International Journal of Information Engineering and Electronic Business (IJIEEB)
IJIEEB Vol. 10, No. 5, 8 Sep. 2018
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Using Publications Linked Open Data to Define Organizational Policies
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Author(s)
Index Terms
Linked open data, policy making, RDF, knowledge representation, data analysis, reasoning
Abstract
Researchers around the world are publishing their scientific research results in different forms such as books, journal articles, reference works and project reports. Publishers of these scientific documents usually describe them by using metadata for organizational purposes. This metadata provides a rich information about scientific documents that can be used for analysis purposes such as measuring the impact of researchers and research centers. It can also be used to find scientific documents published in domain of some ones interest, which ultimately can be used to raise the state of the art to the next level. Scientific publications metadata can also be used to analyze the quality and directions of common and highly cited individuals and organizations, and based on this analysis other individuals and organizations can define directions for their future work and research. However, the main limitation of this metadata is that it is available in different formats that might not facilitate the analysis of scientific documents. Therefore, in this paper we clarify that how our SPedia knowledge base (a semantic based knowledge base of scientific publications metadata which we extracted by using SpringerLink as information source) facilitates the analysis of scientific data for policy making. We discuss different kind of questions that can be answered through SPedia knowledge base and we show that how results of these questions can be used to analyze the performance of individuals as well as organizations. We also show that how results of such analysis can help in making organizational policies regarding future research directions.
Cite This Paper
Muhammad Ahtisham Aslam, "Using Publications Linked Open Data to Define Organizational Policies", International Journal of Information Engineering and Electronic Business(IJIEEB), Vol.10, No.5, pp. 8-14, 2018. DOI:10.5815/ijieeb.2018.05.02
Reference
[1]N. Piedra, J. Chicaiza, J. Atenas, J. Lopez-Vargas, and E. Tovar, Using Linked Data to Blended Educational Materials With OER—A General Context of Synergy: Linked Data for Describe, Discovery and Retrieve OER and Human Beings Knowledge to Provide Context. Berlin, Heidelberg: Springer Berlin Heidelberg, 2017, pp. 283–313.
[2]J. Chicaiza, N. Piedra, J. Lopez-Vargas, and E. Tovar-Caro, “Domain categorization of open educational resources based on linked data,” in Knowledge Engineering and the Semantic Web, P. Klinov and D. Mouromtsev, Eds. Cham: Springer International Publishing, 2014, pp. 15–28.
[3]D. Burkhardt, K. Nazemi, W. Retz, and J. Kohlhammer, “Visual explanation of government-data for policy making through open-data inclusion,” in The 9th International Conference for Internet Technology and Secured Transactions (ICITST-2014), Dec 2014, pp. 83–89.
[4]H. F. Chan, B. S. Frey, J. Gallus, M. Schaffner, B. Torgler, and S. Whyte, “Do the best scholars attract the highest speaking fees? an exploration of internal and external influence,” Scientometrics, vol. 101, no. 1, pp. 793–817, 2014. [Online]. Available: http://dx.doi.org/10.1007/s11192014-1379-3
[5]B. Aleman-Meza, F. Hakimpour, I. B. Arpinar, and A. P. Sheth, “Swetodblp ontology of computer science publications,” Web Semantics: Science, Services and Agents on the World Wide Web, vol. 5, no. 3, pp. 151 – 155, 2007. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S1570826807000121
[6]R. Manrique, O. Herazo, and O. Marino, “Exploring the use of linked˜ open data for user research interest modeling,” in Advances in Computing, A. Solano and H. Ordonez, Eds.˜ Cham: Springer International Publishing, 2017, pp. 3–16.
[7]M. A. Aslam and N. R. Aljohani, “SPedia: A semantics based repository of scientific publications data,” in Web-Age Information Management: 17th International Conference, WAIM 2016, Nanchang, China, June 3-5, 2016, Proceedings, Part I, B. Cui, N. Zhang, J. Xu, X. Lian, and D. Liu, Eds. Cham: Springer International Publishing, 2016, pp. 479–490.
