Abstract
Ontologies are computational artifacts to represent knowledge through classes and relations between them. Those knowledge bases require a lot human effort to be constructed due to the need of domain experts and knowledge engineers. Ontology Learning aims to automatically build ontologies from data that can be from multimedia, web pages, databases, unstructured text, etc. In this work, we propose a methodology to automatically build an ontology to represent concepts map of subjects to be used in academic context. The main contribution of this methodology is that does not require handcrafted features by using Deep Learning techniques to identify taxonomic and semantic relations between concepts of some specific domain. Also, due the implementation of transfer learning is not needed of specific domain dataset, the relation classification model is trained with Wikipedia and WordNet by distant supervision technique and the knowledge is transferred to a specific domain by word embedding techniques. The results of this approach are promising considering the lack of human intervention and feature engineering.
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References
Atapattu, T., Falkner, K., Falkner, N.: Automated extraction of semantic concepts from semi-structured data: supporting computer-based education through the analysis of lecture notes. In: International Conference on Database and Expert Systems Applications, pp. 161–175 (2012)
Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent Dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)
Caraballo, S.A.: Automatic construction of a hypernym-labeled noun hierarchy from text. In: Proceedings of the 37th Annual Meeting of the Association for Computational Linguistics on Computational Linguistics, ACL’99, pp. 120–126. Association for Computational Linguistics, Stroudsburg, PA, USA (1999)
Chakraborty, S., Roy, D., Basu, A.: TMRF e-book development of knowledge based intelligent tutoring system. Sajja & Akerkar 1, 74–100 (2010)
Colace, F., De Santo, M., Greco, L., Amato, F., Moscato, V., Picariello, A.: Terminological ontology learning and population using latent Dirichlet allocation. J. Vis. Lang. Comput. 25(6), 818–826 (2014)
Conde, A., Larrañaga, M., Calvo, I., Elorriaga, J., Arruarte, A.: Automatic generation of the domain module from electronic textbooks: method and validation. IEEE Trans. Knowl. Data Eng. 26(1), 69–82 (2014)
dos Santos, C.N., Xiang, B., Zhou, B.: Classifying relations by ranking with convolutional neural networks. Acl-2015 3, 626–634 (2015)
Fan, M., Zhou, Q., Abel, A., Zheng, T.F., Grishman, R.: Probabilistic belief embedding for large-scale knowledge population. Cogn. Comput. 8(6), 1087–1102 (2016)
Gantayat, N., Iyer, S.: Automated building of domain ontologies from lecture notes in courseware. In: Proceedings—IEEE International Conference on Technology for Education, T4E 2011, pp. 89–95 (2011)
Gligora, M., Jakupovic, A.: A prevalence trend of characteristics of intelligent and adaptive hypermedia e-learning systems (2015)
Hendrickx, I., Kim, S.N., Kozareva, Z., Nakov, P., Séaghdha, D., Padó, S., Pennacchiotti, M., Romano, L., Szpakowicz, S.: SemEval-2010 Task 8: multi-way classification of semantic relations between pairs of nominals. In: Computational Linguistics, Number June 2009 in DEW’09, pp. 94–99. Association for Computational Linguistics, Stroudsburg, PA, USA (2010)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)
Isir, R.M., Canada, G.H.: Using ontological engineering to overcome common AI-ED problems. J. Artif. Intell. Educ. 11, 107–121 (2000)
Kingma, D.P., Adam, J.B.: Method for stochastic optimization. CoRR (2014). abs/1412.6
Komninos, A.: Dependency based embeddings for sentence classification tasks. In: Naacl 2016, pp. 1490–1500 (2016)
Landauer, T.K., Dumais, S.T.: A solution to Plato’s problem: the latent semantic analysis theory of acquisition, induction, and representation of knowledge. Psychol. Rev. 104(2), 211–240 (1997)
Lecun, Y., Bengio, Y., Hinton, G.: Deep learning (2015)
Lin, Y., Shen, S., Liu, Z., Luan, H., Sun, M.: Neural relation extraction with selective attention over instances. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, vol. 1: Long Papers, pp. 2124–2133 (2016)
Mikolov, T., Corrado, G., Chen, K., Dean, J.: Efficient estimation of word representations in vector space. In: Proceedings of the International Conference on Learning Representations (ICLR 2013), pp. 1–12 (2013)
Navarro-Almanza, R., Licea, G., Juárez-Ramírez, R., Mendoza, O.: Semantic Capture analysis in word embedding vectors using convolutional neural network. In: Rocha, A., Correia, A.M., Adeli, H., Reis, L.P., Costanzo, S. (eds.) Recent Advances in Information Systems and Technologies, vol. 1, pp. 106–114. Springer, Cham (2017)
Pan, W., Zhong, F., Yang, Q.: Transfer learning for text mining. In: Aggarwal, C.C., Zhai, C. (eds.) Mining Text Data, pp. 223–257. Springer, Boston, MA, USA (2012)
Qin, P., Weiran, X., Guo, J.: An empirical convolutional neural network approach for semantic relation classification. Neurocomputing 190, 1–9 (2016)
Ramírez-Noriega, A., Juárez-Ramírez, R., Jiménez, S., Inzunza, S., Navarro, R., López-Martínez, J.: An ontology of the object orientation for intelligent tutoring systems. In: 2017 5th International Conference in Software Engineering Research and Innovation, pp. 163–170 (2017)
Rios-Alvarado, A.B., Lopez-Arevalo, I., Sosa-Sosa, V.J.: Learning concept hierarchies from textual resources for ontologies construction. Expert Syst. Appl. 40(15), 5907–5915 (2013)
Xiong, S., Ji, D.: Exploiting flexible-constrained K-means clustering with word embedding for aspect-phrase grouping. Inf. Sci. 367–368, 689–699 (2016)
Zeng, D., Liu, K., Chen, Y., Zhao, J.: Distant supervision for relation extraction via piecewise convolutional neural networks. In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pp. 1753–1762 (2015)
Zhao, H., Lu, Z., Poupart, P.: Self-adaptive hierarchical sentence model. In: IJCAI International Joint Conference on Artificial Intelligence, January 2015, pp. 4069–4076 (2015)
Zhou, P., Shi, W., Tian, J., Qi, Z., Li, B., Hao, H., Xu, B.: Attention-based bidirectional long short-term memory networks for relation classification. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, vol. 2, Short Papers, pp. 207–212 (2016)
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This research was supported/partially supported by MyDCI (Maestría y Doctorado en Ciencias e Ingeniería).
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Navarro-Almanza, R., Juárez-Ramírez, R., Licea, G., Castro, J.R. (2020). Automated Ontology Extraction from Unstructured Texts using Deep Learning. In: Castillo, O., Melin, P., Kacprzyk, J. (eds) Intuitionistic and Type-2 Fuzzy Logic Enhancements in Neural and Optimization Algorithms: Theory and Applications. Studies in Computational Intelligence, vol 862. Springer, Cham. https://doi.org/10.1007/978-3-030-35445-9_50
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