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The access to medical information (journals, blogs, web-pages, dictionaries, and texts) has been increased due to availability of many digital media. In particular, finding an appropriate structure that represents the information contained in texts is not a trivial task. One of the structures for modeling the knowledge are ontologies. An ontology refers to a conceptualization of a specific domain of knowledge. Ontologies are especially useful because they support the exchange and sharing of information as well as reasoning tasks. The usage of ontologies in medicine is mainly focussed in the representation and organization of medical terminologies. Ontology learning techniques have emerged as a set of techniques to get ontologies from unstructured information. This paper describes a new ontology learning approach that consists of a method for the acquisition of concepts and its corresponding taxonomic relations, where also axioms disjointWith and equivalentClass are learned from text without human intervention. The source of knowledge involves files about medical domain. Our approach is divided into two stages, the first part corresponds to discover hierarchical relations and the second part to the axiom extraction. Our automatic ontology learning approach shows better results compared against previous work, giving rise to more expressive ontologies. [ABSTRACT FROM AUTHOR]

• We propose an end-to-end approach for BPMN process modeling from event logs. • We propose a filtering algorithm for removing outliers in the event log. • We present an algorithm for the identification of join gateways in the BPMN models. • We implement a tool called P-Miner that realizes our proposed approach. Information systems daily register a large amount of data in event logs. All these data can be used for the organizations to automatically discover their process models. However, the automatic construction of simple process models with consistently high and balanced fitness and precision remains a challenging task, which has attracted the attention in the scientific and organizational communities to develop suitable methods for creating high-quality business process models. In this paper, we present an approach to discover the model of a business process based on the Business Process Model and Notation (BPMN) standard from its behavior observed in event logs. Particularly, we propose: (1) a method to detect outlier behavior in a given event log; (2) a set of heuristic rules to discover join gateways associated with the closing of each of the split gateways in conformance with the rules of BPMN models; and (3) P-Miner, a tool that realizes our proposed approach to automatically discover and visualize process models. We use a state of the art process discovery technique as a basis for discovering XOR/AND gateways in the models. A set of experiments was carried out on real and artificial event logs for evaluating our proposal, considering the fitness and precision metrics to determine the quality of the built models. The amount of gateways in the resulting BPMN model and the time required to build the process models were estimated. The results reveal that the process models derived by our proposed approach exhibited competitive and more balanced results in fitness and precision than those derived with other discovery techniques. [ABSTRACT FROM AUTHOR]