Tag Archives: Linked Data

Thomas Thurner

The hype, the hope and the LOD2: Sören Auer engaged in the next generation LOD

by , Category: Linked Data & Open Data, Tools & Software

The paneuropean Project LOD2 is one of the biggest projects dealing with linked data. Scientists, programmers and software architects in various european countries are working on the next generation of linked open data. In a series of interviews i’m presenting people working on and with LOD2. As a start, i had the change to talk to Sören Auer, head of the LOD2 project.

Thomas Thurner: Over the recent years the LOD movement gained tremendous momentum. As one of the key players in this area how do you perceive this development? Hype or hope?

Sören Auer: From my point of view the momentum LOD gained is deserved. We should strive for a Web, which is more decentralized, democratic, participatory, transparent and inclusive. Linked Open Data is from my point a key technological building block on this road. However, a lot of work is ahead of us. LOD has to find its way directly into mainstream technology such as CMSes, Search Engines, Web Applications, Mash-Ups and we have to show users and stakeholders the direct added-value of this technology.

Thomas Thurner: What is the current state of the LOD cloud from a technological point of view? Where do you see room for improvement?

Sören Auer: Currently, the technological state of LOD seems to be comparable to the early days of the Web. We are still able to draw maps/clouds of the LOD datasets and data links are still sparse and difficult to maintain. This reminds me a lot of the early days of the Web, where we also had problems with broken links (the infamous 404). Later, after content management systems and Web applications automatized the link generation and maintenance this improved a lot and I hope we are on the same road with LOD technologies finding its way into more and more Web systems.

Thomas Thurner: How is the LOD2 project addressing theses issues? What are the project’s key objectives?

Sören Auer: LOD2 is addressing in three ways: First, we develop new research approaches highly relevant for LOD, for example, for Linked Data management, automatic data linking as well as Linked Data enrichment andquality improvement. Second, we implement and integrate these approaches into specialized tools (e.g. SILK, OntoWiki, Virtuoso and DL-Learner) forming together the integrated LOD2 stack. The LOD2 stack can be used by data publishers for the whole life-cycle of Linked Data management ranging from extraction over linking, authoring, enrichment to exploration & search.

Thomas Thurner: What do you think are the most important factors to bring LOD to the masses?

Sören Auer: From my point of view the key factor here is that we manage to integrate the large number of tools and approaches for supporting the Linked Datalife-cycle stages in a synergistic way, where each aspect adds value and triggers a number of other improvements. For example, the establishing of a new data link has a direct effect on search & exploration of Linked Data. We have to directly show these kind of benefits to users so they receive and instant gratification for contributions to the Web of Data. Semantic Wikis, such as Semantic MediaWiki and OntoWiki, are already nicely working in this direction. An application with an enormous potential to bring LOD to the masses would be the creation of a distributed, social semantic network. With OpenId, WebId, FOAF, Semantic Pingback most of the building blocks are available, but the final step integrating these into an easy-to-use social networking application still has to be done.

Thomas Thurner: Compared to other semantic web approaches linked data principles seem to be rather easy to understand. On the other hand some argue that the “linked data cloud” is a big heap of data which cannot be used for professional purposes. What is your point of view?

Sören Auer: Of course the currently available data is not useful for all potential usage scenarios. However, already now Linked Data can be used for many interesting applications: For example, we just completed the development of a prototype for a large search engine, where users searching are assisted with comprehensive background information obtained from the Linked Data Web. For this use case, information available as Linked Data is already very valuable and useful. The criticism of LOD being a “heap of data” also reminds me a lot of the early days of the Web, where people raised similar criticisms for the Web being a medium of un-professionalism. Later it turned out that, of course there is a lot of amateurism, but as Wikipedia impressively demonstrates the working together of many amateurs with the right tools can in the end outperform few professionals.

Thomas Thurner: Linked Data could also become a new paradigm for light-weight enterprise data integration. What are the biggest obstacles today for linked data to being accepted by the business community?

