Estevam R. Hruschka

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We consider here the problem of building a never-ending language learner; that is, an intelligent computer agent that runs forever and that each day must (1) extract, or read, information from the web to populate a growing structured knowledge base, and (2) learn to perform this task better than on the previous day. In particular, we propose an approach and(More)
We consider the problem of semi-supervised learning to extract categories (e.g., academic fields, athletes) and relations (e.g., PlaysSport(athlete, sport)) from web pages, starting with a handful of labeled training examples of each category or relation, plus hundreds of millions of unlabeled web documents. Semi-supervised training using only a few labeled(More)
Whereas people learn many different types of knowledge from diverse experiences over many years, most current machine learning systems acquire just a single function or data model from just a single data set. We propose a neverending learning paradigm for machine learning, to better reflect the more ambitious and encompassing type of learning performed by(More)
We report research toward a never-ending language learning system, focusing on a first implementation which learns to classify occurrences of noun phrases according to lexical categories such as “city” and “university.” Our experiments suggest that the accuracy of classifiers produced by semi-supervised learning can be improved by coupling the learning of(More)
Traditional approaches to Relation Extraction from text require manually defining the relations to be extracted. We propose here an approach to automatically discovering relevant relations, given a large text corpus plus an initial ontology defining hundreds of noun categories (e.g., Athlete, Musician, Instrument). Our approach discovers frequently stated(More)
The definition of the fuzzy rule base is one of the most important and difficult tasks when designing fuzzy systems. This paper discusses the results of two different hybrid methods investigated earlier, for the automatic generation of fuzzy rules from numerical data. One of the methods proposes the creation of fuzzy rule bases using genetic algorithms in(More)
An alternative to the traditional single function approximation method is the never-ending learning (NEL) approach i.e., a learning paradigm in which, the learner, in an autonomous way, manages to constantly, incrementally and continuously evolve with time. But, most important than just keep evolving, in this new paradigm acquired knowledge can, in a(More)
This paper proposes a feature weighting method based on X<sup>2 </sup> statistical test, to be used in conjunction with a k-NN classifier. Results of empirical experiments conducted using data from several knowledge domains are presented and discussed. Forty four out of forty five conducted experiments favoured the feature weighted approach and are(More)