SL({}_{}) Logic: Elimination of Data Redundancy in Knowledge Representation

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1 January 2002

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Advances in Artificial Intelligence - {IBERAMIA} 2002, 8th Ibero-American Conference on AI, Seville, Spain, November 12-15, 2002, Proceedings , Lecture Notes in Computer Science vol. 2527, pages 141–150.

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Please, cite this work as:

[Cor+02] P. Cordero, M. Enciso, Á. Mora, et al. “SL(_) Logic: Elimination of Data Redundancy in Knowledge Representation”. In: Advances in Artificial Intelligence - IBERAMIA 2002, 8th Ibero-American Conference on AI, Seville, Spain, November 12-15, 2002, Proceedings. Ed. by F. J. Garijo, J. C. R. Santos and M. Toro. Vol. 2527. Lecture Notes in Computer Science. Springer, 2002, pp. 141-150. DOI: 10.1007/3-540-36131-6_15. URL: https://doi.org/10.1007/3-540-36131-6_15.

BibTeX

@InProceedings{Cordero2002b,
     author = {Pablo Cordero and Manuel Enciso and {’A}ngel Mora and Inman P. {de Guzm{’a}n}},
     booktitle = {Advances in Artificial Intelligence - {IBERAMIA} 2002, 8th Ibero-American Conference on AI, Seville, Spain, November 12-15, 2002, Proceedings},
     title = {SL({}_{}) Logic: Elimination of Data Redundancy in Knowledge Representation},
     year = {2002},
     editor = {Francisco J. Garijo and Jos{’e} Crist{’o}bal Riquelme Santos and Miguel Toro},
     pages = {141–150},
     publisher = {Springer},
     series = {Lecture Notes in Computer Science},
     volume = {2527},
     bibsource = {dblp computer science bibliography, https://dblp.org},
     biburl = {https://dblp.org/rec/conf/iberamia/CorderoEMG02.bib},
     doi = {10.1007/3-540-36131-6_15},
     timestamp = {Mon, 05 Feb 2024 00:00:00 +0100},
     url = {https://doi.org/10.1007/3-540-36131-6_15},
}

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Citations
CrossRef - Citation Indexes: 14
Scopus - Citation Indexes: 44

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Mendeley - Readers: 11

Cites

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Papers citing this work

The following is a non-exhaustive list of papers that cite this work:

[1] G. Aguilera, P. Cordero, M. Enciso, et al. “A Non-explosive Treatment of Functional Dependencies Using Rewriting Logic”. In: Advances in Artificial Intelligence – SBIA 2004. Springer Berlin Heidelberg, 2004, p. 31–40. ISBN: 9783540286455. DOI: 10.1007/978-3-540-28645-5_4. URL: http://dx.doi.org/10.1007/978-3-540-28645-5_4.

[2] R. Belohlavek, P. Cordero, M. Enciso, et al. “An Efficient Reasoning Method for Dependencies over Similarity and Ordinal Data”. In: Modeling Decisions for Artificial Intelligence. Springer Berlin Heidelberg, 2012, p. 408–419. ISBN: 9783642346200. DOI: 10.1007/978-3-642-34620-0_36. URL: http://dx.doi.org/10.1007/978-3-642-34620-0_36.

[3] F. Benito-Picazo, P. Cordero, M. Enciso, et al. “Minimal generators, an affordable approach by means of massive computation”. In: The Journal of Supercomputing 75.3 (Jun. 2018), p. 1350–1367. ISSN: 1573-0484. DOI: 10.1007/s11227-018-2453-z. URL: http://dx.doi.org/10.1007/s11227-018-2453-z.

[4] F. Benito-Picazo, P. Cordero, M. Enciso, et al. “Reducing the search space by closure and simplification paradigms: A parallel key finding method”. In: The Journal of Supercomputing 73.1 (Jan. 2016), p. 75–87. ISSN: 1573-0484. DOI: 10.1007/s11227-016-1622-1. URL: http://dx.doi.org/10.1007/s11227-016-1622-1.

[5] F. Benito‐Picazo, M. Enciso, C. Rossi, et al. “Enhancing the conversational process by using a logical closure operator in phenotypes implications”. In: Mathematical Methods in the Applied Sciences 41.3 (Feb. 2017), p. 1089–1100. ISSN: 1099-1476. DOI: 10.1002/mma.4338. URL: http://dx.doi.org/10.1002/mma.4338.

[6] P. Cordero, M. Enciso, D. López-Rodríguez, et al. “fcaR, Formal Concept Analysis with R”. In: The R Journal 14.1 (Jun. 2022), p. 341–361. ISSN: 2073-4859. DOI: 10.32614/rj-2022-014. URL: http://dx.doi.org/10.32614/rj-2022-014.

[7] P. Cordero, M. Enciso, A. Mora, et al. “A Complete Logic for Fuzzy Functional Dependencies over Domains with Similarity Relations”. In: Bio-Inspired Systems: Computational and Ambient Intelligence. Springer Berlin Heidelberg, 2009, p. 261–269. ISBN: 9783642024788. DOI: 10.1007/978-3-642-02478-8_33. URL: http://dx.doi.org/10.1007/978-3-642-02478-8_33.

