Most of the approaches for dealing with uncertainty in the Semantic Web rely on the principle that this uncertainty is already asserted. In this paper, we propose a new approach to learn and reason about uncertainty in the Semantic Web. Using instance data, we learn the uncertainty of an OWL ontology, and use that information to perform probabilistic reasoning on it. For this purpose, we use Markov logic, a new representation formalism that combines logic with probabilistic graphical models.

The main idea behind the Semantic Web is the repre s ntation of knowledge in an explicit and formal way. This is do ne using ontology representation languages as OWL, which is based on Description Logics and other logic formalisms. One of the main objectives with this kind of knowledge representation is that it can then be used for reas oning. But the way reasoning is done in the Semantic Web technology is very strict, defining only a right and wrong view of the world. The real world is uncertai n nd humans have learned how to deal with this crucial aspect. In this paper , we present an approach to reasoning with uncertainty information in the Seman tic Web. We have applied Markov Logic, which is able to reason with uncertai n y information, to several Semantic Web ontologies, showing that it can be use d in several applications. We also describe the main challenges for reasoning with uncertainty in the Semantic Web.

Uncertainty is a characteristic of any relevant an d complex knowledge source. In this thesis, we explored the u se of Markov Logic, a novel representation formalism that combines first-order logic with probabilistic graphical models, to learn and reason about uncerta inty in the Semantic Web. We explored several ways to acquire this uncertaint y automatically from several sources, and applied them in relevant tasks to the Semantic Web

Epilepsy is one of the most frequent neurological disorders. The main method used in epilepsy diagnosis is electroencephalogram (EEG) signal analysis. However this method requires a time-consuming analysis when made manually by an expert due to the length of EEG recordings. This paper proposes an automatic classification system for epilepsy based on neural networks and EEG signals. The neural networks use 14 features (extracted from EEG) in order to classify the brain state into one of four possible epileptic behaviors: inter-ictal, pre-ictal, ictal and pos-ictal. Experiments were made in a (i) single patient (ii) different patients and (ii) multiple patients, using two datasets. The classification accuracies of 6 types of neural networks architectures are compared. We concluded that with the 14 features and using the data of a single patient results in a classification accuracy of 99%, while using a network trained for multiple patients an accuracy of 98% is achieved.

One of the problems that software development companies face today is the increasing dimension of software develop- ment projects, which have grown in size and complexity, as well as in the number of technologies that are involved. The knowledge generated during the software development pro- cess can be a valuable asset for a software company, in order to take advantage of this knowledge the company must ac- quire, store and manage it for reuse. This paper describes the Semantic Reuse System (SRS), a system for manage- ment and reuse of software development knowledge based on the Semantic Web technologies.