Recommender systems fight information overload by selecting automatically items that
match the personal preferences of each user. The so-called content-based recommenders …

The present paper introduces a method to promote existing controlled vocabularies to the
Linked Data initiative. A common data model and an enclosed conversion method for …

In this paper, we explore the relationship between the human-encoded semantics of
ontologies and their application to natural language processing (NLP) tasks, such as word …

In this paper, we present SGDB, a graph database with a storage model optimized for
computation of Spreading Activation (SA) queries. The primary goal of the system is to …

Abstract On the Semantic Desktop users maintain their model of the world in a formal
personal information model ontology. Concepts from this ontology are used to annotate …

Two strong tendencies in social networks today are their growing multidimensionality and
the key role of social objects around which they crystallize. Growing heterogeneity and …

Spreading activation (also known as spread of activation) is a method for searching
associative networks, neural networks or semantic networks. The method is based on the …

The paper presents a wordnet expansion algorithm, which is based on lexicosemantic
relations extracted from large text corpora. We do not assume that the extracted relation …

Recommender systems fight information overload by selecting automatically items that
match the personal preferences of each user. Content-based recommenders suggest items …

Abstract The emerging Web of Data as part of the Semantic Web initiative and the sheer
mass of information now available make it possible the deployment of new services and …