Sören Auer: Using Linked Data for data integration in large enterprises has an enormous potential. Just last week I was invited for a workshop with the IT department of one of the top car makers and the people responsible there for data integration were extremely excited about the opportunities of Linked Data in the large heterogeneous enterprise with more than 3000 different backend systems. Linked Data technologies can easily fill the gap between unstructured Intranet search and expensive & complicated Service-oriented Architectures. Compared to SOA, Linked Data is a pay-as-you-go strategy, where data integration can be performed incementally and in sync with the requirements and evolution of the data structures in the enterprise. In order to realize this vision, we need to continue the maturation of enterprise Linked Data tools – the availability of PoolParty, Sindice Enterprise Edition, Virtuoso, TopBraid are already important steps in that direction.

Thomas Thurner: Automatic mechanisms to curate linked data and to make alignments between datasets possible play a crucial role for the next phase of linked data economics. Which technologies will play a central role? What will be the most critical point – do you see a “wisdom of the crowd” playing a role in this game?

Sören Auer: Definitely! Tapping the wisdom of the crowd for mapping & linking has a huge potential, which is currently unused. We started working in that direction with DBpedia Live and the DBpedia mapping Wiki. In order, to make it really easy for people to contribute we have to dramatically lower the barrier to contributing to the alignment process. In LOD2 we also plan to enable users to create mapping and links between dataset by simply giving examples of correct links and evaluating some automatically generated ones.

Thomas Thurner: At the moment governments all around the world start to publish open data, more and more stakeholders start to understand the benefit of open linked data. On the other hand enterprises haven´t even started with this topic. What could be the dynamics which will trigger projects in industry sectors like financial industries which will make use of open data principles?

Sören Auer: Making statistical and financial information available in structured form and as Linked Data could have a enormous impact in this regard. With the DataCube vocabulary effort a first step in this direction was made, but it would be nice if this vocabulary would get an official stamp of a standardization organization such as W3C. Since the benefit of publishing statistical and financial data in structured form, e.g. as Linked Data, is visible most when done by many, this could be also facilitated by government regulations and industry best-practices.

About INFAI

The Institute for Applied Computer Science (InfAI) at Universität Leipzig hosts research groups in service sciences, knowledge engineering and management as well as natural language processing. The approximately 20 researchers of the Agile Knowledge Engineering and Semantic Web (AKSW) research group at InfAI headed by Dr. Sören Auer are establishing theoretical results and scalable implementations for the field. Particular emphasis is given to areas such as ontology creation and
manipulation, knowledge extraction, ontology learning and information & data integration on the Semantic Data Web. The implemented tools and services (such as DBpedia, OntoWiki, DL-Learner and LinkedGeoData) developed by the group enjoy considerable popularity.

About Sören Auer

Dr. Sören Auer leads the research group Agile Knowledge Engineering and Semantic Web (AKSW) at Universität Leipzig. His research interests include semantic data web technologies, knowledge representation, engineering & management, usability, agile methodologies as well as databases and information systems. He aims to combine strong theoretical results with high-impact practical applications. Sören is author of over 50 peer-reviewed scientific publications resulting in a Hirsch index of 15. Sören is leading the large-scale integrated EU-FP7-ICT research project “LOD2 – Creating Knowledge out of Interlinked Data”. Sören is founder (respectively co-founder) of several high-impact research and community projects such as the Wikipedia semantification project DBpedia or the social Semantic Web toolkit OntoWiki. He is co-organiser of several workshops, programme chair of I-Semantics 2008, OKCON 2010, ESWC 2010 and ICWE 2011, area editor of the Semantic Web Journal, serves as an expert for industry, the European Commission, the W3C and is member of the advisory board of the Open Knowledge Foundation.

Thomas Schandl

Linked data based thesaurus management in collaborative settings

by , Category: Corporate Semantic Web, Enterprise 2.0, Knowledge Management, Linked Data & Open Data, Semantic Web Applications, Tools & Software

The creation and management of controlled vocabularies in companies often takes place in a distributed manner. Different departments in different branch offices often rather create their own vocabularies, than have one large central knowledge model, where everyone contributes.

How to model divergent views on one concept?

Such a central model is not only much harder to manage, but there is also the general problem that differerent departments like marketing, quality assurance, R&D, etc. will have divergent views on the model and its concepts. These different perspectives on one and the same concept are hard to unify in a single model.

