The ADEQUATe project builds on two observations: An increasing amount of Open Data becomes available as an important resource for emerging businesses and furtheron the integration of such open, freely re-usable data sources into organisations’ data warehouse and data management systems is seen as a key success factor for competitive advantages in a data-driven economy. Continue reading
The SEMANTiCS2015 conference comes back this year in its 11th edition where it all started in 2005 to Vienna, Austria!
The conference takes place from 15-17 September 2015 (the main conference will be on 16-17th of September and several back 2 back workshops & events on 15th) at the University of Economics – see all information: http://semantics.cc/. Continue reading
The reduction of green house gas emissions is one of the big global challenges for the next decades. (Linked) Open Data on this multi-domain challenge is key for addressing the issues in policy, construction, energy efficiency, production a like. Today – on the World Environment Day 2014 – a new (linked open) data initiative contributes to this effort: GBPN’s Data Endpoint for Building Energy Performance Scenarios.
GBPN (The Global Buildings Performance Network) provides the full data set on a recently made global scenario analysis for saving energy in the building sector worldwide, projected from 2005 to 2050. The multidimensional dataset includes parameters like housing types, building vintages and energy uses – for various climate zones and regions and is freely available for full use and re-use as open data under CC-BY 3.0 France license.
To explore this easily, the Semantic Web Company has developed an interactive query / filtering tool which allows to create graphs and tables in slicing this multidimensional data cube. Chosen results can be exported as open data in the open formats: RDF and CSV and also queried via a provided SPARQL endpoint (a semantic web based data API). A built-in query-builder makes the use as well as the learning and understanding of SPARQL easy – for advanced users as well as also for non-experts or beginners.
The LOD based information- & data system is part of Semantic Web Companies’ recent Poolparty Semantic Drupal developments and is based on OpenLinks Virtuoso 7 QuadStore holding and calculating ~235 million triples as well as it makes use of the RDF ETL Tool: UnifiedViews as well as D2R Server for RDF conversion. The underlying GBPN ontology runs on PoolParty 4.2 and serves also a powerful domain-specific news aggregator realized with SWC’s sOnr webminer.
Together with other Energy Efficiency related Linked Open Data Initiatives like REEEP, NREL, BPIE and others, GBPNs recent initative is a contribution towards a broader availability of data supporting action agains global warming – as also Dr. Peter Graham, Executive Director of GBPN emphasized “…data and modelling of building energy use has long been difficult or expensive to access – yet it is critical to policy development and investment in low-energy buildings. With the release of the BEPS open data model, GBPN are providing free access to the world’s best aggregated data analyses on building energy performance.”
The Linked Open Data (LOD) is modelled using the RDF Data Cube Vocabulary (that is a W3C recommendation) including 17 dimensions in the cube. In total there are 235 million triples available in RDF including links to DBpedia and Geonames – linking the indicators: years – climate zones – regions and building types as well as user scenarios….
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.