Exploiting Semantic Distance in Linked Open Data for Recommendation

The use of Linked Open Data (LOD) has been explored in recommender systems in different ways, primarily through its graphical representation. The graph structure of LOD is utilized to measure inter-resource relatedness via their semantic distance in the graph. The intuition behind this approach is that the more connected resources are to each other, the more related they are. One drawback of this approach is that it treats all inter-resource connections identically rather than prioritizing links that may be more important in semantic relatedness calculations. Another drawback of current approaches is that they only consider resources that are connected directly or indirectly through an intermediate resource only. In this document, we show that different types of inter-resource links hold different values for relatedness calculations between resources, and we exploit this observation to introduce improved resource semantic relatedness measures that are more accurate than the current state of the art approaches. Moreover, we introduce an approach to propagate current semantic distance approaches that does not only expand the coverage of current approaches, it also increases their accuracy. To validate the effectiveness of our approaches, we conducted several experiments to identify the relatedness between musical artists in DBpedia, and they demonstrated that approaches that prioritize link types resulted in more accurate recommendation results. Also, propagating semantic distances beyond one hub resources does not only result in an improved accuracy, it also shows that propagating semantic distances beyond one hub resources improves the coverage of LOD-based recommender systems

Supporting semantic web search and structured queries on mobile devices

There has been much recent interest in user-friendly interfaces that support queries and searching the Semantic Web, without requiring knowledge of sparql and the internal structure used by DBpedia or other knowledge bases. Although powerful, the existing proposals assume the use of desktop computers featuring rather large displays and pointing devices such as a mouse or trackpad. In this paper we tackle the problem of querying and searching the Semantic Web from mobile devices, by taking full advantage of their small touch-enabled screens. We focus on a user-friendly interface that can be used from smartphones, mini tablets, smart watches and possibly other wearable computers such as Google Glass. Indeed existing approaches become much less usable and effective on mobile since these support and require different modalities of user interaction. Our proposal is based on an adaptation of the recently proposed concept of SBE query system, developing a novel mobile interface that allows both browsing and querying the Semantic Web without using sparql nor knowledge of the underlying ontology/schema of the supporting knowledge base. To demonstrate the properties of the proposed interface we have developed QPedia1 , a mobile app that allows to take full advantages of DBpedia through our mobile-enabled userfriendly interface