Tommaso Di Noia

Learn More
Motivated by the matchmaking problem in electronic marketplaces, we study abduction in Description Logics. We devise suitable definitions of the problem, and show how they can model commonsense reasoning usually employed in analyzing classified announcements having a standardized terminology. We then describe a system partially implementing these ideas, and(More)
More and more resources are becoming available on the Web, and there is a growing need for infrastructures that, based on advertised descriptions, are able to semantically match demands with supplies.We formalize general properties a matchmaker should have, then we present a matchmaking facilitator, compliant with desired properties.The system embeds a(More)
The World Wide Web is moving from a Web of hyper-linked Documents to a Web of linked Data. Thanks to the Semantic Web spread and to the more recent Linked Open Data (LOD) initiative, a vast amount of RDF data have been published in freely accessible datasets. These datasets are connected with each other to form the so called Linked Open Data cloud. As of(More)
In this paper we present a Description Logic approach to extended matchmaking between Demands and Supplies in an Electronic Marketplace, which allows the semantic-based treatment of negotiable and strict requirements in the description.To this aim we exploit two novel non-standard Description Logic inference services, Concept Contraction -which extends(More)
Matchmaking arises when supply and demand meet in an electronic marketplace, or when agents search for a web service to perform some task, or even when recruiting agencies match curricula and job profiles. In such open environments, the objective of a matchmaking process is to discover best available offers to a given request. We address the problem of(More)
The advent of the Linked Open Data (LOD) initiative gave birth to a variety of open knowledge bases freely accessible on the Web. They provide a valuable source of information that can improve conventional recommender systems, if properly exploited. In this paper we present SPrank, a novel hybrid recommendation algorithm able to compute top-N item(More)
We present algorithms based on truth-prefixed tableaux to solve both Concept Abduction and Contraction in ALN DL. We also analyze the computational complexity of the problems, showing that the upper bound of our approach meets the complexity lower bound. The work is motivated by the need to offer a uniform approach to reasoning services useful in(More)
Feature modeling is a technique for capturing commonality and variability. Feature models symbolize a representation of the possible application configuration space, and can be customized based on specific domain requirements and stakeholder goals. Most feature model configuration processes neglect the need to have a holistic approach towards the(More)
The availability of a huge amount of interconnected data in the so called Web of Data (WoD) paves the way to a new generation of applications able to exploit the information encoded in it. In this paper we present a model-based recommender system leveraging the datasets publicly available in the Linked Open Data (LOD) cloud as DBpedia and LinkedMDB. The(More)