An extension of SPARQL for expressing qualitative preferences

In this paper we present SPREFQL, an extension of the SPARQL language that allows appending a PREFER clause that expresses "soft" preferences over the query results obtained by the main body of the query. The extension does not add expressivity and any SPREFQL query can be transformed to an equivalent standard SPARQL query. However, clearly separating preferences from the "hard" patterns and filters in the WHERE clause gives queries where the intention of the client is more cleanly expressed, an advantage for both human readability and machine optimization. In the paper we formally define the syntax and the semantics of the extension and we also provide empirical evidence that optimizations specific to SPREFQL improve run-time efficiency by comparison to the usually applied optimizations on the equivalent standard SPARQL query.Comment: Accepted to the 2017 International Semantic Web Conference, Vienna, October 201

A Model of User Preferences for Semantic Services Discovery and Ranking

Current proposals on Semantic Web Services discovery and ranking are based on user preferences descriptions that often come with insufficient expressiveness, consequently making more difficult or even preventing the description of complex user desires. There is a lack of a general and comprehensive preference model, so discovery and ranking proposals have to provide ad hoc preference descriptions whose expressiveness depends on the facilities provided by the corresponding technique, resulting in user preferences that are tightly coupled with the underlying formalism being used by each concrete solution. In order to overcome these problems, in this paper an abstract and sufficiently expressive model for defining preferences is presented, so that they may be described in an intuitively and user-friendly manner. The proposed model is based on a well-known query preference model from database systems, which provides highly expressive constructors to describe and compose user preferences semantically. Furthermore, the presented proposal is independent from the concrete discovery and ranking engines selected, and may be used to extend current Semantic Web Service frameworks, such as wsmo, sawsdl, or owl-s. In this paper, the presented model is also validated against a complex discovery and ranking scenario, and a concrete implementation of the model in wsmo is outlined.Comisión Interministerial de Ciencia y Tecnología TIN2006-00472Comisión Interministerial de Ciencia y Tecnología TIN2009-07366Junta de Andalucía TIC-253

Garbage collection in C++

SIGLEAvailable from TIB Hannover: RN 4106(243) / FIZ - Fachinformationszzentrum Karlsruhe / TIB - Technische InformationsbibliothekDEGerman

Neogeography: The Challenge of Channelling Large and Ill-Behaved Data Streams

Neogeography is the combination of user generated data and experiences with mapping technologies. In this paper we propose a research project to extract valuable structured information with a geographic component from unstructured user generated text in wikis, forums, or SMSes. The project intends to help workers communities in developing countries to share their knowledge, providing a simple and cheap way to contribute and get benefit using the available communication technology

PQMPMS: A Preference-enabled Querying Mechanism for Personalized Mobile Search

Abstract. A key challenge for personalized mobile search is to tailor the answers to the specific user by considering her contextual situation. To adapt the retrieved items to user’s context, this paper presents a preference-enabled querying mechanism for personalized mobile search. By exploiting the user’s dialogue history, we infer the weighted user preferences and interests. To further compute personalized answers, we aim to continuously collect the ratings given by the user’s friends regarding relevant topics from stream-based data sources such as Twitter. An experiment shows that our approach allows to compute the most relevant answers, providing an increased quality of search experience for the user