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.

A common objection from critics of the semantic web is that regular users or webmasters won’t go to the trouble of marking up their content or whole web sites with RDF.
While it is obvious that nobody is going to decorate their web pages with hand-carved RDF triples, it is also apparent that a lot of the current web’s pages are generated by transforming information from relational databases to HTML pages, which are perfectly suited for human consumption, but which suffer from a big loss of machine-readable semantics.

As the information in the relational databases is highly structured and contains rich semantics, it is only natural to also use the already existing structured data to generate RDF representations of the same information.

Triplify is all about this approach of bootstrapping data for the semantic web. It does this for web applications which are built on PHP and MySQL.
Triplify consists of a lightweight PHP script and a configuration file. The latter is used to do the mapping of the columns of an application’s relational database to appropriate RDF classes and properties.

In many cases a site administrator who wants to export her site’s content as RDF, only has to save Triplify with a premade configuration file for her site’s application into the right folder, as for many popular applications like WordPress, Joomla! or phpBB all the work has already been done.
Once installed, Triplify can be used to generate a dump of the site’s complete RDF graph, or to generate Linked Data, as each of the site’s main concepts’ RDF graph is provided under its own URL, e.g. the semantic description of a user with the ID 123 can be accessed under http://yoursite.com/triplify/user/123.

If no configuration for an application exits, it is fairly easy to create one by yourself.
All one has to do is to look at the app’s database schema, find appropriate classes and properties from well known ontologies and create MySQL queries that grab the data from the relational database and map them to RDF classes or properties.
An example for a query that takes the data from a table describing the user of a CMS:
"SELECT id, name AS 'foaf:name', url AS 'foaf:homepage', short_description AS 'dc:abstract' FROM user_table",

Triplify’s creator Sören Auer kindly gave us the opportunity for an interview:

Triplify is very easy to configure for web developers. For which scenarios would you recommend to use Triplify, and in which situations other approaches of semantifying your data might be more suitable?

As you already mentioned Triplify was primarily developed for Web applications developed in PHP. These usually have a relatively small and simple set of tables. Triplify creates complete RDF exports, Linked Data or JSON, but does not include SPARQL endpoint functionality. When SPARQL is required you are better off with D2R Server or Virtuosos RDF views.

Triplify creates semantic representations of the data in relational databases. Do you think there would also be benefit in the inverse approach i. e. creating an application that parses triples and writes it to a relational DB according to a mapping file?

In certain scenarios this might make sense, but for the most cases I think the database schema has to be developed separately. Database schemata contain more storage and retrieval oriented information, such as for example about data indexing. Vocabularies and ontologies on the other hand represent information on a conceptually higher level and are more flexible with regard to evolution of the information structures than databases.

Are there plans for further development of Triplify?

Sure. We want to add SPARQL support and possibly port Triplify to other scripting languages such as Ruby and Python.

Thank you Sören, we will stay tuned about the news from your great application and look forward to the Triplification Challenge 2009!

Just like the overall organization of the project meeting was very good, so Sebastian Dietzold, Sebastian Hellmann, Michael Martin and Jörg Unbehauen did a real good job at putting the ideas behind key concepts of the semantic web across in several introductory SemWeb presentations. Their talks about various technologies from the semantic web stack like URIs, RDF and its serialisations, RDFS, SPARQL and some related tools were well suited to bring people who are relatively new to the semantic web up to speed. Links to the presentation slides can be found at the project page in the coming days.

Later Jens Lehmann outlined the new things OWL 2 brings, e. g. profiles, which are subsets of OWL 2 and which provide different degrees of expressivity and reasoning efficiency.

The last day started with Sören Auer’s presentation of their semantic wiki OpenResearch, a site where information on conferences, journals and scientists is pooled. OpenResearch is built with Semantic MediaWiki (SMW), just like our Social Semantic Web wiki.

