A Proposal for Domain Ontological Learning Yuridiana Alemán, María J. Somodevilla, and Darnes Vilariño Benemérita Universidad Autónoma de Puebla, Faculty of Computer Science, Puebla, Mexico {yuridiana.aleman, mariajsomodevilla, dvilarinoayala}@gmail.com Abstract. In this paper, the population ontology problem is addressed and a semiautomatic methodology for ontology learning is proposed. In this work a study of advances in this research area is presented, which is divided according with the aim of the ontology and the class extraction, creation, or population. Based on this study, an initial proposal for ontology semiautomatic population is introduced. This initial proposal consists of four general steps: class extrac- tion, creation, population and evaluation, where the population step is the main objective of this research. As future work, this methodology is intended to be applied to pedagogic domain, specially to classroom learning tools. Keywords: Ontology learning, Semiautomatic population, Pedagogy domain, Ontology creation, NLP. 1 Introduction In recent years, the available information has increased exponentially; thus, infor- mation science researchers propose strategies to develop processes and generate an- swers according to the user requirements in Information Processing Systems (IPS) [1]. The classic techniques of information retrieval cannot resolve problems like heterogeneity and ambiguity of web information. It is necessary to develop new semantics approaches to improve actual research, for example ontologies. Ontologies can be used for purposes such as structure knowledge in taxonomies, vocabulary manage, natural language processing applications, searches, recommenda- tion systems, and e-learning among others [2, 3, 4]. Ontologies can model interaction systems between users and their environment, since to its property to manage complex knowledge in reusable formal representations. Ontology is a formal, explicit specification of a shared conceptualization. Their classes, relationships, constraints and axioms define a common vocabulary to share knowledge [5]. The ontology construction process can be manual or automatic, if it is automatic, the process is called “Ontology learning” and include: relevant terminology acquisition, synonyms extraction, concepts formation, hierarchical organization of elements, relationships learning, properties, attributes, with their rank and domain, hierarchical organization of relationships, instantiation of schema axioms, and arbitrary axioms definition [6]. In the manual construction process, a domain expert is necessary for model formalization; but generally, it is difficult to transmit their knowledge and the proper way to formalize it [7]. Ontology population 63 ISSN 1870-4069 Research in Computing Science 133 (2017) pp. 63–70; rec. 2017-03-13; acc. 2017-05-25