REDUCED CONCEPT LATTICE USABILITY – AN ANALYSIS OF METADATA EVOLVEMENT IMPACT
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J. Berends, W. Carrara and C. Radu, “The economic benefits of open data,” Analytical report 9, 2017.
M. Barbero et al., “Study to support the review of directive 2003/98. EC on the re-use of public sector information,” 2018, ISBN 978-92-79-83169-0. [Online]. Available: http://doi.org/10.2759/ 373622.
T. Davies, S.B. Walker, M. Rubinstei and F. Perini, “The State of Open Data: Histories and Horizons,” Cape Town and Ottawa: African Minds and IDRC, Published by African Minds and the IDRC, 2019. [Online]. Available: http://doi.org/10.5281/zenodo.2668475.
K. Braunschweig, J. Eberius, M. Thiele, et al., “The State of Open Data Limits of Current Open Data Platforms”, 2012.
K. J. Reiche, E. Höfig, “Implementation of metadata quality metrics and application on public government data,” In 2013 IEEE 37th An-nual Computer Software and Applications Conference Workshops, pp. 236–241, 2013. [Online]. Available: http://doi.org/10.1109/COMPSACW.2013. 32.
A. Zuiderwijk, C. Volten, M. Kroesen, et al, “Motivation perspectives on opening up municipality data: Does municipality size matter?” Information 9, no. 11: 267. [Online]. Available: http://doi.org/10.3390/info9110267.
M. Frtunić Gligorijević, M. Bogdanović, and L. Stoimenov, “Tracking metadata changes in the government open data portals,” In ICIST 2022 Proceedings, pp.180-184, 2022. ISBN 978-86-85525-24-7
R. Wille, “Restructuring lattice theory: an approach based on hierarchies of concepts,” In: Ferré S., Rudolph S. (eds) Formal Concept Analysis. Lecture Notes in Computer Science. Vol. 5548, Springer, Berlin, Heidelberg, 2009.
J. Poelmans, P. Elzinga, S. Viaene, G. Dedene, “Formal concept analysis in knowledge discovery: a survey,” In Proceedings of the International conference on conceptual structures, pp. 139-153. Springer, Berlin, Heidelberg, 2009.
J. Poelmans, S. O. Kuznetsov, D. I. Ignatov, D. Dedene, “Formal concept analysis in knowledge processing: A survey on models and techniques,” Expert systems with applications. 40(16), 6601-6623, 2013.
M. W. Chekol, A. Napoli, “An FCA framework for knowledge discovery in SPARQL query answers,” In Proceedings of the 12th International Semantic Web Conference, 2013.
P. Valtchev, R. Missaoui, R. Godin, “Formal concept analysis for knowledge discovery and data mining: The new challenges,” In Proceedings of the International conference on formal concept analysis, pp. 352-371, Springer, Berlin, Heidelberg, 2004.
M. Alam, A. Buzmakov, V. Codocedo, A. Napoli, “Mining definitions from RDF annotations using formal concept analysis,” In Proceedings of the International Joint Conference in Artificial Intelligence, 2015.
M. Alam, T. N. N. Le, A. Napoli, “Latviz: A new practical tool for performing interactive exploration over concept lattices,” In CLA 2016-Thirteenth International Conference on Concept Lattices and Their Applications, 2016.
F. Venter, D. G. Kourie, B. W. Watson, “FCA-based two dimensional pattern matching,” In International Conference on Formal Concept Analysis, pp. 299-313. Springer, Berlin, Heidelberg, 2009.
G. Li, “DeepFCA: Matching biomedical ontologies using formal concept analysis embedding techniques,” In Proceedings of the 4th International Conference on Medical and Health Informatics, pp. 259-265, 2020.
B. Zhou, S. C. Hui, K. Chang, “A formal concept analysis approach for web usage mining,” In Proceedings of the International Conference on Intelligent Information Processing, pp. 437-441, Springer, Boston, MA, 2004.
H. He, H. Hai, W. Rujing, “FCA-based web user profile mining for topics of interest,” In Proceedings of the IEEE International Conference on Integration Technology, pp. 778-782, 2007.
P. K. Singh, A. K. Cherukuri, A. Gani, “A comprehensive survey on formal concept analysis, its research trends and applications,” International Journal of Applied Mathematics and Computer Science, 2016. [Online]. Available: http://doi.org/10.1515/amcs-2016-0035
H. Azibi, N. Meddouri, M. Maddouri, “Survey on Formal Concept Analysis Based Supervised Classification Techniques, Machine Learning and Artificial Intelligence, pp. 21-29, IOS Press, 2020. [Online]. Available: http://doi.org/10.3233/FAIA200762
P. Krajca, J. Outrata, V. Vychodil, “Parallel recursive algorithm for FCA,” in CLA, pp. 71–82, 2008.
J. Dean and S. Ghemawat, “MapReduce: Simplified data processing on large clusters,” OSDI'04: Sixth Symposium on Operating System Design and Implementation, San Francisco, pp. 137-150, 2004.