While SMW is a very useful tool as it lowers the entry barriers for using semantic wikis, Sören also pointed out  that in comparison OntoWiki provides some important features that SMW doesn’t have:

• SMW doesn’t use SPARQL for its queries, but a less powerful custom query language, whereas OntoWiki has full SPARQL support.
• OntoWiki’s UI has many widgets that support the user when entering data or new properties on a page (e. g. there is an autocomplete feature for suggesting properties)
• With SMW changes to the wiki’s semantic structure often entail manual changes to many, many pages. With OntoWiki it is easy to e.g. change poperties at any time.

For the new version of OntoWiki Sören and his team use the Zend framework and develop the Erfurt API to store and access RDF data. The Erfurt API supports SPARQL, versioning, caching and RDF based authentification/access control. It abstracts different stores using the adapter pattern, so it can be used with Virtuoso and any other store which has an interface provided by Zend_Db (MySQL, Oracle, PostgreSQL, etc.) plus they are working on an interface for Redland. Find the slides for Philipp Frischmuth’s Erfurt API presentation here, the API documentation here and Norman Heino’s Zend & OntoWiki Application Framework presentation here.

Julian Jöris demonstrated how Selenium is used for acceptance testing. This is a very promising testing framework for web applications, where one can e.g. record interactions with different browsers and automatically run them as tests. Selenium has a Firefox extension to record macros and is integrated with PHPUnit.

Finally we had a very good discussion about our conX-OntoWiki integration use case and application ideas, so we left Leipzig with a pleasant anticipation of the coming co-operation in the project.

Another example: One of the biggest Open Source Magazines in Germany, t3n, has recently published its new magazine with many stories around the Semantic Web. Editor in chief, Jan Christe says: “We have constantly stumbled upon semantic web related stuff  when we scanned the news, so we decided to set a focus on this topic.”

The Semantic Web is tangible now – Christe says: “Applications like OpenCalais, Zemanta or Tagaroo show the end-users what´s really in for them.” And it is also nice to see, that the semantic web won´t be reduced down to “search” anymore: t3n´s new issue has also interesting articles about Linked Data, for instance Sören Auer´s “How to develop Semantic Web Applications”.

So, as a conclusion: Paul Miller´s waiting for the “Semantic Web in Business” (a great blog post!) has an end. It won´t be found in heavy books, rather in the open source community and sometimes in light-weight magazines.

Yes, we can!

DBpedia has become the largest RDF repository for encyclopaedic knowledge, extracting structured information from Wikipedia and making it available on the Web of Data. UMBEL, on the other hand, provides an OpenCYC-based, light-weight ontology structure for relating Web content and data to a standard set of subject concepts, with a number of 20,000 concepts currently reached. In the Linked Data Cloud, DBpedia and UMBEL map and cross-reference each other.

In practice this means that UMBEL provides classes to describe the concepts to which “things” are members. For instance, named entities from Wikipedia such as “John F. Kennedy” are mapped with subject concepts such as Leader, Person, Administrator and Graduate, with broader and equivalent classes in CYC and FOAF and broader subject concepts within UMBEL. A link is set to Wikipedia, as well as a ‘same as’ reference to DBpedia. A class structure enables faceted browsing and extraction, inferencing, and navigation and discovery for all datasets linked to that structure.

DBpedia, in turn, returns properties of ‘John J. Kennedy’ (e.g. abstracts in available Wikipedia languages, demographic information such as birth date and place, alma mater, predecessors and successors), and ‘same as’ references, e.g., to the JFK entry in Freebase (who recently released their RDF service) and the aforementioned page in UMBEL. Furthermore, DBpedia maps the URI with available RDF types, for instance foaf:person or yago:AssassinatedAmericanPoliticians and, once again, with UMBEL’s subject concepts Person, Administrator, Graduate and Leader.

