datos.bne.es: A library linked dataset

We describe the datos.bne.es library dataset. The dataset makes available the authority and bibliography catalogue from the Biblioteca Nacional de España (BNE, National Library of Spain) as Linked Data. The catalogue contains around 7 million authority and bibliographic records. The records in MARC 21 format were transformed to RDF and modelled using IFLA (International Federation of Library Associations) ontologies and other well-established vocabularies such as RDA (Resource Description and Access) or the Dublin Core Metadata Element Set. A tool named MARiMbA automatized the RDF generation process and the data linkage to DBpedia and other library linked data resources such as VIAF (Virtual International Authority File) or GND (Gemeinsame Normdatei, the authority dataset from the German National Library).

Using Provenance for Quality Assessment and Repair in Linked Open Data

As the number of data sources publishing their data on the Web of Data is growing, we are experiencing an immense growth of the Linked Open Data cloud. The lack of control on the published sources, which could be untrustworthy or unreliable, along with their dynamic nature that often invalidates links and causes conflicts or other discrepancies, could lead to poor quality data. In order to judge data quality, a number of quality indicators have been proposed, coupled with quality metrics that quantify the “quality level” of a dataset. In addition to the above, some approaches address how to improve the quality of the datasets through a repair process that focuses on how to correct invalidities caused by constraint violations by either removing or adding triples. In this paper we argue that provenance is a critical factor that should be taken into account during repairs to ensure that the most reliable data is kept. Based on this idea, we propose quality metrics that take into account provenance and evaluate their applicability as repair guidelines in a particular data fusion setting.

A core ontological model for semantic sensor web infrastructures

Semantic Sensor Web infrastructures use ontology-based models to represent the data that they manage; however, up to now, these ontological models do not allow representing all the characteristics of distributed, heterogeneous, and web-accessible sensor data. This paper describes a core ontological model for Semantic Sensor Web infrastructures that covers these characteristics and that has been built with a focus on reusability. This ontological model is composed of different modules that deal, on the one hand, with infrastructure data and, on the other hand, with data from a specific domain, that is, the coastal flood emergency planning domain. The paper also presents a set of guidelines, followed during the ontological model development, to satisfy a common set of requirements related to modelling domain-specific features of interest and properties. In addition, the paper includes the results obtained after an exhaustive evaluation of the developed ontologies along different aspects (i.e., vocabulary, syntax, structure, semantics, representation, and context).

Enabling query technologies for the semantic sensor web

Sensor networks are increasingly being deployed in the environment for many different purposes. The observations that they produce are made available with heterogeneous schemas, vocabularies and data formats, making it difficult to share and reuse this data, for other purposes than those for which they were originally set up. The authors propose an ontology-based approach for providing data access and query capabilities to streaming data sources, allowing users to express their needs at a conceptual level, independent of implementation and language-specific details. In this article, the authors describe the theoretical foundations and technologies that enable exposing semantically enriched sensor metadata, and querying sensor observations through SPARQL extensions, using query rewriting and data translation techniques according to mapping languages, and managing both pull and push delivery modes.

SRBench: A streaming RDF/SPARQL benchmark

We introduce SRBench, a general-purpose benchmark primarily designed for streaming RDF/SPARQL engines, completely based on real-world data sets from the Linked Open Data cloud. With the increasing problem of too much streaming data but not enough tools to gain knowledge from them, researchers have set out for solutions in which Semantic Web technologies are adapted and extended for publishing, sharing, analysing and understanding streaming data. To help researchers and users comparing streaming RDF/SPARQL (strRS) engines in a standardised application scenario, we have designed SRBench, with which one can assess the abilities of a strRS engine to cope with a broad range of use cases typically encountered in real-world scenarios. The data sets used in the benchmark have been carefully chosen, such that they represent a realistic and relevant usage of streaming data. The benchmark defines a concise, yet omprehensive set of queries that cover the major aspects of strRS processing. Finally, our work is complemented with a functional evaluation on three representative strRS engines: SPARQLStream, C-SPARQL and CQELS. The presented results are meant to give a first baseline and illustrate the state-of-the-art.