Think of a company that sells mobile phones and wants to create a model of its line of products. It wants to utilize this model in the context of its online shop as well as in the context of its user support forum. While the structure of the model (i.e. the relationships between the products) might be very similar or the same in both contexts, there will be differences in which properties of the products are actually relevant in the respective contexts.

In the model of the marketing department there might be a concept for a “Phantastax StamiMaxx” cell phone with a definiton “The StamiMaxx has a powerful battery and is great for professionals who travel a lot”. They might relate it to manufacturer “ACME Corporation” and to several concepts representing different features like “Android OS”, “Multi-touch touchscreen”, etc.
The very same phone has different properties that are interesting from the Quality Assurance departement’s perspective. They might call it by a more specific name like “Phantastax i3000 StamiMaxx S”, have a different definition for it like “3G cell phone implementing the new WTF3000 protocol, …” and relate it to concepts representing known problems and their solutions.

Now they face the task to integrate these different models, as it is not desirable to use a bunch of isolated models within one company.

Support of collaborative work on distributed models

To support this kind of collaborative work on distributed knowledge models, we would like to link the concepts of the models, just as is we link documents in the World Wide Web. Fortunately the Simple Knowledge Organisation System (SKOS) offers mapping properties that can be used to define relationships between concepts from different knowledge models.

E.g. when we want to say that concept “Phantastax StamiMaxx” in the product line thesaurus refers to the same real world entity as concept “Phantastax i3000 StamiMaxx S” in the Quality Assurance thesaurus, then we can use skos:exactMatch to express that. If we want to express that the concepts are merly similar, skos:closeMatch could be used.

The other SKOS mapping properties express a hierarchical (narrowMatch, broadMatch) or an associative (relatedMatch) mapping relation between concepts from different concept schemes. With those we can say that my Samsung Galaxy concept has a skos:broadMatch “Smartphone” in the product line vocabulary and a skos:relatedMatch “ACME Corporation” in a controlled vocabulary about Tech companies.

Modularisation of knowledge models

In this way SKOS thesaurus management systems like PoolParty make it possible to modularise knowledge models, represent concepts in their different contexts and consequently enable collaborative work on those models: The marketing guy can work on his model with the concept properties focused on sales without disrupting the work of the quality assurance expert on her own thesaurus. Later one or both of them can create the skos:exactMatch link between the concepts that are the same, like seen in the “Exact Matching Concepts” box in screenshot of PoolParty below.

Enrich your knowledge: Get connected with the LOD Cloud

Going a step further the models could be connected to external knowledge, e.g. a source from the Linked Open Data (LOD) Cloud. Once we establish links to LOD hubs like DBpedia, we can import additional information for their concepts or use it to establish whether similar concepts from different models really refer to the same real world resource.

Thomas Thurner

Hjalmar Gislason: “What I call the emerging field of Data Market.”

by , Category: Linked Data & Open Data, Open Government Data, Search Engines, Tools & Software

Open Government Data, and Open Data provided by the corporate sector, stimulate an upcoming market segment: Commercial Open Data Services. The islandic StartUp datamarket.com is on of the emerging companies in this field. Thomas Thurner from Semantic Web Company had the chance to talk to Hjalmar Gislason, founder and CEO of datamarket.com.

Semantic Puzzle: What’s the business idea behind datamarket.com? Whom do you expect to pay for what?
Hjalmar Gislason: From the end-user perspective its easiest to describe datamarket.com as a search engine for statistical data, a “Google for statistics” if you will. Any data that is already available open and for free out there will still be open and free on DataMarket, just easier to find, use, compare and download from a single source. While the audience for a search engine for statistical content is obviously way smaller than for text content, a significant part of that audience is business users, looking for data for business reasons. This means that there are more direct and lucrative methods to monetize the usage than simply contextual ads – especially in reselling access to premium data. This is a market that already turns over billions of dollars annually, but is as far from any of the “2.0 world” as one could possibly imagine (think Bloomberg, Reuters, FactSet). We believe there is an opportunity to disrupt a part of their business with a freemium approach, and furthermore open up the data market by reaching a business audience outside the narrowly defined financial user base that these companies cater to. There is data out there – free and premium alike – that can help almost any business make better plans and decisions. Connecting people and businesses to the data that they need will release phenomenal value. Tapping into just a fraction of that will be a hugely successful business for those that get it right.