Due to its reliance on Wikipedia, DBpedia does a great job at covering a bandwidth of knowledge as broad as the spectrum of the interest of people participating in Wikipedia; it’s within the area of named entities, i.e. entities such as persons, organizations, locations, which have a proper name, but are not necessarily and specifically part of a particular, acknowledged domain or discipline. UMBEL, on the other hand, has as its most apparent advantage its reliance on OpenCyc and with that the strong inferencing and logic capabilities of the CYC knowledge-base which are thus also brought to the Web of Data. DBpedia is a community project started by the University of Leipzig, Free University Berlin and OpenLink Software, while the open and free UMBEL is developed and hosted by Zitgist with support from, again, OpenLink Software.

Now, and in particular with the recent release of Zitgist’s web service endpoints and with the incorporation of UMBEL classes in DBpedia, questions arises as to the relationship of the two projects, and regarding the role of OpenLink Software in the further process. To draw a distinction:

One could say that DBpedia’s goal is to lower the barrier for web developers and end-users in the actual use of the semantic web, while UMBEL aims at bringing “order to the chaos” that is inherent to user-generated, collective knowledge.

Would you agree with this description – and is it a contradiction at all or the kind of dynamic the Semantic Web community has been waiting for?

Mike Bergman: Yes, I would agree with this description, though we have tried many others. For example, in various writings in the past, we have described UMBEL as a roadmap, or middleware, or a backbone, or a concept ontology, or an ‘infocline’, or a meta layer for metadata, and others. Today, what I tend to use, particularly in reference to DBpedia, is the TBox-ABox distinction in computer science and description logics. UMBEL is more of a class or structural and concept relationships schema — a TBox — while DBpedia is more of an an instance and entity layer with attributes — an ABox. I think they are pretty complementary…

Sören Auer: I very much agree with Mike, but would like to add that Wikipedia authors do not have in mind to create a coherent and consistent knowledge base when working on Wikipedia. I think the more we demonstrate the benefits of the semantic representations in DBpedia to the Wikipedia community, these people will start to organize and rearrange content to enable the use of Wikipedia as a knowledge base. Right now, Wikipedia authors just have not yet been confronted with the problem of synonymous infobox properties or the uncleanliness of the category system, for example. I think with a few small and non-invasive changes to Wikipedia, much of the current chaos can be already resolved.

Mike Bergman: I agree, too, with Sören’s adder: I think it is difficult for Wikipedia authors to be consistent or coherent across the entire Wikipedia knowledge base. I think, then, the real question is where does that coherence or structural consistency come from? I think the nature of that task is quite different than creating or editing instance articles.

As for the dynamics and drivers of the community, the role of DBpedia for practical, linked data can not be overstressed. It was the first, remains the biggest, and has brought much visibility and awareness to linked data. I think I was one of the first to give DBpedia some press shortly after its release nearly two years ago, which is still one of my most popular blog posts. I’d like to think that UMBEL is now timely and well-positioned to help provide a complementary resource of concept classes, but that is not proven. DBpedia is.

DBpedia relies on user-generated content, UMBEL, with CYC, is expert-driven. How will a system that combines these divergent approaches continue to grow?

Mike Bergman: DBpedia is in the enviable position of being able to leverage two things: 1) the phenomenal success and growth of its source content, Wikipedia; and 2) the growing sophistication of information and structure extraction techniques built around that phenomenon. In fact, my most recent SWEETpedia listing of research projects based on Wikipedia now exceeds 170 or something projects and we discover and learn about more daily. We now see major research efforts in Germany, Austria, Japan, New Zealand, the US and England (among those I know) aggressively mining and learning from Wikipedia. I’m sure DBpedia will learn more, but others will increasingly contribute as well. This is unprecedented and very exciting. Wikipedia may have as strong a heritage in contributing to research and language and structure understanding as it does as a reference encyclopedia.

But, that is largely instance and attribute data, the ABox to use my earlier terminology. For structural and conceptual relationships — the coherent way to organize and relate things in the world; that is, the TBox — I think there is much subtlety and thinking required. I’ve used the phrase before that creating structural schema is “not like flinging hash”. Efforts such as Cyc, with nearly 1,000 person years of consistent testing and effort behind it, or perhaps others such as SUMO or what is coming out of the biology community with OBO (Open Biomedical Ontologies), offer better coherency and the ability to interoperate across diverse datasets and domains. Perhaps Wikipedia and its data extraction offshoots may someday get to this point — and I truly hope so — but are not anywhere near that today in my opinion.

