Semantic search in RealFoodTrade

We present RealFoodTrade (RFT), a system that allows farmers and fisher- men to sell their products directly to the end-buyer. RFT mak es use of Linked Data sets, together with a domain ontology designed by expert s, to perform semantic search over products on sale. RFT employs geo-locat ion technology on mobile devices to match demand and supply according to the l ocation. We sketch the semantic search techniques in RFT and illustrat e a prototype tailored to the fishing industry

A Linked Data Recommender System Using a Neighborhood-Based Graph Kernel

Abstract. The ultimate mission of a Recommender System (RS) is to help users discover items they might be interested in. In order to be really useful for the end-user, Content-based (CB) RSs need both to harvest as much information as possible about such items and to effectively han-dle it. The boom of Linked Open Data (LOD) datasets with their huge amount of semantically interrelated data is thus a great opportunity for boosting CB-RSs. In this paper we present a CB-RS that leverages LOD and profits from a neighborhood-based graph kernel. The proposed ker-nel is able to compute semantic item similarities by matching their local neighborhood graphs. Experimental evaluation on the MovieLens dataset shows that the proposed approach outperforms in terms of accuracy and novelty other competitive approaches.