On correctness in RDF stream processor benchmarking

Two complementary benchmarks have been proposed so far for the evaluation and continuous improvement of RDF stream processors: SRBench and LSBench. They put a special focus on different features of the evaluated systems, including coverage of the streaming extensions of SPARQL supported by each processor, query processing throughput, and an early analysis of query evaluation correctness, based on comparing the results obtained by different processors for a set of queries. However, none of them has analysed the operational semantics of these processors in order to assess the correctness of query evaluation results. In this paper, we propose a characterization of the operational semantics of RDF stream processors, adapting well-known models used in the stream processing engine community: CQL and SECRET. Through this formalization, we address correctness in RDF stream processor benchmarks, allowing to determine the multiple answers that systems should provide. Finally, we present CSRBench, an extension of SRBench to address query result correctness verification using an automatic method.

Implementación de un recolector de RDF

La web semántica aporta un mayor conocimiento a los datos para que estos puedan ser procesados por las máquinas. Esto es posible gracias a estándares como por ejemplo Resource Framework Description (RDF). Éste, aporta un marco para que la información pueda ser representada de una manera más comprensible para las maquinas. Muchas veces la información no se encuentra codificada en RDF pero igualmente es interesante aprovecharse de sus características. Es por ello que surge la necesidad de crear una herramienta que permita consultas entre distintas fuentes de datos apoyándose en el estándar RDF independientemente del formato de origen de los datos. De esta manera se conseguirá realizar consultas entre las diversas fuentes, las cuales, sin la unificación en un estándar semántico, serían mucho más difíciles de conseguir.---ABSTRACT---The Semantic Web provides a new knowledge framework to data, therefore computers would become capable of analyzing the data. Standards, as Resource Framework Description (RDF), help to achieve it. RDF promotes the easier way for computers on how to describe data. Sometimes data are coded in a different way from RDF, nevertheless it would also be interesting to examine it. Accordingly, the need to create new software emerges. The software, based on RDF, would be able to combine information from different sources regardless of its format. Consequently, several sources, whatever their original formats were, could be queried on an easier way since a common semantic standard is available.

Estudio de fiabilidad y viabilidad de la Web 2.0 y la Web semántica para enriquecer lexicones en el dominio farmacológico

Los actuales sistemas de Reconocimiento de Entidades en el dominio farmacológico, necesarios como apoyo para el personal sanitario en el proceso de prescripción de un tratamiento farmacológico, sufren limitaciones relacionadas con la falta de cobertura de las bases de datos oficiales. Parece por tanto necesario analizar la fiabilidad de los recursos actuales existentes, tanto en la Web Semántica como en la Web 2.0, y determinar si es o no viable utilizar dichos recursos como fuentes de información complementarias que permitan generar y/o enriquecer lexicones empleados por sistemas de Reconocimiento de Entidades. Por ello, en este trabajo se analizan las principales fuentes de información relativas al dominio farmacológico disponibles en Internet. Este análisis permite concluir que existe información fiable y que dicha información permitiría enriquecer los lexicones existentes con sinónimos y otras variaciones léxicas o incluso con información histórica no recogida ni mantenida en las bases de datos oficiales.

Semantic Description of Web Services and Possibilities of BPEL4WS

The using of the upsurge of semantics web technologies gives a possibility for an increasing of the flexibility, extensibility and consistency of the existent industrial standards for modeling of web services. In the paper the types of semantic description of web services and the degree of their realization in BPEL4WS (Business Process Execution Language for Web Services) respectively on the abstract and executable level are treated. The methods for using of BPEL4WS for the purposes of semantic web services in the direction of their semi-automatic integration are suggested.