Sören Auer: I’m actually not so concerned about the lack of structure and coherency as Mike. If we look at the current mostly textual Web and the search engines making it accessible to us humans, there is almost no data, few structure and even less coherency, but search engines still manage to provide an enormous added-value. If we add more data to the Webm much more sophisticated browsing, searching and data integration interfaces can be built. Structure and coherency will then emerge automatically, once people see how their content is indexed and can be easily found (or not).

The same happened by the way with the traditional Web 1.0 – in the beginning nobody used HTML’s meta-data tags. Once search engines started to interpret those for ranking results, meta-tags shifted to the center of attention of every Web content manager. Applied to the Semantic Data Web: once search engines understand foaf:person, everybody will use this concept for describing people.

A little experiment – Web 3.0, Semantic Web, Web of Data, Linked Data: Can you think of an ontology that is able to connect these terms and reveal the concepts behind them?

Mike Bergman: Well, I’d like to think UMBEL is that ontology (smile). That is certainly our intent, though truthfully we are still working out structural details and have not added all of this nice SemWeb terminology. But it is coming shortly (smile).

Sören Auer: A precise definition of these terms in the mathematical sense does not (and probably will never) exist, so articles such as those in Wikipedia (or many other publications) about the terms are from my point of view completely sufficient to reveal the concepts behind them to us. Of course it’s nice to have pretty and world-wide unique identifiers (such as provided by UMBEL) to annotate articles about these terms.

Mike Bergman: Well, UMBEL is about linking to concepts, though we welcome anyone thinking our identifiers are pretty (smile). One key aspect we will see moving forward is how we can translate those concepts into one of the 250 languages now used by Wikipedia while retaining existing structure. That is a real exciting prospect.

What are the concepts you would personally want to employ to explain the over-arching idea of these terms to a newbie?

Mike Bergman: Structured data on the Web is becoming like newly visible stars as nighttime darkens in the desert. Structured data are points of light in a global information space. We need fixed reference points in that sky to find specific stars. We need linkages to extract meanings and constellations from them. So, we both need to expose those stars — as linked data — and to provide fixed references to find them again and connections to draw meaning from them. Objects, references and connections all work in concert to expose the wonder.

Sören Auer: Its difficult for me to add something after Mike’s truly poetic description of pretty technical terms (smile). I see Linked Data or the Web of Data as the next milestone on the road of realizing the vision of the Semantic Web. In this regard, I’m Marxist (smile) and think Marx’s Law of transformation of Quantity into Quality applies: once we have a sufficient quantity of data out there on the Web, a new quality will emerge. Unfortunately, we are still far away from reaching a critical mass, since the Semantic or Data Web as we recently found out (cf.: Triplify – Lightweight Linked Data Publication from Relational Databases, PDF, 332 KB) is effectively shrinking if compared with the growth of the traditional Web.

Kingsley Idehen from OpenLink Software was the first to announce on the W3C mailing list that DBpedia & UMBEL are now “fully connected.” Is Kingsley the bridge-builder between the two projects?

Mike Bergman: Without question. Kingsley has backed both efforts in a big way with people and resources. His company actually did the first RDF linkage between the two projects. What is more remarkable, however, is that DBpedia and UMBEL are but a small slice of the things Kingsley has been backing. He has been a leader in middleware, scalable clusters and cloud computing, RDFizing all data forms, converting relational legacy data to linked RDF data, and providing demos and teaching to newbies on mailing lists. I’m glad you asked this question because Kingsley is a real catalyst and visionary.

