Thomas Schandl

PoolParty 3.0 and its all new Linked Data framework

The new major release of PoolParty boasts with new Linked Data capabilities that further unlock the potential that the Semantic Web can bring to improve your metadata management, to enhance your data with external knowledge and to ease data integration efforts within your organization and with your partners.

In PoolParty 3.0 we created a Linked Data interlinking editor, making it easier than ever to add your own lookup and interlinking services (even for non-RDF sources) and made the Linked Data publishing front-end fully customizable in design, layout and regards to which parts of your content will be displayed.

But let’s start at the beginning:

Step 1 – Hook into the Linked Data Cloud!

In the era of the rapidly growing Linked Data Cloud your knowledge models don’t need to stay isolated from the outside world anymore. Simply use PoolParty’s new and improved lookup service to find matching resources from the Linked Open Data Cloud (e.g. from DBpedia).

Imagine having different data models that all refer to the same product categories and world regions. Once you have them represented in PoolParty you can use its lookup service to find matching resources from the Linked Data Cloud. In this way you will get globally used identifiers for your product categories and regions, usually in the form of a URI like http://dbpedia.org/resource/Berlin. This eases your internal data integration efforts, and it can aid the data exchange with partners or customers and enables hassle-free distributed management of knowledge models.

Image 1: Lookup of concept ‘Austria’ and selection of properties and values to be imported

 

With PoolParty 3.0 we increased the number of included lookup services: DBpedia, Geonames, Wordnet, Umbel, Yago, Freebase, Sindice, dmoz and LCSH – BBC Wildlife, Enis and Gemet are available on request.

Step 2 – Pull in Semantic Data!

There is a vast amount of Linked Data out there just waiting to be leveraged for thesaurus creation and extension. To meet that end we had a close look at our interlinking module and decided to enhance it a way that it becomes more of a Linked Data editor.

Once you have a base thesaurus in PoolParty and hooked a couple of your concepts into the cloud as described above, you can proceed to pull in the good stuff that comes with the Linked Data resources you have found.

Image 2: Imported Linked Data for concept ‘London’

 

As you can see in the image above, you can extend your local thesaurus with labels, definitions and all kinds of other information like e.g. in the case of countries their population, GDP, spoken languages, famous people born there, newspaper articles related to the political situation, and so on.

Now PoolParty 3.0 takes this approach a couple of steps further. You can not only specify which of your local concepts corresponds to which Linked Data resource and grab all semantic information that comes with this resource, but now you are able to selectively pick out the data items you are interested in and even transform the predicates they originally came with. Just switch them to whatever custom properties you created or want to re-use from any ontology (see an example in Image 1).

In this way you can easily enrich your own knowledge models with external information – which in turn can be utilized for better content recommendation, easier data integration and improved search services.

Step 3 – Publish your Linked Data in Style

Previous PoolParty versions already offered the possibility to instantly publish your thesauri, taxonomies or vocabularies and display their concepts as HTML while additionally providing machine-readable RDF versions for them. This means that anyone using PoolParty intuitive GUI can become a W3C standards compliant Linked Data publisher without having to know anything about Semantic Web technicalities.
Of course you don’t need to publish all your valuable models, just choose the parts that safely can be shared with the public and keep everything else behind your firewall, available only to you and trusted partners!

In this new release of PoolParty the design of all pages on the Linked Data front-end is now under your full control. You can use your own style sheets and create views on your data with velocity templates. It is even possible to develop project- and thesaurus-specific templates and layouts, so they can have an individual look and display different predicates and their values.

Take a look at PoolParty´s standard linked data frontend!

The following images show a PoolParty default Linked Data page and a custom-made Linked Data page of a PoolParty concept that has some DBpedia info imported.

Image 3: PoolParty default Linked Data page

PoolParty Linked Data page of ScOT thesaurus courtesy of Educational Services Australia
Image 4: Custom Linked Data page of ScOT thesaurus (courtesy of Educational Services Australia)

 

Step 4 – Unlock new Linked Data Sources

With PoolParty 3.0 you are in no way limited to DBpedia, Freebase, Geonames and the other lookup services that PoolParty provides out of the box: you can add your own non-Semantic Web data sources to the mix, thereby enabling you to boldly go where no Linked Data tool has gone before.

Maybe you have a product thesaurus and want to specify which products are related to patents that can be found with Google Patents?
Or you want to interlink concepts from a company taxonomy with related articles from the Guardian’s search service or any other newspaper that provides a search API?

All those sources are not available as RDF, so how can you re-use them easily as data sources for Linked Data style interlinking? For such cases PoolParty introduces the Unified Lookup API, which makes it easy to turn almost any third party Web API into a source for interlinking your concepts with third party resources as described above.

This makes it possible to interlink your concepts with many kinds of data out there, be it New York Times articles, UN data, synonym services, abbreviations, press releases, juridical information – or any web API important for your knowledge domain.

That being said, if you have suggestions for additional lookup services that you think are interesting, let us know!

To gain a first hand impression of the new PoolParty just apply for a demo account!

Thomas Schandl

Drupal and the Semantic Web – Interview with Stéphane Corlosquet

Stéphane Corlosquet has been the main driving force in incorporating Semantic Web capabilities into Drupal. In the recent release of Drupal 7, Semantic Web technologies became part of the core of this popular CMS, which is used to power at least 1% of all the world’s web sites.

Drupal is the leading CMS when it comes to implementing Semantic Web standards. What are the reasons for this, what makes Drupal such a good fit for Semantic Web technologies?

