On Logical Characterisation of Human Concept Learning based on Terminological Systems
DOI:
https://doi.org/10.12775/LLP.2017.020Keywords
concept, human concept learning, concept construction, terminological knowledge, terminological systems, logic and cognitionAbstract
The central focus of this article is the epistemological assumption that knowledge could be generated based on human beings’ experiences and over their conceptions of the world. Logical characterisation of human inductive learning over their produced conceptions within terminological systems and providing a logical background for theorising over the Human Concept Learning Problem (HCLP) in terminological systems are the main contributions of this research. In order to make a linkage between ‘Logic’ and ‘Cognition’, Description Logics (DLs) will be employed to provide a logical description and analysis of actual human inductive reasoning (and learning). This research connects with the topics ‘logic & learning’, ‘cognitive modelling’, and ‘terminological knowledge representation’.
References
Baader, F., D. McGuinness, D. Nardi, and P. Patel-Schneider (eds.), The Description Logic Handbook: Theory, Implementation and Applications, Cambridge University Press, 2010. DOI: 10.1017/CBO9780511711787
Badie, F., “Logical characterisation of concept transformations from human into machine relying on predicate logic”, in Proceedings of the Ninth International Conference on Advances in Computer-Human Interactions, 2016.
Bartlett, F.C., A Study in Experimental and Social Psychology, Cambridge University Press, 1932.
Du, J., and C.X. Ling, “Active teaching for inductive learners”, pages 851–861 in Proceedings of the 2011 SIAM International Conference on Data Mining, SIAM/Omnipress, 2011. DOI: 10.1137/1.9781611972818.73
Fisher, D., and N. Frey, Background Knowledge: The Missing Piece of the Comprehension Puzzle, NH: Heinemann, Portsmouth, 2009.
Glasersfeld, E., “Cognition, construction of knowledge, and teaching”, Synthese 80, 1 (1989): 121–140. DOI: 10.1007/BF00869951
Götzsche, H., Deviational Syntactic Structures, Bloomsbury Academic, London/New Delhi/New York/Sydney, 2013.
Hawthorne, J., “Inductive logic”, The Stanford Encyclopedia of Philosophy, Winter 2016 Edition. https://plato.stanford.edu/archives/win2016/entries/logic-inductive/
Higman, G., “Ordering by divisibility in abstract algebras”, pages 326–336 in Proceedings of the London Mathematical Society s3–2, 1 (1952): 326–336. DOI: 10.1112/plms/s3-2.1.326
Husén, T., and T.N. Postlethwaite, Constructivism in Education, Oxford, New York: Pergamon Press, 1989.
Kruskal, J.B., “The theory of well-quasi-ordering: A frequently discovered concept”, Journal of Combinatorial Theory, Series A 13, 3 (1972): 297–305. DOI: 10.1016/0097-3165(72)90063-5
Lavrač, N., and S. Džeroski, Inductive Logic Programming: Techniques and Applications, Routledge, New York, NY, 10001, 1993.
Lavrač, N., and S. Džeroski, Inductive Logic Programming: Techniques and Applications, Ellis Horwood, New York, USA, 1994.
Lehmann, J., “Dl-learner: Learning concepts in description logics”, Journal of Machine Learning Research (JMLR) (2009): 2639–2642.
Lehmann, J., S. Auer, L. Bühmann, and S. Tramp, “Class expression learning for ontology engineering”, Web Semantics: Science, Services and Agents on the World Wide Web 9, 1 (2011): 71–81. DOI: 10.1016/j.websem.2011.01.001
Lehmann, J., N. Fanizzi, L. Bühmann, and C. d’Amato, “Concept learning”, pages 71–91 in J. Lehmann and J. Voelker (eds.), Perspectives on Ontology Learning, AKA/IOS Press, 2014.
Lehmann, J., and J. Voelker, “An introduction to ontology learning”, pages ix–xvi in J. Lehmann and J. Voelker (eds.), Perspectives on Ontology Learning, AKA/IOS Press, 2014.
Margolis, E., and S. Laurence, “The ontology of concepts – abstract objects or mental representations?”, Noûs 41, 4 (2007): 561–593. DOI: 10.1111/j.1468-0068.2007.00663.x
Margolis, E., and S. Laurence, “Concepts and theoretical unification”, Behavioral and Brain Sciences 33, 2–3 (2010): 219–220. DOI: 10.1017/S0140525X10000427
Marzano, R.J., Building Background Knowledge for Academic Achievement: Research on What Works in Schools, VA: ASCD, Alexandria, 2004.
Matthews, P.H., The Concise Oxford Dictionary of Linguistics, Oxford University Press, 2014.
McCracken, D. D., “An inductive approach to teaching object-oriented design”, pages 184–188 in J. Prey and R.E. Noonan (eds.), The Proceedings of the Thirtieth SIGCSE Technical Symposium on Computer Science Education – SIGCSE ’99, ACM, 1999. DOI: 10.1145/299649.299748
Mitchell, T., Machine Learning, McGraw Hill, 1997.
Peacocke, C., A Study of Concepts, MIT Press, 1992.
Piaget, J., Six Psychological Studies, Random House, New York, USA, 1967.
Sawyer, K., The Cambridge Handbook of The Learning Sciences, Cambridge Handbooks in Psychology, 2014. DOI: 10.1017/CBO9781139519526
Sell, R., T. Rtmann, and S. Seiler, “Inductive teaching and learning in engineering pedagogy on the example of remote labs”, iJEP (2014): 12–15.
Sjøberg, S., “Constructivism and learning”, pages 485–490 in P. Peterson and B. Baker, and E. McGaw (eds.), International Encyclopaedia of Education, 3rd edition, Elsevier, 2007. DOI: 10.1016/B978-0-08-044894-7.00467-X
Staab, S., and R. Studer (eds.), International Handbooks on Information System, Springer, 2004.
Stich, S., “What is a theory of mental representation?”, Mind 101, 402 (1992): 243–261. DOI: 10.1093/mind/101.402.243
Taba, H., A Teacher’s Handbook to Elementary Social Studies: An Inductive Approach, Addison-Wesley Pub. Co., 1971.
Zalta, E., “Fregean senses, modes of presentation, and concepts”, Philosophical Perspectives (Noûs) 35, s15 (2001): 335–359. DOI: 10.1111/0029-4624.35.s15.15
Downloads
Published
How to Cite
Issue
Section
Stats
Number of views and downloads: 777
Number of citations: 0