Sören Auer: I agree, Kingsley is a mover and shaker in many areas of technology and innovation and in particular the Data Web. However, we should not forget his marvelous team at OpenLink with the database mastermind Orri Erling, Hugh Williams, Ivan Mikhailov, Yrjäna Rankka and all the others.

In the same vein: What are roles that are vital for the LOD-engineering process? Are there also “gardeners” or “bureaucrats“, as Wikipedians would put it?

Mike Bergman: I think the LOD (linking open data) and DBpedia mailing lists have been very effective, and there continues to be good community and organization around those efforts. We know that Wikipedia is not a free for all, with a kind of self-policing plus type of governance. I think that works well at the instance level.

However, structure decisions at the level of conceptual schema such as UMBEL or OpenCyc or even the mapping of classes between ontologies or datasets requires more skill and care. Others may not agree, but I think the schema aspects essential to UMBEL’s purpose — while definitely needing to be open and participatory at the suggestion or input level — possibly require roles more like “priest” or “professional” or “authority” at the actual roll-out level. Without quality, structure is nothing, and all of this is just an elaborate toy.

Sören Auer: Again my philosophy here differs a little from Mike’s: I’m pretty skeptic there will be one ontology or organization scheme for the Semantic Web. Rather, I think structure and homogeneity will be achieved on a peer-to-peer basis first and a community consensus will emerge later.

Mike Bergman: Yeah, this is an excellent point and I’m glad Sören raised it. While it is true we have put much effort into creating a lightweight structure of concept classes for linking disparate datasets, we too do not see “one ontology to rule them all.” I suspect there will often be none, and then many times other frameworks chosen. It really depends on the use case and purpose. UMBEL’s specific purpose is to provide a coherent framework for serious knowledge engineers looking to federate data. After that, other frameworks with a different purposes may then need to do the heavy lifting of actual data interoperability.

With its recent RDF service release, Freebase has risen to the level of a major SemWeb knowledge base, too. Where do you see its role in the future SemWeb ecology, also in relation to your own projects?

Sören Auer: To be honest, I was not very convinced of Freebase from the very beginning although their technology in particular the user interfaces are impressive. From my point of view the Freebase approach was too centralized and proprietary. A better strategy would have been to develop an open (source) technology, which people can deploy on their own Websites combined with server side crawling and search facilities. The first part of this equation is by the way exactly what we are aiming for with our OntoWiki and Triplify projects. However, if Freebase now moves towards more openness and interoperability, this can be only applauded.

Mike Bergman: I think early on that Freebase was needing to get its own feet set and did not do much with regard to open standards or external interoperability. These are good signs we are now seeing. However, I think the revenue model around user-supplied data remains highly suspect. Heck, Wikipedia with all of its tremendous success is daily soliciting contributions from users. It’s hard to get traction without being open and free, and its hard to make money when you are open and free even with traction. But these new announcements now make it much easier for us to use Freebase should our customers request it.

About DBpedia:

DBpedia is a community effort to extract structured information from Wikipedia and to make this information available on the Web. Wikipedia is the by far largest publicly available encyclopedia on the Web. Wikipedia editions are available in over 250 languages with the English one accounting for more than 2.49 million articles. Unfortunately, Wikipedia’s search capabilities are limited to full-text search, which allows very limited access to this valuable knowledge-base. Semantic Web technologies enable expressive queries against structured and interlinked information on the Web. DBpedia allows you to make sophisticated queries against Wikipedia, and to link other data sets on the Web to Wikipedia data. The DBpedia data set currently provides information about more than 2.49 million “things”, including at least 108,000 persons, 392,000 places, 57,000 music albums, and 36,000 films. Altogether, the DBpedia data set consists of 218 million pieces of information (RDF triples).

dbpedia.org (general website)

wiki.dbpedia.org/OnlineAccess (DBpedia Wiki – Online Access)

About UMBEL:

