LAUSR

From the perspective of philosophy, ontology relations denote ultimate semantic relations of related knowledge concepts. Beyond doubt, it is still a very difficult problem on how to automatically depict and construct ontology relations because of its high abstractness. Some latest research attempted to realize ontology relation learning by learning abstract hierarchies or similarities among knowledge concepts. Inspired by the requirements of associative semantic cognition like in the human brain, a constructivist ontology relation learning (CORL) method is put forward in this study by borrowing the idea of the constructivist learning theory. Wherein, two following points are supposed: 1) each symbol knowledge is looked as a token of representing certain abstract pattern and 2) each pattern denotes a type of relation structures on other patterns, or a directly observed event data, such as physical sensing data, natural image, sound data, text word etc. So, ontology relation could be considered as the associative support degrees from other knowledge concepts to the target concept, which reflects how one knowledge ontology can be demarcated by other knowledge concepts. Then, the knowledge network can be employed to represent an entire domain knowledge system. Meanwhile, an associative random walk mechanism (ARWM) on knowledge network can be considered to explain the semantic generative process of every document. Thus, CORL can be realized by integrating ARWM into an extended latent Dirichlet allocation (LDA) model. Some theoretical and experimental analysis are done. The corresponding results demonstrate that CORL can obtain effective associative semantic relations among concept words, and gain some novel characteristics in better representing knowledge ontology than existing methods.