Semantic Web Company

Jamie Taylor, Metaweb

Andreas Blumauer from Semantic Web Company (SWC) talked with Jamie Taylor, Minister of Information at Metaweb Technologies Inc. about Freebase & Linked Data and Google´s announcement to use RDFa.

SWC: At ISWC 2008 Freebase became “officially” part of the LOD Cloud. What exactly has changed since that time?

Jamie: Since Freebase is a community writable semantic database, the addition of the RDF interface allows anyone to publish data into the LOD cloud. LOD Applications can access any Freebase Topic through the RDF interface by constructing a URI from the Freebase identifier.  But perhaps more importantly, because entities in Freebase can be annotated with multiple identifiers, Freebase Topics can be retrieved by constructed URIs using the identifiers used by other systems and data sets.
For instance, the movie Blade Runner can be referred to as http://rdf.freebase.com/ns/en.blade_runner, but it can also be referenced as http://rdf.freebase.com/ns/authority.netflix.movie.70053131 using the Netflix identifier, http://rdf.freebase.com/ns/authority.imdb.title.tt0083658 using the IMDB identifier, or as http://rdf.freebase.com/ns/wikipedia.en.Dangerous_Days using a Wikipedia wikiword (which in this case is a Wikipedia redirect to the wikiword Blade_Runner).
Freebase also provides a user maintained mapping of how these identifiers can be used to address resources in other LOD systems. The sameas.freebase.com schema can tell an LOD user that the Freebase Blade Runner Topic can also be found in DBpedia using Wikipedia identifiers or how musical artists can be found at the BBC using Musicbrainz identifiers.  In fact, the Freebase RDF interface uses the sameas.freebase.com schema to create the owl:sameAs links in the RDF output allowing the user community to expand the interconnections between Freebase and the LOD Cloud.
Linked Data providers are also using the strong identifiers in Freebase to identify entities such as companies and locations in their own data sets.  When they find an entity that is not represented in Freebase, they simply add the entity to Freebase and use the newly minted Freebase identifier.  This permits anyone using their data to understand how their entities relates to any of the more than 5 million things interconnected within Freebase.

The RDF interface can also be used to reference the Freebase type system, giving LOD data set providers vocabularies across a wide range of subject areas.  And because anyone can expand Freebase’s data model, data providers can use our schema development tools to build and extend these vocabularies to suite their needs.
Freebase was not designed for ephemeral or fast changing data, like weather conditions or stock ticks.  But this type of information is well suited for publication as Linked Data.  Freebase entities representing a location or company can be annotated with references to LOD services that provide these types of volatile data.  Similarly, Linked Data provides a great way to disseminate very fined grained information that might be associated with a scientific study or financial report.  Linked Data provides a seemless transition from Freebase, where a user (or application) can run a query with constraints that run across a wide range of types to find entities of interest along with the LOD services that provide access to temporal or high resolution data not available in Freebase.
We recently demonstrated MQL Extensions which allows the Metaweb Query Language to use data from other systems as a part of the query constraint and result set.  While MQL Extensions are user extensible and work with a wide array of systems,  this capability makes the connection between Freebase and the LOD Cloud even more transparent.
For example, because US companies that are registered with the SEC are annotated CIK code in Freebase and the sameas.freebase.com schema indicates that the CIK annotation can be used to create a URI that is dereferencable at rdfabout.com, it is possible to write a MQL query that asks who is on the board of financial services companies that trade on NASDAQ and are  headquartered in California (and using another MQL Extension, you can ask for their stock price as well!)

SWC: Many organisations are very interested in Linking Open Data now but they are still not sure if they can benefit from publishing data on the web – what´s your experience so far?

Jamie: Linked Open Data provides a simple, standard way for organizations to distribute structured data.  For most organizations, providing access to data is another important outlet to announce the availability of higher value services.  For organizations involved in building or selling physical goods, the bits representing what they provide are not the goods themselves, but a way of attracting potential customers.  Making catalogs and specification sheets available in electronic form, so other applications can connect buyers to their physical goods is simply an effective marketing system.  Even for firms involved in electronic services, providing access to open structured data is generally a lead-in to value added services.  For instance, if I ran a service collecting hard-to-find information about manufacturing relationships between medium sized businesses, I would publish open company profiles covering things like market size, industry, location for the medium-sized businesses I tracked, so potential users the premium data would know I had the coverage they were looking for.

SWC: Just recently Google has announced to use RDFa to enhance their search results. What do you think?

