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In recent years, much effort has been put in ontology learning. However, the knowledge acquisition process is typically focused in the taxonomic aspect. The discovery of non-taxonomic relationships is often neglected, even though it is a fundamental point in structuring domain knowledge. This paper presents an automatic and unsupervised methodology that(More)
The information content (IC) of a concept provides an estimation of its degree of generality/concreteness, a dimension which enables a better understanding of concept's semantics. As a result, IC has been successfully applied to the automatic assessment of the semantic similarity between concepts. In the past, IC has been estimated as the probability of(More)
Semantic similarity estimation is an important component of analysing natural language resources like clinical records. Proper understanding of concept semantics allows for improved use and integration of heterogeneous clinical sources as well as higher information retrieval accuracy. Semantic similarity has been the focus of much research, which has led to(More)
Proper understanding of textual data requires the exploitation and integration of unstructured and heterogeneous clinical sources, healthcare records or scientific literature, which are fundamental aspects in clinical and translational research. The determination of semantic similarity between word pairs is an important component of text understanding that(More)
Estimation of the semantic likeness between words is of great importance in many applications dealing with textual data such as natural language processing, knowledge acquisition and information retrieval. Semantic similarity measures exploit knowledge sources as the base to perform the estimations. In recent years, ontologies have grown in interest thanks(More)
PURPOSE The agent-oriented paradigm has emerged as a viable approach for the development of autonomic systems in the healthcare domain. This paper reviews representative works in this area in order to identify the main research lines and study their level of applicability. Moreover, from the analysis of those works and the authors' own experiences, some(More)
Semantic Annotation is required to add machine-readable content to natural language text. A global initiative such as the Semantic Web directly depends on the annotation of massive amounts of textual Web resources. However, considering the amount of those resources, a manual semantic annotation of their contents is neither feasible nor scalable. In this(More)
PURPOSE Information Technologies and Knowledge-based Systems can significantly improve the management of complex distributed health systems, where supporting multidisciplinarity is crucial and communication and synchronization between the different professionals and tasks becomes essential. This work proposes the use of the ontological paradigm to describe(More)
In the context of Statistical Disclosure Control, microaggregation is a privacy preserving method aimed to mask sensitive microdata prior to publication. It iteratively creates clusters of, at least, k elements, and replaces them by their prototype so that they become k-indistinguishable (anonymous). This data transformation produces a loss of information(More)
The Information Content (IC) of a concept quantifies the amount of information it provides when appearing in a context. In the past, IC used to be computed as a function of concept appearance probabilities in corpora, but corpora-dependency and data sparseness hampered results. Recently, some authors tried to overcome previous approaches, estimating IC from(More)