P. Krajcaand, V. Vychodil, “Distributed algorithm for computing formal concepts using map-reduce framework,” in International Symposium on Intelligent Data Analysis,pp. 333–344, 2009. [Online]. Available: http://doi.org/10.1007/978-3-642-03915-7_29.
R. K. Chunduri, A. K. Cherukuri, M. Tamir, “Concept generation in formal concept analysis using MapReduce framework,” in 2017 International Conference on Big Data Analytics and Computational Intelligence (ICBDAC), pp. 191–204, 2017. [Online]. Available: http://doi.org/10.1109/ICBDACI.2017.8070834.
M. Alwersh, L. Kovács, “Survey on attribute and concept reduction methods in formal concept analysis,” Indonesian Journal of Electrical Engineering and Computer Science, vol.30, no.1, pp. 366~387, 2023. [Online]. Available: http://doi.org/10.11591/ijeecs.v30.i1.pp366-387
J. Li, C. Mei, Y. Lv, “A heuristic knowledge-reduction method for decision formal contexts,” Computers & Mathematics with Applications, vol. 61, no. 4, pp. 1096–1106, 2011. [Online]. Available: http://doi.org/10.1016/j.camwa.2010.12.060.
J. Medina, “Relating attribute reduction in formal, object-oriented and property-oriented concept lattices,” Computers & Mathematics with Applications, vol.64, no. 6, pp. 1992–2002, 2012. [Online]. Available: http://doi.org/10.1016/j.camwa.2012.03.087.
H. Wang W.-X. Zhang, “Approaches to knowledge reduction in generalized consistent decision formal context,” Math Comput Model, vol. 48, no. 11–12, pp. 1677–1684, 2008. doi: 10.1016/j.mcm.2008.06.007.
L. Antoni, M. E. Cornejo, J. Medina, E. Ramírez-Poussa, “Attribute classification and reduct computation in multi-adjoint concept lattices,” IEEE Transactions on Fuzzy Systems, vol. 29, no. 5, pp. 1121–1132, 2020. [Online]. Available: http://doi.org/10.1109/TFUZZ.2020.2969114.
S. M. Dias N. Vieira, “Reducing the size of concept lattices: The JBOS approach,” in Cla, vol. 672, pp. 80–91, 2010.
V. Codocedo, C. Taramasco, H. Astudillo, “Cheating to achieve formal concept analysis over a large formal context,” in The Eighth International Conference on Concept Lattices and their Applications-CLA 2011, pp. 349–362, 2011.
C. A. Kumar, S. Srinivas, “Mining associations in health care data using formal concept analysis and singular value decomposition,” J Biol Syst, vol. 18, no. 04, pp. 787–807, 2010. [Online]. Available: http://doi.org/10.1142/S0218339010003512.
G. Stumme, R. Taouil, Y. Bastide, N. Pasquier, L. Lakhal, “Computing iceberg concept lattices with titanic,” Data & Knowledge Engineering, vol. 42, no. 2, pp. 189–222, 2002. [Online]. Available: http://doi.org/10.1016/S0169-023X(02)00057-5.
P. K. Singh, C. A. Kumar, “Concept lattice reduction using different subset of attributes as information granules,” Granular Computing, vol. 2, no. 3, pp. 159–173, 2017. [Online]. Available: http://doi.org/10.1007/s41066-016-0036-z.
M. Frtunić Gligorijević, M. Bogdanović, L. Stoimenov, "An Approach for Metadata Enrichment on Open Data Portals," In 11th International Conference on Electrical, Electronic and Computing Engineering (IcETRAN), Nis, Serbia, pp. 1-5, 2024.
J. Pennington, R. Socher, C. Manning, “Glove: Global vectors for word representation,” In Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP), pp. 1532-1543, 2014.
M. Bogdanović, N. Veljković, M. Frtunić Gligorijević, D. Puflović, L. Stoimenov, “On revealing shared conceptualization among open datasets,” Journal of Web Semantics, vol. 66, 2021. [Online]. Available: http://doi.org/10.1016/j.websem.2020.100624.
N. Rekabsaz, M. Lupu A. Hanbury, “Exploration of a Threshold for Similarity based on Uncertainty in Word Embedding,” In: Jose, J., et al. Advances in Information Retrieval. ECIR 2017. Lecture Notes in Computer Science(), vol 10193. Springer, Cham., 2017. [Online]. Available: http://doi.org/10.1007/978-3-319-56608-5_31
DOI: https://doi.org/10.22190/FUACR250429005F
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