Tutorial on Scalable Integration of Linked DataThe goal of this tutorial is to introduce, motivate and detail techniques for integrating heterogeneous structured data from across the Web. Inspired by the growth in Linked Data publishing, our tutorial aims at educating Web researchers and practitioners about this new publishing paradigm. The tutorial will show how Linked Data enables uniform access, parsing and interpretation of data, and how this novel wealth of structured data can potentially be exploited for creating new applications or enhancing existing ones.
Linked Enterprise Data PatternsThe goal of this workshop is threefold:
Describe a set of standard patterns, design choices and best practices that will give application writers much stronger guidance and reduce the number of design decisions they have to make when adopting Linked Data as an application architecture.
Identify a handful of gaps in the current Linked Data specs that are uncovered by common application scenarios.
Propose a set of simple solutions to those gaps that could be rapidly specified and approved by W3C
DOIs as Linked DataCrossRef has made the metadata for 46 million Digital Object Identifiers (DOI) available as Linked Data. DOIs are heavily used in the publishing space to uniquely identify electronic documents (largely scholarly journal articles). CrossRef is a consortium of roughly 3,000 publishers, and is a big player in the academic publishing marketplace.
So practically what this means is that all the places in the scholarly publishing ecosystem where DOIs are present (caveat below), it's now possible to use the Web to retrieve metadata associated with that electronic document. Say you've got a DOI in the database backing your institutional repository:
15 Ways to Think About Data Quality (Just for a Start)I don't think data quality is an amorphous, aesthetic, hopelessly subjective topic. Data "beauty" might be subjective, and the same data may have different applicability to different tasks, but there are a lot of obvious and straightforward ways of thinking about the quality of a dataset independent of the particular preferences of individual beholders. Here are just some of them:
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