Historically, Drupal is known to be web standard compliant. It supported the RDF-based aggregation format known as RSS 1.0 as early as in 2001, which was later upgraded to RSS 2.0. The Drupal community prides itself in valid HTML code, not only for the code generated by Drupal, but also by taking the extra step of automatically fixing faulty HTML entered by its users. Drupal has been using XHTML since its version 4.0 in 2002. The next logical step beyond XHTML was to add a layer of semantics with the RDFa standard, a W3C recommendation published in 2008.

There are definitely many reasons that contributed to the addition of RDFa into Drupal 7. The first comes from the Drupal project lead, Dries Buytaert, who is passionate about the web and open source. Secondly, the growing Drupal community is very web savvy and includes many experts from different backgrounds in accessilibity, CSS, HTML, security etc. As a result, every release of Drupal includes many latest standards. The community meets twice a year at conferences (DrupalCons), thes events play a great role in hashing out what technologies or designs will be incorporated into the next version of Drupal. Because of the flexibility of its internal architecture, Drupal is able to keep up with the latest web standards. Content in Drupal is very structured and provides site administrators with a user interface to build the site structure they want, using entity types, content types, fields and taxonomies for categorization. When it comes to other CMSs, Joomla!’s community appears to be more fragmented with a core software that is not as extensible as Drupal and WordPress is more of a blogging platform, so turning it into a full blown CMS can be challenging. Both WordPress and Joomla! are in fact adapting the concept of Drupal’s Content Construction Kit (CCK) to their software but they have not yet reached the same level of maturity as Drupal.

A common objection to the adoption of Semantic Web technologies is that the learning curve is steep and that it is too complicated for many web developers to get into it. How can Drupal 7 change that? Which features accessible for the average web site operator will it offer?

Semantic Web technologies don’t have to be complicated when applied to simple use cases! We purposely chose only of a subset of semantic web technologies to integrate into the core of Drupal, keeping the learning curve for the Drupal developers and users as low as possible. The main technology is RDFa which includes the notions of vocabularies (a schema, or collection of attributes) as well as Compact URIs (CURIEs) which make the authoring of RDFa easier. In fact, some web developers might have come across these notions before when working with Dublin Core in the meta tags as such dc:title or dc:date.

Which benefits will web site owners get when they switch to a semantics enabled Drupal 7?

Google and Bing increasingly rely on machine-readable structured data from the websites that they crawl. The design of Drupal 7 embeds semantic meta data that makes machine-to-machine (M2M) search native for a Drupal 7 website. RDFa can add value by giving search engines more details such as the latitude and longitude of a venue for display on a map; or providing the ISO date format for localization and proper display in the search results for different countries.

What are your hopes regarding the development of other applications that either provide or consume data from D7 sites? Which improvements of standards, best practices or (lightweight) ontologies in the Semantic Web community would you like to see?

Services like Sig.ma are already able to collect semantic data from different sources and display it in new ways in the form of mash-ups. Eventually, these services that consume semantic data will not be just Drupal specific, as more platforms jump on the semantic web band wagon. What I hope to see as improvements or best practices in the future are more well-maintained vocabularies. Many of the existing vocabularies are over engineered, some fail to de-reference properly. Their is also some work to be done in order to improve the tooling made available to web developers as well as introducing the simple concepts of Linked Data to web developers via easy to read documentation.

Thank you for this interview, Stéphane!

Thomas Thurner

Report on developments at the European Semantic Technology Market

The present state of development, future trends and expected market scenarios for Semantic Technologies are shown in the just published “Demand driven Mapping Report”. The report is part of the EU-funded project Value It, which is about bringing together the various stakeholders within the sector: Industry, Research and Government. VALUE-IT preliminary findings show that the STE potential market in Europe will size up to €1.44B for 2014. Scanning furthermore the executive summary of the report, some findings attract attention:

The survey results also show considerable variation by sector, both of policy and technology implementation. With respect to technologies, ICT companies are also the most willing to consider semantic approaches. The ICT sector has an unusually high interest in all ST components, with 20% or more being willing to consider all of them, and over half of IT respondents looking at Web 2.0 (social computing). [...]  The use of tagging technologies – which overall is the least mature approach in the survey – is most advanced in Life Sciences. The Life Sciences, Media & Entertainment, and ICT sectors all have a reasonably strong interest in Natural Language Processing (roughly 25% on average). Ontologies and RDF/OWL are the technologies least often considered, though the interest in these Semantic Technologies is not insignificant. Taxonomies are slightly more popular, perhaps indicating that companies are taking the first step to prepare for a more semantic approach to IT solutions. The ICT, Energy & Utilities, and Media & Entertainment sectors all have a reasonably strong interest in using taxonomies.

The 190 pages report gives an actual overview of the status quo on European Semantic Technology Market and is now available for download: Final demand driven mapping Report

Jana Herwig

Read this: Linking Social Networks on the Web with FOAF

Jennifer Golbeck, Matthew Rothstein. Linking Social Networks on the Web with FOAF: A Semantic Web Case Study. Proceedings of the Twenty-Third Conference on Artificial Intelligence (AAAI’08).
Download (PDF, 320 KB).

ABSTRACT
One of the core goals of the Semantic Web is to store data in distributed locations, and use ontologies and reasoning to aggregate it. Social networking is a large movement on the web, and social networking data using the Friend of a Friend (FOAF) vocabulary makes up a significant portion of all data on the Semantic Web. Many traditional webbased social networks share their members’ information in FOAF format. While this is by far the largest source of FOAF online, there is no information about whether the social network models from each network overlap to create a larger unified social network model, or whether they are simply isolated components. In this paper, we present a study of the intersection of FOAF data found in many online social networks. Using the semantics of the FOAF ontology and applying Semantic Web reasoning techniques, we show that a significant percentage of profiles can be merged from
multiple networks. We present results on how this affects network structure and what it says about relationships and individual behavior. Finally, we discuss the implications this has for using web-based social networking data to create intelligent user interfaces and social software.

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