Source: 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 extracts references from publications in Figshare.com, and tries to find the corresponding Digital Object Identifier (DOI).

Linkitup is developed as part of our strategy to bring technology for adding semantics to research data to actual users.

Linkitup currently contains five plugins:

• Wikipedia/DBPedia linking to tags and categories
• Linking of authors to the DBLP bibliography
• CrossRef linking of papers to DOIs through bibliographic reference extraction
• Elsevier Linked Data Repository linking to tags and categories
• ORCID linking to authors

Using Figshare allows Data2Semantics to:

• tap into a wealth of research data already published
• provide state-of-the art data enrichment services on a prominent platform with a significant user base, and
• bring RDF data publishing to a mainstream platform.
• And lastly, Figshare removes the need for a Data2Semantics data repository

Linkitup feeds the enriched metadata back as links to the original article in Figshare, but also builds a RDF representation of the metadata that can be downloaded separately, or published as research output on Figshare.

We aim to extend linkitup to connect to other research repositories such as EASY and the Dataverse Network.

A live version of Linkitup is available at http://linkitup.data2semantics.org. Note that the software is stil in beta! You will need a Figshare login and some data published in Figshare to get started.

More information, including installation instructions are available from Github.

Source: 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. However, this is not enough:

• What data is a scientific conclusion based on?
• How was that data gathered?
• Was the data altered in the process?
• Did the authors cite all sources?
• What part of the cited paper is used in the article?

Detailed provenance of scientific articles, presentations, data and images is often missing. Data2Semantics investigates the possibilities for reconstructing provenance information.

Over the past year, we have implemented a pipeline for provenance reconstruction (see picture), that is based on four steps: preprocessing, hypotheses generation, hypotheses pruning, aggragation and ranking. Each of these steps perform multiple analyses on a document that can be run in parallel.

Our first results, running this pipeline on a Dropbox folder (including version history), are encouraging. We achieve an F-score of 0.7  when we compare generated dependency relations to manually specified ones (our gold standard).

Sara Magliacane has a paper accepted at the ISWC 2012 Doctoral Consortium, where she explains her approach and methodology.

Source: 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 the same time, this data is increasingly dynamic: information is added and removed at a much more granular level than before. Where until recently the Linked Data cloud grew one dataset at a time, these datasets are now `live`, and can change as fast as every minute.

For most Linked Data applications, the cloud, but also the individual datasets are too big too handle. In some cases, as in e.g. the clinical decision support use case of Data2Semantics, one may need to support e.g. offline access from a tablet computer. Another use case is where semantic web development requires a testing environment: running your experiment on a full dataset will just take too much time.

The question then is, how can we make a proper selection of the original dataset that is sufficiently representative for the application purpose, while at the same time ensuring timely synchronisation with the original dataset whenever updates take place.

A first version of the partial replication implementation was integrated in the latest version of the Hubble CDS Prototype.

Laurens Rietveld has an accepted paper at the ISWC 2012 doctoral consortium, where he explains his research approach and methodology. You can find the paper at http://www.mendeley.com/research/replication-for-linked-data/

Source: Data2Semantics

For over a year now, Data2Semantics organizes biweekly lunches for all COMMIT projects running at VU Amsterdam under the header ‘COMMIT@VU’. These projects are SEALINCMedia, EWIDS, METIS, e-Infrastructure Virtualization for e-Science Applications, Data2Semantics, and eFoodLab.

On October 29th, we had a lively discussion about opportunities to collaborate across projects (see photo).

Concretely:

• SEALINCMedia and Data2Semantics will collaborate on trust and provenance. Trust and provenance analysis technology developed in SEALINCMedia can benefit from the extensive provenance graphs constructed in Data2Semantics, and vice versa.
• There is potential for integrating the Amalgame vocabulary alignment toolkit (SEALINCMedia) in the Linkitup service of Data2Semantics. Also, Amalgame can play a role in the census use case of Data2Semantics (aligning vocabularies of occupations through time)
• Both the SEALINCMedia and Data2Semantics projects are working on annotation methodologies and tools. Both adopt the Open Annotation model, and require multi-layered annotations (i.e. annotations on annotations).
• eFoodlab and Data2Semantics are both working on annotating and interpreting spreadsheet data. We already had a joint meeting last year on this topic, but it makes sense to revive this collaboration.
• We decided to gather vocabularies and datasets used by the various projects to make more clear where expertise in using these vocabularies lies. Knowing at a glance who else is using a particular dataset or vocabulary can be very helpful: you know on who’s door to knock if you have questions or want to share experiences.

Source: Data2Semantics

A quick heads up on the progress of Data2Semantics over the course of the third quarter of 2012.

Management summary: we have made headway in developing data enrichment and analysis tools that will have use in practice.

First of all, we developed a first version of a tool for enriching and interlinking data stored in the popular Figshare

data repository. This tool, called Linkitup, takes metadata provided by the data publisher (usually the author) and can link it to DBPedia/Wikipedia, DBLP, ORCID, ScopusID, the Elsevier Linked Data Repository. These links are fed back to the publication in Figshare, but can also be published separately as RDF. This way, we use Web 2.0 mechanisms that are popular amongst researchers to allow them to enrich their data, and reap immediate benefit. Our plans are to integrate increasingly elaborate enrichment and analysis tools in this dashboard (e.g. annotation, complexity analysis, provenance reconstruction, etc.)

Linkitup is available from http://github.com/Data2Semantics/linkitup . We are aiming for a first release soon!

Furthermore, we have made a good start at reconstructing provenance information from documents stored in a Dropbox folder. This technology can be used to trace back research ideas through chains of publications in a way that augments and extends the citation network. The provenance graph does not necessarily span just published papers, but can be constructed for a variety of document types (posters, presentations, notes, documents, spreadsheets, etc.).

A first implementation of (partial) linked data replication that will make dealing with large volumes of linked data much more manageable. The crux of partial replication lies in the selection (i.e. ranking) of a suitable subset of data to replicate. We will use data information measures, graph analysis, and statistical methods to perform this selection. The use case of clinical decision support will be the primary testing ground for this technology.