.: LOTED2: an ontology of European public procurement notices. Semant. Web 7(3), 267–293 (2016)
Dorneles, C.F., et al.: Approximate data instance matching: a survey. Knowl. Inf. Syst. 27(1), 1–21 (2011)
Fortuna, B., et al.: A kernel canonical correlation analysis for learning the semantics of text. In: Kernel Methods in Bioengineering, Communications and Image Processing (2006)
Kharlamov, E., et al.: Ontology based data access in statoil. Web Semant.: Sci. Serv. Agents World Wide Web 44, 3–36 (2017)
Lehmann, J., et al.: DBpedia - a large-scale, multilingual knowledge base extracted from Wikipedia. Semant. Web 6(2), 167–195 (2015)
Muñoz-Soro, J.F., et al.: PPROC, an ontology for transparency in public procurement. Semant. Web 7(3), 295–309 (2016)
Necaský, M., et al.: Linked data support for filing public contracts. Comput. Ind. 65(5), 862–877 (2014)
Paulheim, H.: Knowledge graph refinement: a survey of approaches and evaluation methods. Semant. Web 8(3), 489–508 (2017)
Portet, F., et al.: Automatic generation of textual summaries from neonatal intensive care data. Artif. Intell. 173(7), 789–816 (2009)
Rodríguez, J.M.Á., et al.: Towards a Pan-European e-procurement platform to aggregate, publish and search public procurement notices powered by linked open data: the moldeas approach. Int. J. Softw. Eng. Knowl. Eng. 22(3), 365–384 (2012)
Shanbhag, P., et al.: Temporal visualization of planning polygons for efficient partitioning of geo-spatial data. In: Proceedings of the IEEE Symposium on Information Visualization (InfoVis 2005). IEEE Computer Society, Washington, DC (2005)
Smith, M.A., et al.: Analyzing (social media) networks with NodeXL. In: Proceedings of the 4th International Conference on Communities and Technologies, pp. 255–264. ACM, New York (2009)
Suchanek, F.M., et al.: Knowledge bases in the age of big data analytics. Proc. VLDB Endowment 7(13), 1713–1714 (2014)
Yan, J., et al.: A retrospective of knowledge graphs. Front. Comput. Sci. 12(1), 55–74 (2018)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Simperl, E. et al. (2019). Towards a Knowledge Graph Based Platform for Public Procurement. In: Garoufallou, E., Sartori, F., Siatri, R., Zervas, M. (eds) Metadata and Semantic Research. MTSR 2018. Communications in Computer and Information Science, vol 846. Springer, Cham. https://doi.org/10.1007/978-3-030-14401-2_29
Download citation
DOI: https://doi.org/10.1007/978-3-030-14401-2_29
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-14400-5
Online ISBN: 978-3-030-14401-2
eBook Packages: Computer ScienceComputer Science (R0)