Predictions obtained by, eg, artificial neural networks have a high accuracy but humans
often perceive the models as black boxes. Insights about the decision making are mostly …

Word sense disambiguation (WSD) is the ability to identify the meaning of words in context
in a computational manner. WSD is considered an AI-complete problem, that is, a task …

Ontologies are at the core of the semantic web. As knowledge bases, they are very useful
resources for many artificial intelligence applications. Ontology learning, as a research area …

This volume brings together ontology learning, knowledge acquisition and other related
topics. It presents current research in ontology learning, addressing three perspectives. The …

Ontologies are the backbone of the Semantic Web, a semantic-aware version of the World
Wide Web. The availability of large-scale high quality domain ontologies depends on …

Word sense disambiguation (WSD) is traditionally considered an AI-hard problem. A break-
through in this field would have a significant impact on many relevant Web-based …

Word sense disambiguation (WSD), the task of identifying the intended meanings (senses)
of words in context, has been a long-standing research objective for natural language …

In 2004 we published in this journal an article describing OntoLearn, one of the first systems
to automatically induce a taxonomy from documents and Web sites. Since then, OntoLearn …

The rapid advancement in the theoretical understanding of statistical and machine learning
methods for semisupervised learning has made it difficult for nonspecialists to keep up to …

Ontology evaluation is a critical task, even more so when the ontology is the output of an
automatic system, rather than the result of a conceptualization effort produced by a team of …