The TUC will kick off its activities on November 19/20, 2012 at the first scheduled meeting in Barcelona. An online questionnaire is available at http://goo.gl/PwGtK for interested parties can make contributions on their experiences and needs for consideration in the LDBC benchmarks.
Read MoreThis week we received notification from the EU that the LDBC project has been granted. We think this is great news. The LDBC project (is a STREP and will run until Q2 2015. LDBC stands for Linked Data Benchmark Council, and linked data here of course comprises RDF data management, but also includes the emerging class of […]
Read More“Graphs are everywhere. Organizations of all sizes, from large enterprise to new startups, are embracing graph databases as the fastest way to query and store graph data. The EU has recognized this, and has funded the Linked Data Benchmark Council to promote and further the research in graph databases. We are grateful to the EU […]
Read MoreNovember 9, 2012, the EU confirmed the start of the new FP7 project called Linked Data Benchmark Council (LDBC). The main objective of LDBC is the development of benchmarks for the emerging field of RDF and graph data management systems, as well as to spur industry cooperation around such benchmarks. This new council of database […]
Read MoreSource: Data2Semantics Complexity metrics form the backbone of graph analysis. Centrality, betweenness, assortativity and scale freeness are just a handful of selections from a large and quickly growing literature. It seems that every purpose has its own notion of complexity. Can we find a way to tie these disparate notions together? Algorithmic statistics provide an […]
Read MoreSource: Data2Semantics TabLinker, introduced in an earlier post, is a spreadsheet to RDF converter. It takes Excel/CSV files as input, and produces enriched RDF graphs with cell contents, properties and annotations using the DataCube and Open Annotation vocabularies. TabLinker interprets spreadsheets based on hand-made markup using a small set of predefined styles (e.g. it needs to […]
Read MoreSource: Data2Semantics Part of work package 2 is developing machine learning techniques to automatically enrich linked data. The web of data has become so large, that maintaining it by hand is no longer possible. In contrast to existing techniques for learning for the semantic web, we aim at applying the techniques directly to the linked data. We use kernel […]
Read MoreSource: Data2Semantics Linkitup is a Web-based dashboard for enrichment of research output published via the Figshare.com repository service. For license terms, see below. Linkitup currently does two things: it takes metadata entered through Figshare.com and tries to find equivalent terms, categories, persons or entities on the Linked Data cloud and several Web 2.0 services. it […]
Read MoreSource: Data2Semantics Work package 5 of Data2Semantics focuses on the reconstruction of provenance information. Provenance is a hot topic in many domains, at it is believed that accurate provenance information can benefit measures of trust and quality. In science, this is certainly true. Provenance information in the form of citations is a centuries old practice. […]
Read MoreSource: Data2Semantics What if Dolly was only a partial replica? Work package 3 of Data2Semantics centers around the problem of ranking Linked Data. Over the past year, we have identified partial replication as a core use case for ranking. The amount of linked data is rapidly growing, especially in the life sciences and medical domain. At […]
Read More