Linked knowledge sources for topic classification of microposts:a semantic graph-based approach

Short text messages a.k.a Microposts (e.g. Tweets) have proven to be an effective channel for revealing information about trends and events, ranging from those related to Disaster (e.g. hurricane Sandy) to those related to Violence (e.g. Egyptian revolution). Being informed about such events as they occur could be extremely important to authorities and emergency professionals by allowing such parties to immediately respond. In this work we study the problem of topic classification (TC) of Microposts, which aims to automatically classify short messages based on the subject(s) discussed in them. The accurate TC of Microposts however is a challenging task since the limited number of tokens in a post often implies a lack of sufficient contextual information. In order to provide contextual information to Microposts, we present and evaluate several graph structures surrounding concepts present in linked knowledge sources (KSs). Traditional TC techniques enrich the content of Microposts with features extracted only from the Microposts content. In contrast our approach relies on the generation of different weighted semantic meta-graphs extracted from linked KSs. We introduce a new semantic graph, called category meta-graph. This novel meta-graph provides a more fine grained categorisation of concepts providing a set of novel semantic features. Our findings show that such category meta-graph features effectively improve the performance of a topic classifier of Microposts. Furthermore our goal is also to understand which semantic feature contributes to the performance of a topic classifier. For this reason we propose an approach for automatic estimation of accuracy loss of a topic classifier on new, unseen Microposts. We introduce and evaluate novel topic similarity measures, which capture the similarity between the KS documents and Microposts at a conceptual level, considering the enriched representation of these documents. Extensive evaluation in the context of Emergency Response (ER) and Violence Detection (VD) revealed that our approach outperforms previous approaches using single KS without linked data and Twitter data only up to 31.4% in terms of F1 measure. Our main findings indicate that the new category graph contains useful information for TC and achieves comparable results to previously used semantic graphs. Furthermore our results also indicate that the accuracy of a topic classifier can be accurately predicted using the enhanced text representation, outperforming previous approaches considering content-based similarity measures. © 2014 Elsevier B.V. All rights reserved.

Stretching the life of Twitter classifiers with time-stamped semantic graphs

Social media has become an effective channel for communicating both trends and public opinion on current events. However the automatic topic classification of social media content pose various challenges. Topic classification is a common technique used for automatically capturing themes that emerge from social media streams. However, such techniques are sensitive to the evolution of topics when new event-dependent vocabularies start to emerge (e.g., Crimea becoming relevant to War Conflict during the Ukraine crisis in 2014). Therefore, traditional supervised classification methods which rely on labelled data could rapidly become outdated. In this paper we propose a novel transfer learning approach to address the classification task of new data when the only available labelled data belong to a previous epoch. This approach relies on the incorporation of knowledge from DBpedia graphs. Our findings show promising results in understanding how features age, and how semantic features can support the evolution of topic classifiers.

EPCIS Event-Based traceability in pharmaceutical supply chains via automated generation of linked pedigrees

In this paper we show how event processing over semantically annotated streams of events can be exploited, for implementing tracing and tracking of products in supply chains through the automated generation of linked pedigrees. In our abstraction, events are encoded as spatially and temporally oriented named graphs, while linked pedigrees as RDF datasets are their specific compositions. We propose an algorithm that operates over streams of RDF annotated EPCIS events to generate linked pedigrees. We exemplify our approach using the pharmaceuticals supply chain and show how counterfeit detection is an implicit part of our pedigree generation. Our evaluation results show that for fast moving supply chains, smaller window sizes on event streams provide significantly higher efficiency in the generation of pedigrees as well as enable early counterfeit detection.

O futuro da web semântica e sua importância nas organizações

Trabalho realizado sob orientação do Prof. António Brandão Moniz para a disciplina “Factores Sociais da Inovação” do Mestrado Engenharia Informática realizado na Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa (Portugal)

An Approach To Publish a Data Warehouse Content as Linked Data

Mestrado em Engenharia Informática - Área de Especialização em Tecnologias do Conhecimento e Decisão

Llenguatges de consulta en l'àmbit de la Web Semàntica : Cas d'estudi SPARQL

Aquest projecte s'emmarca dintre la idea de la Web Semàntica. A la primera part introdueix progressivament al tema de la Web Semàntica fins arribar a establir la necessitat de tenir SGBDs. La segon part explota algun dels SGBDs estudiats per realitzar una aplicació web que permeti mostrar alguna aplicació de la Web Semàntica.