On Feeding Business Systems with Linked Resources from the Web of Data

Business systems that are fed with data from the Web of Data require transparent interoperability. The Linked Data principles establish that different resources that represent the same real-world entities must be linked for such purpose. Link rules are paramount to transparent interoperability since they produce the links between resources. State-of-the-art link rules are learnt by genetic programming and build on comparing the values of the attributes of the resources. Unfortunately, this approach falls short in cases in which resources have similar values for their attributes, but represent different real-world entities. In this paper, we present a proposal that leverages a genetic programming that learns link rules and an ad-hoc filtering technique that boosts them to decide whether the links that they produce must be selected or not. Our analysis of the literature reveals that our approach is novel and our experimental analysis confirms that it helps improve the F1 score by increasing precision without a significant penalty on recall.Ministerio de Economía y Competitividad TIN2013-40848-RMinisterio de Economía y Competitividad TIN2016- 75394-

The TMS Map Scales with Increased Stimulation Intensity and Muscle Activation

One way to study cortical organisation, or its reorganisation, is to use transcranial magnetic stimulation (TMS) to construct a map of corticospinal excitability. TMS maps are reported to be acquired with a wide variety of stimulation intensities and levels of muscle activation. Whilst MEPs are known to increase both with stimulation intensity and muscle activation, it remains to be established what the effect of these factors is on the map's centre of gravity (COG), area, volume and shape. Therefore, the objective of this study was to systematically examine the effect of stimulation intensity and muscle activation on these four key map outcome measures. In a first experiment, maps were acquired with a stimulation intensity of 110, 120 and 130% of resting threshold. In a second experiment, maps were acquired at rest and at 5, 10, 20 and 40% of maximum voluntary contraction. Map area and map volume increased with both stimulation intensity (P 0.09 in all cases). This result indicates the map simply scales with stimulation intensity and muscle activation

A Hybrid Genetic-Bootstrapping Approach to Link Resources in the Web of Data

In the Web of Data, real-world entities are represented by means of resources, for instance the southern Spanish city “Seville” that is represented by means of the resource that is available at http://es.dbpedia.org/page/Sevilla in the DBpedia dataset. Link rules are intended to link resources that are different, but represent the same real-world entities; for instance the resource that is available at https://www.wikidata.org/wiki/Q8717 represents exactly the same real-world entity as the resource aforementioned. A link rule may establish that two resources that represent cities should be linked as long as the GPS coordinates are the same. Such rules are then paramount to integrating web data, because otherwise programs would deal with every resource independently from the other. Knowing that the previous resources rep resent the same real-world entity allows them to merge the information that they provide independently (which is commonly known as integrat ing link data). State-of-the-art link rules are learnt by genetic program ming systems and build on comparing the values of the attributes of the resources. Unfortunately, this approach falls short in cases in which resources have similar values for their attributes, but represent different real-world entities. In this paper, we present a proposal that hybridises a genetic programming system that learns link rules and an ad-hoc filter ing technique that bootstraps them to decide whether the links that they produce must be selected or not. Our analysis of the literature reveals that our approach is novel and our experimental analysis confirms that it helps improve the F1 score, which is defined in the literature as the harmonic mean of precision and recall, by increasing precision without a significant penalty on recall

Introduction to Linked Data and Its Lifecycle on the Web

With linked data, a very pragmatic approach towards achieving the vision of the semantic web has gained some traction in the last years. The term linked data refers to a set of best practices for publishing and interlinking structured data on the web. While many standards, methods and technologies developed within by the semantic web community are applicable for linked data, there are also a number of specific characteristics of linked data, which have to be considered. In this article we introduce the main concepts of linked data. We present an overview of the linked data lifecycle and discuss individual approaches as well as the state-of-the-art with regard to extraction, authoring, linking, enrichment as well as quality of linked data. We conclude the chapter with a discussion of issues, limitations and further research and development challenges of linked data. This article is an updated version of a similar lecture given at reasoning web summer school 2011

Introduction to Linked Data and Its Lifecycle on the Web

With linked data, a very pragmatic approach towards achieving the vision of the semantic web has gained some traction in the last years. The term linked data refers to a set of best practices for publishing and interlinking structured data on the web. While many standards, methods and technologies developed within by the semantic web community are applicable for linked data, there are also a number of specific characteristics of linked data, which have to be considered. In this article we introduce the main concepts of linked data. We present an overview of the linked data lifecycle and discuss individual approaches as well as the state-of-the-art with regard to extraction, authoring, linking, enrichment as well as quality of linked data. We conclude the chapter with a discussion of issues, limitations and further research and development challenges of linked data. This article is an updated version of a similar lecture given at reasoning web summer school 2011

The BigDataEurope platform - supporting the variety dimension of big data

The management and analysis of large-scale datasets - described with the term Big Data - involves the three classic dimensions volume, velocity and variety. While the former two are well supported by a plethora of software components, the variety dimension is still rather neglected. We present the BDE platform - an easy-to-deploy, easy-to-use and adaptable (cluster-based and standalone) platform for the execution of big data components and tools like Hadoop, Spark, Flink, Flume and Cassandra. The BDE platform was designed based upon the requirements gathered from seven of the societal challenges put forward by the European Commission in the Horizon 2020 programme and targeted by the BigDataEurope pilots. As a result, the BDE platform allows to perform a variety of Big Data flow tasks like message passing, storage, analysis or publishing. To facilitate the processing of heterogeneous data, a particular innovation of the platform is the Semantic Layer, which allows to directly process RDF data and to map and transform arbitrary data into RDF. The advantages of the BDE platform are demonstrated through seven pilots, each focusing on a major societal challenge