Publishing, Harmonizing and Consuming Census Data: the CEDAR Project

<p>This paper discusses the use of Linked Open Data to increase complex tabular datasets quality, machine­processability, and ease of format transformation. We illustrate this application with the historical Dutch censuses: census data is open, but notoriously difficult to compare, aggregate and query in a uniform fashion. We describe an approach to achieve these goals, emphasizing open problems and trade­offs.</p

Graph Sampling for Linked Data: DBPedia Results

<p>Results for Graph Sampling for Linked Data, a submission for ISWC 2013</p> <p>We apply different rewrite methods to transform our RDF graph into an unweighted directed graph with unlabelled edges. The rewritten graph is analysed using different standard network algorithms, after which the weights are aggregated back to the RDF triples.</p> <p>We measure the quality of the subgraph using query logs, where we calculate the recall by executing each query on the subgraph as well as the original graph.</p> <p>Navigatable results (i.e. html) are available here:<br>http://data2semantics.github.io/GraphSampling/</p> <p> </p

Graph Sampling for Linked Data: Semantic Web Dog Food Results

<p>Results for Graph Sampling for Linked Data, a submission for ISWC 2013<br>We apply different rewrite methods to transform our RDF graph into an unweighted directed graph with unlabelled edges. The rewritten graph is analysed using different standard network algorithms, after which the weights are aggregated back to the RDF triples.<br>We measure the quality of the subgraph using query logs, where we calculate the recall by executing each query on the subgraph as well as the original graph.<br>Navigatable results (i.e. html) are available here:<br>http://data2semantics.github.io/GraphSampling/</p> <p> </p

Graph Samping Results

<p>These boxplots show the recall distribution of our queries for each combination of rewrite method and network analysis algorithm for the 4 datasets we considered. The bounds of the box represent the lower and upper quartiles of the recall scores. The average recall is denoted by the triangle and the horizontal line provides the median recall score. Whiskers extend to datapoints that are up to 1.5 times larger and smaller than the interquartile range. Any points outside this range are considered outliers, and are represented as dots.</p> <p> </p> <p>Whenever we can claim a statistically significant better recall than one of our baselines, we show this using + or * signs. Our significance calculations are public as well on github: https://github.com/Data2Semantics/GraphSampling/blob/master/bin/significance</p

Graph Sampling for Linked Data

<p>Results for Graph Sampling for Linked Data, a submission for ISWC 2013</p> <p>We apply different rewrite methods to transform our RDF graph into an unweighted directed graph with unlabelled edges. The rewritten graph is analysed using different standard network algorithms, after which the weights are aggregated back to the RDF triples. </p> <p>We measure the quality of the subgraph using query logs, where we calculate the recall by executing each query on the subgraph as well as the original graph. </p> <p> </p> <p>Navigatable results (i.e. html) are available here:<br>http://data2semantics.github.io/GraphSampling/</p> <p> </p

Graph Sampling for Linked Data: SP2Bench Results

<p>Results for Graph Sampling for Linked Data, a submission for ISWC 2013<br>We apply different rewrite methods to transform our RDF graph into an unweighted directed graph with unlabelled edges. The rewritten graph is analysed using different standard network algorithms, after which the weights are aggregated back to the RDF triples.<br>We measure the quality of the subgraph using query logs, where we calculate the recall by executing each query on the subgraph as well as the original graph.<br>Navigatable results (i.e. html) are available here:<br>http://data2semantics.github.io/GraphSampling/</p> <p> </p

Graph Sampling Results

<p>Boxplots showing the quality of the sampled subgraphs. The quality/relevance is based on the recall of a given set of queries for this dataset.</p> <p>Whenever we can claim a statistically significant better recall than one of our baselines, we show this using + or * signs. Our significance calculations are public as well on github: <a href="https://github.com/Data2Semantics/GraphSampling/blob/master/bin/significance/getSignificance.R">https://github.com/Data2Semantics/GraphSampling/blob/master/bin/significance/getSignificance.R</a></p> <p> </p

Graph Sampling for Linked Data: Linked Movie Database Results

<p>Results for Graph Sampling for Linked Data, a submission for ISWC 2013<br>We apply different rewrite methods to transform our RDF graph into an unweighted directed graph with unlabelled edges. The rewritten graph is analysed using different standard network algorithms, after which the weights are aggregated back to the RDF triples.<br>We measure the quality of the subgraph using query logs, where we calculate the recall by executing each query on the subgraph as well as the original graph.<br>Navigatable results (i.e. html) are available here:<br>http://data2semantics.github.io/GraphSampling/</p> <p> </p

Demonstrating The Entity Registry System: Implementing 5-Star Linked Data Without the Web

Abstract. Linked Data applications often assume that connectivity to data repositories and entity resolution services are always available. This may not be a valid assumption in many cases. Indeed, there are about 4.5 billion people in the world who have no or limited Web access. Many data-driven applications may have a critical impact on the life of those people, but are inaccessible to such populations due to the architecture of today’s data registries. In this demonstration, we show how our new open-source ERS system can be used as a general-purpose entity registry suitable for deployment in poorly-connected or ad-hoc environments.

Bilingual Researcher Profiles: Modeling Dutch Researchers in both English and Dutch Using the VIVO Ontology

<p>In this poster we describe the process of mapping researcher information from the Dutch National Academic Research and Collaborations Information System (NARCIS) to the VIVO ontology. Our goal is to use the VIVO ontology to accurately represent these researchers and their organizations, while remaining true to the native language and structure of the Dutch university. To achieve this, we first created an extension ontology to account for differences in the Dutch naming structure and differences in university position description and alignment. Secondly, through the use of language attribute tags, we recorded data in both English and Dutch to achieve better access by both the native Dutch population and the larger English based research community. Finally, we leveraged the SKOS ontology to take advantage of a classification structure, already created by NARCIS, to describe researcher expertise uniformly across the system. <em>Presented at ASIST 2013, Nov 1-6, Montreal, Canada</em></p