UMBEL (Upper Mapping and Binding Exchange Layer) is a lightweight ontology structure for relating Web content and data to a standard set of 20,000 subject concepts. Its purpose is to provide a fixed set of reference points in a global knowledge space. These subject concepts have defined relationships between them, and can act as binding or attachment points for any Web content or data. UMBEL is like a map of an interstate highway system, a set of roadsigns to help find related content and a way of getting from one big place to another. Once in the right vicinity, other maps (or ontologies) — more akin to detailed street maps — are then necessary to get to specific locations or street addresses. By definition, these more fine-grained maps are beyond UMBEL’s scope. But UMBEL can help provide the context for placing such detailed maps in relation to one another and in relation to the Big Picture of what related content is about.

umbel.org (project website)

umbel.zitgist.com (UMBEL webservice – sandbox)

The interview was led by Andreas Blumauer, SWC

Sören Auer described the difference between the Web 1.0, 2.0 and 3.0 as follows: On the Web 1.0, you had many websites that provided unstructured, mainly textual content. On the Web 2.0, you have a few large websites that are specialised on specific content types. And, finally, on the Web 3.0, there are many websites which contain, and are able to semantically syndicate, arbitrarily structured content.

So why would we need another web? What you cannot do with the current web is finding answers to seemingly complex, yet in reality pretty mundane question such as: Where in Leipzig do I find an apartment that is close to bilingual, German-French child care facilities? Are there any ERP service providers which have offices in Vienna and Berlin? Who are the researchers in South-East Asia currently working on database related topics?

Sören further discussed three of the present means of bringing relation data to the web: Triplify (a web application plugin that exposes data from relational databases in RDF), D2RQ (a declarative language to describe mappings between relational database schemata and OWL/RDFS ontologies, developed at Free University Berlin), and Virtuoso Universal Server (a middleware and database engine hybrid delivering for instance data integration for SQL, RDF, XML, Web Services). With respect to Triplify, Sören – who is Triplify’s founder and main developer at AKSW Uni Leipzig – showed and discussed the configuration for WordPress 2.1., which can be found here (click here for more configurations, e.g. for Joomla, OpenConf and Drupal). The next aim for Triplify is to become an integral part in enduser web app distibutions.

And important question raised by Sören was: How do next generation search engines know that something has changed on the web of data? He suggested three approaches:

1. Always try to crawl everything (this may sound silly – but that’s actually what is happening on the current web)
2. Ping a central update notification service – e.g. PingTheSemanticWeb.com – which works as a showcase, but will probably not scale if the data web gets really deployed.
3. Each linked data endpoint publishes an update log – e.g. with Triplify, as a special folder inside the Triplify namespace, e.g. http://example.com/Triplify/update

Also discussed by Sören and worth checking out is Reuters’ Semantic proxy – the demo went live in late September.

Orri Erling, as the lead developer of the Virtuoso Team, addressed the issue of mapping relational databases to RDF with OpenLink Virtuoso. In his talk, he addressed the pros and cons of RDF data warehouse:

Pros

• Even query performance across all data
• Possibility of forward-chaining inference
• Some SPARQL features may be better supported, e.g. Unspecified predicates

Cons

• Keeping data up-to-date
• Complex set up, needs dedicated servers: you don’t build them on a whim

What Virtuoso delivers is mapping of SPARQL to SQL against any existing schema (whether stored in Virtuoso or elsewhere); a physical quad-store (quad as in quadruple; not as in quad-bike ; and Federated/local Relational Data Base Management Systems (RDBMS).

A more detailed discussion of the requirements for Relational-to-RDF Mapping is available on Orri’s blog, where he discusses it in the light of his own experience. A power point presentation of a previous talk he gave to the W3C RDB2RDF Incubator Group can be downloaded here: Mapping Relational Databases to RDF with OpenLink Virtuoso (PPT, 115KB). His summary of the group discussions around the same topic, Requirements for Relational to RDF Mapping, can be found here.

Orri also showed the Virtuoso billion triples demo which, according to the corresponding blogpost, “is being worked on at the time of submission and may be shown online by appointment.” The demo was a submission to the Billion Triples Challenge.

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