The concept of relevance is a hot topic in the information retrieval process. In recent years the extreme growth of digital documents brought to light the need for novel approaches and more efficient techniques to improve the accuracy of IR systems to take into account real users' information needs. In this article we propose a novel metric to measure the semantic relatedness between words. Our approach is based on ontologies represented using a general knowledge base for dynamically building a semantic network. This network is based on linguistic properties and it is combined with our metric to create a measure of semantic relatedness. In this way we obtain an efficient strategy to rank digital documents from the Internet according to the user's interest domain. The proposed methods, metrics, and techniques are implemented in a system for information retrieval on the Web. Experiments are performed on a test set built using a directory service having information about analyzed documents. The obtained results compared to other similar systems show an effective improvement.

In the last years, the large availability of data and schema models formalized through different languages has demanded effective and efficient methodologies to reuse such models. One of the most challenging problem consists in integrating different models in a global conceptualization of a specific knowledge or application domain. This is a hard task to accomplish due to ambiguities, inconsistencies and heterogeneities, at different levels, that could stand in the way. The ability to effectively and efficiently perform knowledge reuse is a crucial factor in knowledge management systems, and it also represents a potential solution to the problem of standardization of information and a viaticum towards the realization of the Semantic web. In this paper, an approach to ontology reuse based on heterogeneous matching techniques will be presented, in particular, we will show how the process of ontology construction will be improved and simplified, by automatizing the selection and the reuse of existing data models. The proposed approach will be applied to the food domain, specifically to the food production.