The ontologies are a powerful tool for representing the knowledge from a particular domain so it is necessary to know its elements in order to guarantee the safety and satisfaction of the task for which was designed and created. This work presents a general review of the elements and evaluation of the ontology in order to offer practical definitions and some ontology application examples.

Background Medical experts in the domain of Diabetes Mellitus (DM) acquire specific knowledge from diabetic patients through monitoring and interaction. This allows them to know the disease and information about other conditions or comorbidities, treatments, and typical consequences of the Mexican population. This indicates that an expert in a domain knows technical information about the domain and contextual factors that interact with it in the real world, contributing to new knowledge generation. For capturing and managing information about the DM, it is necessary to design and implement techniques and methods that allow: determining the most relevant conceptual dimensions and their correct organization, the integration of existing medical and clinical information from different resources, and the generation of structures that represent the deduction process of the doctor. An Ontology Network is a collection of ontologies of diverse knowledge domains which can be interconnected by meta-relations. This article describes an Ontology Network for representing DM in Mexico, designed by a proposed methodology. The information used for Ontology Network building include the ontological resource reuse and non-ontological resource transformation for ontology design and ontology extending by natural language processing techniques. These are medical information extracted from vocabularies, taxonomies, medical dictionaries, ontologies, among others. Additionally, a set of semantic rules has been defined within the Ontology Network to derive new knowledge. Results An Ontology Network for DM in Mexico has been built from six well-defined domains, resulting in new classes, using ontological and non-ontological resources to offer a semantic structure for assisting in the medical diagnosis process. The network comprises 1367 classes, 20 object properties, 63 data properties, and 4268 individuals from seven different ontologies. Ontology Network evaluation was carried out by verifying the purpose for its design and some quality criteria. Conclusions The composition of the Ontology Network offers a set of well-defined ontological modules facilitating the reuse of one or more of them. The inclusion of international vocabularies as SNOMED CT or ICD-10 reinforces the representation by international standards. It increases the semantic interoperability of the network, providing the opportunity to integrate other ontologies with the same vocabularies. The ontology network design methodology offers a guide for ontology developers about how to use ontological and non-ontological resources in order to exploit the maximum of information and knowledge from a set of domains that share or not information.

No abstract

In this work, a model for textual emotion classification based on Ranking technique is presented. The Ranking technique uses the frequencies of words in order to assign a relevance for each in a tweets (Spanish) after calculating the total relevance of the tweet for each classes. The classes are associated to four emotions: happiness, sadness, anger and fear and the highest relevance indicates to which class the tweet belongs. The training and test corpora are created by manually selected key words as references for a crawling tool, both contain manually tagged tweets extracted from Twitter; the training corpus was validated by K-Fold Cross Validation having a 90% of acceptance. The results are compared with Naïve Bayes and Bigrams Probabilities models using precision, recall and F-measure.