Semantic Puzzle: Can you tell me a bit about the technological framework behind datamarket.com? How is the content from third parties is feeded
into the system, and which APIs do you use? As you provide mainly XLS and CSV, have you thought, to provide data also als XML in future?
Hjalmar Gislason: The backend system is written in Python. We read data from the sources in various different formats, ranging from Excel files and even scraping of web pages to proprietary APIs and Web Services. The data is then stored in a normalized format in a Postgres database that we’re using in a pretty unique way to be able to efficiently store the billions of time series and fact values that the system will eventually hold (currently at around 100 million time series and 600 million fact values). The web site is also written in Python, using the Django framework, but also making use of a lot of javascript libraries (and a bunch of our own code) to allow for an exciting user experience. We’re currently using a Flash-based solution called amCharts for the charts, but have already taken some steps to replace that with our own solution that we’ve written on top of the excellent Protovis visualization library. While you are right that the export formats we provide for end users are XLS, CSV and images (for exporting the graphs), our REST-ful API actually supports XML and JSON formats as well. So we already provide data as XML.

Semantic Puzzle: As you for sure know Tim Berners-Lee’s 5-stars scheme for OGD-Providers. Where do you se your own service in this framework?
Hjalmar Gislason: Any fact value, time series and data set on DataMarket is “addressable” with a direct URL using our API. In that sense, all the data on DataMarket is four-star data according to Berners-Lee’s definition. In many cases we’re integrating to data that is only one or two star data, so just by integrating it into our system we’ve moved it a few notches up that ladder. In some cases we’ve even been helping organizations publishing data for the first time, taking the data from 0 to 4 stars in one go. We’ve been toying around with several ideas that would take – or enable users to take – the data all the way to 5-star status, but that’s still just on the drawing table.

Semantic Puzzle: You re-use a lot of Open Data comming from the Island Government. Is there also a state-owned Data Portal for Island, or is
your service a “commercial replacement” for such a public effort?
Hjalmar Gislason: There is no government-operated data portal in Iceland, and to my knowledge there are no plans for implementing one yet. Sadly there are several more pressing issues in terms of eGovernment here that take higher priority. We don’t see our efforts as a replacement for such a portal, but we have managed to fulfill a little part of that role when it comes to statistical data. We’ve also been really vocal about the benefits of open data and among other things been influential in launching an open data wiki - opingogn.net (Icelandic only) – that exmplains the concepts with examples and use cases and attempts to list in a directory listing as many sources of government data as possible. There is some movement, but as an open data enthusiast I’d really like to see things happening faster. As a matter of fact I think there are reasons for Iceland to be extra enthusiastic about open data to increase transparency and restore trust after the crash of the banks and the economic system in 2008.

Semantic Puzzle: A lot of commercial Open Data Services (Socrata, Factual, Google …) are evolving at the moment. What do you think, which development this market segment will face in the next month and years, and are you able to list your sight on the crucial factors for such business?
Hjalmar Gislason: I’ve been writing quite a lot up on the developments in this industry on our blog. One of the things I’ve written the most about is what I call the Emerging field of Data Market“. I define “data markets” as “Services that make it easy to find data from a range of secondary data sources, then consume or acquire the data in a usable – and often unified – format.” Many of these services are trying to create marketplaces for data, envisioning that data providers can offer their data sets for sale to data seekers. As there are several players in this space already, I believe we’ll see many of them try to differentiate themselves in 2011 by focusing on specific types of data. There are definitely opportunities in building specialized data markets for geospatial data, for statistics and for enormous scientific data sets – to name a few types – and each comes with their own challenges, target audiences and preferred approaches. In the spirit of doing one thing and doing it well, I think most of these projects will want to see success in one such segment of the market before generalizing – or consolidating.


The interviewee: Hjalmar is a successful entrepreneur, founder of three startups in the gaming, mobile and web sectors since 1996. Prior to launching DataMarket, Hjalmar worked on new media and business development for companies in the Skipti Group (owners of Iceland Telecom) after their acquisition of his search startup – Spurl. Hjalmar offers a mix of business, strategy and technical expertise.

Thomas Schandl

Drupal and the Semantic Web – Interview with Stéphane Corlosquet

by , Category: Linked Data & Open Data, Tools & Software

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!