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.
ACM Reference Format:Rinaldi, A. M. 2009. An ontology-driven approach for semantic information retrieval on the Web.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.