Named Entity Recognition (NER) poses new challenges in real-world documents in which there are entities with different roles according to their purpose or meaning. Retrieving all the possible entities in scenarios in which only a subset of them based on their role is needed, produces noise on the overall precision. This work proposes a NER model that relies on role classification models that support recognizing entities with a specific role. The proposed model has been implemented in two use cases using Spanish drug Summary of Product Characteristics: identification of therapeutic indications and identification of adverse reactions. The results show how precision is increased using a NER model that is oriented towards a specific role and discards entities out of scope.

The Semantic Web of the future will be characterized by using a very large number of ontologies embedded in ontology networks. It is important to provide strong methodological support for collaborative and context-sensitive development of networks of ontologies. This methodological support includes the identification and definition of which activities should be carried out when ontology networks are collaboratively built. In this paper we present the consensus reaching process followed within the NeOn consortium for the identification and definition of the activities involved in the ontology network development process. The consensus reaching process here presented produces as a result the NeOn Glossary of Activities. This work was conceived due to the lack of standardization in the Ontology Engineering terminology, which clearly contrasts with the Software Engineering field. Our future aim is to standardize the NeOn Glossary of Activities.

Acquiring knowledge from the Web to build domain ontologies has become a common practice in the Ontological Engineering field. The vast amount of freely available information allows collecting enough information about any domain. However, the Web usually suffers a lack of structure, untrustworthiness and ambiguity of the content. These drawbacks hamper the application of unsupervised methods of building ontologies demanded by the increasingly popular applications of the Semantic Web. We believe that the combination of several processing mechanisms and complementary information sources may potentially solve the problem. The analysis of different sources of evidence allows determining with greater reliability the validity of the detected knowledge. In this paper, we present GALeOn (General Architecture for Learning Ontologies) that combines sources and processing resources to provide complementary and redundant evidence for making better estimations about the relevance of the extracted knowledge and their relationships. Our goal in this paper is to show how combining several information sources and extraction mechanisms is possible to build a taxonomy of concepts with a higher accuracy than if only one of them is applied. The experimental results show how this combination notably increases the precision of the obtained results with minimum user intervention.

We describe the integration of some multilingual language resources in ontological descriptions, with the purpose of providing ontologies, which are normally using concept labels in just one (natural) language, with multilingual facility in their design and use in the context of Semantic Web applications, supporting both the semantic annotation of textual documents with multilingual ontology labels and ontology extraction from multilingual text sources.