In DynaLearn, semantics of the QR models ingredients is made explicit by representing them as terms in ontologies. That easies the task of exploring the knowledge contained in the models, enabling rich comparisons among them. The facts that the user explicitly represents in the model constitutes the asserted ontology. Nevertheless, logical rules can be applied to these facts in order to extract other knowledge (inferred facts) that was not made explicit by the modeller. Taxonomical reasoning techniques can make emerge these inferred facts. In the Semantic Technologies module in DynaLearn, the exploration of taxonomic structures and the application of taxonomical reasoning techniques play a major role. This document describes the task of integrating taxonomic reasoning in DynaLearn in order to enrich the results presented to users by discovering additional semantic information that is not explicit in the QR models initially. This enables: (1) a better identification of similar terms between models, which directly benefits feedback and collaborative filtering, (2) detection of inconsistencies between models, which enriches the information given to the user during semantic feedback, and (3) classification of instances during the grounding process. All these aspects are analysed in the document. As an addition, and to complete the cycle of WP4 deliverables, we annex in this document a description of the test plan that we created and applied regularly to the different components of the ST module.
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