- No need for registration – you can log in with your existing accounts at Google, Xing, LinkedIn or Twitter
- Upload and check as many vocabularies you like (100MB maximum size for each vocabulary)
- Access reports of the quality checks for all uploaded vocabularies
- Quality reports can also be sent to you by email, if you wish
Two live demos of PoolParty Semantic Integrator demonstrate new ways to retrieve information based on linked data technologies
Linked data graphs can be used to annotate and categorize documents. By transforming text into RDF graphs and linking them with LOD like DBpedia, Geonames, MeSH etc. completely new ways to make queries over large document repositories become possible.
An online-demo illustrates those principles: Imagine you were an information officer at the Global Health Observatory of the World Health Organisation. You inform policy makers about the global situation in specific disease areas to direct support to the required health support programs. For your research you need data about disease prevalence in relation with socioeconomic factors.
Datasets and technology
About 160.000 scientific abstracts from PubMed, linked to three different disease categories were collected. Abstracts were automatically annotated with PoolParty Extractor, based on terms from the Medical Subject Headings (MeSH) and Geonames that are organized in a SKOS thesaurus, managed with PoolParty Thesaurus Server. Abstracts were transformed to RDF and stored in Virtuoso RDF store. In the next step, it is easy to combine these data sets within the triple store with large linked data sources like DBPedia, Geonames or Yago. The use of linked data makes it easy to e.g. group annotated countries by the Human Development Index (HDI). The hierarchical structure of the thesaurus was used to collect all concepts that are connected to a specific disease.
This demo was developed based on the libraries sgvizler to visualize SPARQL results. AngularJS was used to dynamically replace variables in SPARQL query templates.
Another example of linked data based search in the field of renewable energy can be tried out here.
The European Data Forum (EDF) is an annual meeting place for industry, research, policy makers, and community initiatives to discuss the challenges and opportunities of data in Europe, especially in the light of recent developments such as Open Data, Linked Data and Big Data.
EDF 2014 will be held in Athens, Greece on March 19-20, 2014. The program will consist of a mixture of presentations, panels and networking sessions by industry, academics, policy makers, and community initiatives. Topics will cover a wide spectrum of research and technology development, applications, and socio-economic aspects of the data value chain.
Call for Contributions
EDF 2014 is seeking inspiring presentations addressing the topics listed below:
- Innovative research and technology for Open Data, Linked Data and Big Data
- Socio-economic and policy issues
- Data visions for the future
Proposals for presentations should be submitted as a single PDF file at https://www.easychair.org/conferences/?conf=edf2014 or sent via e-mail to firstname.lastname@example.org. Proposals will be reviewed by the Organizing Committee of EDF 2014 according to their relevance to the scope and purpose of the event.
- Submission of proposals: December 10 2013, 22.00pm CET
- Notification of acceptance or rejection: early January 2014
- Full EDF2014 program available: end of January 2014
The ongoing debate around the question whether ‘there is money in linked data or not’ has now been formulated more poignantly by Prateek Jain (one of the authors of the original article) recently: He is asking, ‘why linked open data hasn’t been used that much so far besides for research projects?‘.
I believe there are two reasons (amongst others) for the low uptake of LOD in non-academic settings which haven’t been discussed in detail until today:
Since most organizations live on their internal knowledge which they combine intelligently with very specific (and most often publicly available) knowledge (and data), they would benefit from LOD only if certain domains were covered. A frequently quoted ‘best practice’ for LOD is that portion of data sets which is available at Bio2RDF. This part of the LOD cloud has been used again and again by the life sciences industry due to its specific information and its highly active maintainers.
We need more ‘micro LOD clouds’ like this.
I believe that the first generation of LOD cloud has done a great job. It has visualised the general principles of linked data and was able to communicate the idea behind. It even helped – at least in the very first versions of it – to identify possibly interesting data sets. And most of all: it showed how fast the cloud was growing and attracted a lot of attention.
But now it’s time to clean up:
A first step should be to make a clear distinction between the section of the LOD cloud which is open and which is not. Datasets without licenses should be marked explicitly, because those are the ones which are most problematic for commercial use, not the ones which are not open.
A second improvement could be made by making some quality criteria clearly visible. I believe that the most important one is about maintenance and authorship: Who takes responsibility for the quality and trustworthiness of the data? Who exactly is the maintainer?
This brings me to the second and most important reason for the low uptake of LOD in commercial applications:
2. Most datasets of the LOD cloud are maintained by a single person or by nobody at all (at least as stated on datahub.io)
Would you integrate a web service which is provided by a single, maybe private person into a (core-)application of your company? Wouldn’t you prefer to work with data and services provided by a legal entity which has high reputation at least in its own knowledge domain? We all know: data has very little value if it’s not maintained in a professional manner. An example for a ’good practice’ is the integrated authority file provided by German National Library. I think this is a trustworthy source, isn’t it? And we can expect that it will be maintained in the future.
It’s not the data only which is linked in a LOD cloud, most of all it’s the people and organizations ‘behind the datasets’ that will be linked and will co-operate and communicate based on their datasets. They will create on top of their joint data infrastructure efficient collaboration platforms, like the one in the area of clean energy – the ‘Trusted Clean Energy LOD Cloud‘:
REEEP and its reegle-LD platform has become a central hub in the clean energy community. Not only data-wise but also as an important cooperation partner in a network of NGOs and other types of stakeholders which promote clean energy globally.
Linked Data has become the basis for more effective communication in that sector.
To sum up: To publish LOD which is interesting for the usage beyond research projects, datasets should be specific and trustworthy (another example is the German labor law thesaurus by Wolters Kluwer). I am not saying that datasets like DBpedia are waivable. They serve as important hubs in the LOD cloud, but for non-academic projects based on LOD we need an additional layer of linked open datasets, the Trusted LOD cloud.