[8] P. Cordero, M. Enciso, A. Mora, et al. “An Efficient Algorithm for Reasoning about Fuzzy Functional Dependencies”. In: Advances in Computational Intelligence. Springer Berlin Heidelberg, 2011, p. 412–420. ISBN: 9783642214981. DOI: 10.1007/978-3-642-21498-1_52. URL: http://dx.doi.org/10.1007/978-3-642-21498-1_52.

[9] P. Cordero, M. Enciso, A. Mora, et al. “Computing non-redundant sets of functional dependencies via simplification”. In: 2013 IEEE Symposium on Foundations of Computational Intelligence (FOCI). IEEE, Apr. 2013, p. 9–14. DOI: 10.1109/foci.2013.6602449. URL: http://dx.doi.org/10.1109/foci.2013.6602449.

[10] P. Cordero, M. Enciso, A. Mora, et al. “Knowledge discovery in social networks by using a logic-based treatment of implications”. In: Knowledge-Based Systems 87 (Oct. 2015), p. 16–25. ISSN: 0950-7051. DOI: 10.1016/j.knosys.2015.07.018. URL: http://dx.doi.org/10.1016/j.knosys.2015.07.018.

[11] P. Cordero, M. Enciso, A. Mora, et al. “A tableaux-like method to infer all minimal keys”. In: Logic Journal of IGPL 22.6 (Sep. 2014), p. 1019–1044. ISSN: 1368-9894. DOI: 10.1093/jigpal/jzu025. URL: http://dx.doi.org/10.1093/jigpal/jzu025.

[12] P. Cordero, M. Enciso, A. Mora, et al. “Specification and inference of fuzzy attributes”. In: 2011 IEEE Symposium on Foundations of Computational Intelligence (FOCI). IEEE, Apr. 2011, p. 107–114. DOI: 10.1109/foci.2011.5949472. URL: http://dx.doi.org/10.1109/foci.2011.5949472.

[13] P. Cordero, M. Enciso, A. Mora, et al. “Inference of Mixed Information in Formal Concept Analysis”. In: Trends in Mathematics and Computational Intelligence. Springer International Publishing, Oct. 2018, p. 81–87. ISBN: 9783030004859. DOI: 10.1007/978-3-030-00485-9_9. URL: http://dx.doi.org/10.1007/978-3-030-00485-9_9.

[14] P. Cordero, A. Mora, I. de Guzmán, et al. “Non-deterministic ideal operators: An adequate tool for formalization in Data Bases”. In: Discrete Applied Mathematics 156.6 (Mar. 2008), p. 911–923. ISSN: 0166-218X. DOI: 10.1016/j.dam.2007.02.014. URL: http://dx.doi.org/10.1016/j.dam.2007.02.014.

[15] A. Mora, G. Aguilera, M. Enciso, et al. “A new closure algorithm based in logic: SLFD-Closure versus classical closures.” In: INTELIGENCIA ARTIFICIAL 10.31 (Dec. 2006). ISSN: 1137-3601. DOI: 10.4114/ia.v10i31.935. URL: http://dx.doi.org/10.4114/ia.v10i31.935.

[16] A. Mora, P. Cordero, M. Enciso, et al. “Closure via functional dependence simplification”. In: International Journal of Computer Mathematics 89.4 (Mar. 2012), p. 510–526. ISSN: 1029-0265. DOI: 10.1080/00207160.2011.644275. URL: http://dx.doi.org/10.1080/00207160.2011.644275.

[17] Á. Mora, M. Enciso, P. Cordero, et al. “An Efficient Preprocessing Transformation for Functional Dependencies Sets Based on the Substitution Paradigm”. In: Current Topics in Artificial Intelligence. Springer Berlin Heidelberg, 2004, p. 136–146. ISBN: 9783540259459. DOI: 10.1007/978-3-540-25945-9_14. URL: http://dx.doi.org/10.1007/978-3-540-25945-9_14.

[18] A. Mora, I. P. de Guzmán, M. Enciso, et al. “Ideal non-deterministic operators as a formal framework to reduce the key finding problem”. In: International Journal of Computer Mathematics 88.9 (Jun. 2011), p. 1860–1868. ISSN: 1029-0265. DOI: 10.1080/00207160.2010.484488. URL: http://dx.doi.org/10.1080/00207160.2010.484488.

[19] E. Rodríguez-Lorenzo, K. Adaricheva, P. Cordero, et al. “Formation of the D-basis from implicational systems using Simplification logic”. In: International Journal of General Systems 46.5 (Jul. 2017), p. 547–568. ISSN: 1563-5104. DOI: 10.1080/03081079.2017.1349632. URL: http://dx.doi.org/10.1080/03081079.2017.1349632.

[20] E. Rodríguez-Lorenzo, K. Bertet, P. Cordero, et al. “Direct-optimal basis computation by means of the fusion of simplification rules”. In: Discrete Applied Mathematics 249 (Nov. 2018), p. 106–119. ISSN: 0166-218X. DOI: 10.1016/j.dam.2017.12.031. URL: http://dx.doi.org/10.1016/j.dam.2017.12.031.

[21] J. M. Rodríguez‐Jiménez, P. Cordero, M. Enciso, et al. “Data mining algorithms to compute mixed concepts with negative attributes: an application to breast cancer data analysis”. In: Mathematical Methods in the Applied Sciences 39.16 (Jan. 2016), p. 4829–4845. ISSN: 1099-1476. DOI: 10.1002/mma.3814. URL: http://dx.doi.org/10.1002/mma.3814.