[8]M. A. Aslam and N. R. Aljohani, “SPedia: A Central Hub for the Linked Open Data of Scientific Publications,” International Journal on Semantic Web and Information Systems (IJSWIS), vol. 13, no. 1, pp. 128–146, 2017. [Online]. Available: http://www.igi-global.com/article/SPedia/172426
[9]G. Gombos and A. Kiss, SPARQL Processing over the Linked Open Data with Automatic Endpoint Detection. Cham: Springer International Publishing, 2014, pp. 183–192.
[10]K. Debackere and W. Glanzel, “Using a bibliometric approach to¨ support research policy making: The case of the flemish bof-key,” Scientometrics, vol. 59, no. 2, pp. 253–276, 2004. [Online]. Available: http://dx.doi.org/10.1023/B:SCIE.0000018532.70146. 02
[11]O. N. Ventura and A. W. Mombru, “Use of bibliometric information to´ assist research policy making. a comparison of publication and citation profiles of full and associate professors at a school of chemistry in uruguay,” Scientometrics, vol. 69, no. 2, pp. 287–313, 2006. [Online]. Available: http://dx.doi.org/10.1007/s11192-006-0154-5
[12]S.-U. Hassan, P. Haddawy, P. Kuinkel, A. Degelsegger, and C. Blasy, “A bibliometric study of research activity in asean related to the eu in fp7 priority areas,” Scientometrics, vol. 91, no. 3, pp. 1035–1051, 2012. [Online]. Available: http://dx.doi.org/10.1007/s11192-012-0665-1
[13]F. Jimenez-S´ aez, J. M. Zabala-Iturriagagoitia, and J. L. Zof´ ´ıo, “Who leads research productivity growth? guidelines for r&d policy-makers,” Scientometrics, vol. 94, no. 1, pp. 273–303, 2013. [Online]. Available: http://dx.doi.org/10.1007/s11192-012-0763-0
[14]C. Huang, J. Su, X. Xie, X. Ye, Z. Li, A. Porter, and J. Li, “A bibliometric study of china’s science and technology policies: 1949–2010,” Scientometrics, vol. 102, no. 2, pp. 1521–1539, 2015. [Online]. Available: http://dx.doi.org/10.1007/s11192-014-1406-4
[15]T. Turko, G. Bakhturin, V. Bagan, S. Poloskov, and D. Gudym, “Influence of the program “5–top 100” on the publication activity of russian universities,” Scientometrics, vol. 109, no. 2, pp. 769–782, 2016. [Online]. Available: http://dx.doi.org/10.1007/s11192- 016-2060-9
[16]C. Daraio, M. Lenzerini, C. Leporelli, H. F. Moed, P. Naggar, A. Bonaccorsi, and A. Bartolucci, “Data integration for research and innovation policy: an ontology-based data management approach,” Scientometrics, vol. 106, no. 2, pp. 857–871, 2016. [Online]. Available: http://dx.doi.org/10.1007/s11192-015-1814-0
[17]M. Schmidt, M. Meier, and G. Lausen, “Foundations of sparql query optimization,” in Proceedings of the 13th International Conference on Database Theory, ser. ICDT ’10. New York, NY, USA: ACM, 2010, pp. 4–33. [Online]. Available: http://doi.acm.org/10.1145/1804669.1804675
[18]P. Peng, L. Zou, M. T. Ozsu, L. Chen, and D. Zhao, “Processing¨ sparql queries over distributed rdf graphs,” The VLDB Journal, vol. 25, no. 2, pp. 243–268, Apr. 2016. [Online]. Available: http://dx.doi.org/10.1007/s00778-015-0415-0
[19]F. Kerdjoudj and O. Cure, “RDF knowledge graph visualization from´ a knowledge extraction system,” CoRR, vol. abs/1510.00244, 2015. [Online]. Available: http://arxiv.org/abs/1510.00244
[20]R. Chawuthai and H. Takeda, “Rdf graph visualization by interpreting linked data as knowledge,” in Semantic Technology, G. Qi, K. Kozaki, J. Z. Pan, and S. Yu, Eds. Cham: Springer International Publishing, 2016, pp. 23–39.
[21]S. Elbassuoni, M. Ramanath, R. Schenkel, and G. Weikum, “Searching RDF graphs with SPARQL and keywords,” IEEE Data Eng. Bull., vol. 33, no. 1, pp. 16–24, 2010. [Online]. Available: http://sites.computer.org/debull/A10mar/weikum-paper.pdf