A Cognitive Knowledge Base for Learning Disabilities Using Concept Analysis
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in recent days, the amount of unstructured data has been widely increased, and it is a challenging task to derive meaningful insights and knowledge from the data. a cognitive knowledge base (ckb) is constructed to derive the hidden semantic knowledge from the unstructured text data. a knowledge base (kb) is a collective structure of knowledge developed by ontology or rule-based techniques. a ckb is an object-attribute-relational (oar) model constituted by a weighted hierarchical concept network. ckb relies on the denotational mathematical structure of formal concepts like concepts, objects, attributes, and knowledge. this paper deals with the construction of a ckb using formal concept analysis (fca). construction of ckb using fca consists of four steps, namely (i) topic modeling guided concept extraction, (ii) determination of objects and attributes, (iii) determination of relevant objects, relevant attributes, hyper-attributes, and (iv) construction of relational hierarchy. the proposed framework derives meaningful insights by extracting main concepts and representing the weighted hierarchical concept network as a relational hierarchy. this paper demonstrates a ckb developed for dyslexic children.
KeywordsCognitive knowledge base Formal concept analysis Concept extraction Relational hierarchy Semantic knowledge
the authors gratefully acknowledge dst, new delhi, for providing financial support to carry out this research work under dst-inspire fellowship scheme. one of the authors, ms. m. srivani, is thankful for dst for the award of dst-inspire fellowship.
- 1.Wang, Y.: Formal cognitive models of data, information, knowledge, and intelligence. WSEAS Trans. Comput. 14(3), 770–781 (2015)
- 2.Wang, Y., Zatarain, O.A., Valipour, M.: Building cognitive knowledge bases sharable by humans and cognitive robots. In: IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 3189–3194. Canada (2017).
- 3.Zhao, M., Zhang, S., Li, W., Chen, G.: Matching biomedical ontologies based on formal concept analysis. J. Biomed. Semant. 9(11), 1–27 (2018).
- 4.Al-Hunaiyyan, A., Bimba, A. T., Idris, N., Al-Sharhan, S.: A cognitive knowledge-based framework for social and metacognitive support in mobile learning. Interdiscip. J. Inf., Knowl. Manag. 12, 75–98 (2017).
- 5.Bimba, A.T., Idris, N., Mahmud, R.B., Al-Hunaiyyan, A.: A cognitive knowledge-based framework for adaptive feedback. In: International Conference on Computational Intelligence in Information System, pp. 245–255. Springer, Cham (2016).
- 6.Wang, Y.: On a novel cognitive knowledge base (CKB) for cognitive robots and machine learning. Int. J. Softw. Sci. Comput. Intell. (IJSSCI) 6(2), 41–62 (2014).
- 7.Wang, Y., Zatarain, O.A.: A novel machine learning algorithm for cognitive concept elicitation by cognitive robots. Int. J. Cogn. Inform. Nat. Intell. (IJCINI) 11(3), 31–46 (2017)
- 8.Wang, Y.: Concept algebra: a denotational mathematics for formal knowledge representation and cognitive robot learning. J. Adv. Math. Appl. 4(1), 61–86 (2015).
- 9.Valipour, M., Wang, Y.: Building semantic hierarchies of formal concepts by deep cognitive machine learning. In: IEEE 16th International Conference on Cognitive Informatics Cognitive Computing (ICCI* CC), pp. 51–58 (2017).
- 10.Wang, Y., Valipour, M., Zatarain, O.A.: Quantitative semantic analysis and comprehension by cognitive machine learning. Int. J. Cogn. Inform. Nat. Intell. (IJCINI) 10(3), 13–28 (2016).
- 11.Wang, Y.: On cognitive foundations and mathematical theories of knowledge science. In: Artificial Intelligence: Concepts, Methodologies, Tools, and Applications, pp. 889–914. IGI Global (2017).
- 12.ElBedwehy, M.N., Ghoneim, M.E., Hassanien, A.E., Azar, A.T.: A computational knowledge representation model for cognitive computers. Neural Comput. Appl. 25(7–8), 1517–1534 (2014).
- 13.Tian, Y., Wang, Y., Gavrilova, M.L., Ruhe, G.: A formal knowledge representation system (FKRS) for the intelligent knowledge base of a cognitive learning engine. Int. J. Softw. Sci. Comput. Intell. (IJSSCI) 3(4), 1–17 (2011).
- 14.Lieto, A., Lebiere, C., Oltramari, A.: The knowledge level in cognitive architectures: current limitations and possible developments. Cogn. Syst. Res. 48, 39–55 (2018).
- 15.Andrews, M.: A Gibbs Sampler for a Hierarchical Dirichlet Process Mixture Model. PsyArXiv, pp. 1–9 (2019).