Jamie: We are excited about Google’s announcement. Yahoo’s use of RDFa for Search Monkey and Google’s announcement gives RDFa users tangible benefits. The Search Monkey team was very quick to realize that because users can create data models in Freebase, and because the elements of those models all have strong RDF identifiers, Freebase provides a large and user extensible vocabulary for RDF/RDFa (see the list of vocabularies). When a user wants to create a Search Monkey application that works with their film review site, they need not invent a new vocabulary (that will probably be used only once),  they can use the Freebase Film Domain vocabulary which supports over 63,000 instances in Freebase alone.
Similarly, with over 5 Million well described Topics in Freebase and over 14,000,000 Named Objects (Topics, images, musical tracks and documents) when a user wants to unambiguously identify a subject or object in RDF/RDFa, Freebase has an extremely large collection of identifiers to draw from.  These cover people, places, companies, movies, music, books and wide variety of other subjects.  If Freebase doesn’t have the entity the user is looking for, they can of course add it themselves and make use of the identifier immediately. I think this is why Google used some Freebase identifiers in their examples. We hope that with Yahoo and Google’s support for RDFa the web will become a strongly annotated source of data which can support a wide range of user applications.

SWC: Thank you, Jamie!

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Since SWC is involved in a couple of semantic web projects in the media industry, I was watching for the BBC Music relaunch. Now the new platform is online – and from an enduser’s perspective the new system offers comfortable ways to navigate through the world of music: Bands, their members, biographies and outgoing links like to Wikipedia or MySpace are retrieved from MusicBrainz and mashed up with BBC blogs, playlists or reviews.

Matthew Shorter, interactive editor for music at the BBC, told silicon.com:

We’re kind of on a journey of moving from what’s effectively a magazine/print publication-based metaphor around web publishing…to a world where we recognise that that’s not the way that people use the web.

No doubt: Linked Data is a great deal for the end-users but what´s in for the providers, in this case for BBC?

From a media company’s perspective Shorter has mentioned a handful of interesting arguments why linked data could be useful:

1. reusing data from MusicBrainz and Wikipedia also provides better value for the licence payer as the BBC isn’t wasting resources reproducing data already in the public domain
2. from an SEO point of view, once we start generating a lot of meaningful links among our pages, then we’re going to improve the find-ability of our content via web search
3. by having as open a platform as we can, then our hope at least is that people will pick up that content and do things with it and we’ll benefit from incoming links as a result

This could be summarised as follows (by adding a fourth item):

1. re-use existing data
2. increase find-ability
3. extend your eco-system
4. understand users’ interests

By saying that linked data can help providers to understand their users in a more profound way which is based on the more granular way how information is offered in the linked data world (paradigm shift: page versus linked data) I´d like to ask a short, value-free question: Which side of the internet will drive the business in the future – the visible web or the deep web? Was linked data designed only for the visible web?

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Sure, that´s not the end of the flagpole – but “a little semantics goes a long way” (Jim Hendler): With two Firefox add-ons, you can pimp your Google and you will get (1) a better overview over the search results, (2) kind of a moderated search and (3) information from Wikipedia along with the results.

Install Cloudlet and Googlepedia (Don´t forget to donate!) and you will see something like this:

Sure, both “mashups” are not based on RDF, and the “TagCloud” is not as accurate as we wished, but let us be patient again. At least this picture makes end-users yearning for a bit more semantics (which goes a long way…) on top of the usual lists of search results.

Semantic MediaWiki is being featured in issue 12/2008 of the German popular computer magazine iX in an article about wiki engines. It’s the only semantic wiki among those presented, and although it is an extension of MediaWiki (which underlies Wikipedia) – which is also in the article – it is discussed separately and thus receives quite some emphasis in the article. iX has featured Semantic MediaWiki before, more precisely in an article dedicated to it in 11/2007. It’s well-deserved, I think, considering the many sites which use Semantic MediaWiki.

It’s good to see that the visibility of Semantic Web is also growing outside academia and involved industry.

Author: Pascal Hitzler

The Linked Open Data infrastructure is in a tremendous process of maturing – the recent release of UMBEL’s webservice AND the incorporation of UMBEL classes in DBpedia are yet another confirmation of this exciting process. Knowing and having met DBpedia co-initiator, Triplify main developer and head of the AKSW research group Sören Auer and UMBEL editor and Zitgist CEO Mike Bergman in various contexts, I felt it was time to talk to and pick the brains of both these key players in a dialog situation. The (first) result is the interview you can find below. As not everyone can expected to be familiar with both projects, here is some backgrond to get you started (you can also go directly to the interview):

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…
(more…)

This morning’s first session was dedicated to Using the Web of Data, or, as Alan Dix put it: “In the end, it’s not about data – it’s about use!” Alan and Richard Cyganiak were the keynoters for this session.

Alan Dix is a Professor at the Computing Department of Lancaster University, and author (with Janet Finlay, Gregory Abowd, and Russel Beale) of Human-Computer Interaction.

To start with, Alan pointed to the two sides of achieving the web of data: Firstly generating the web of data (a billion triples, as mighty as this may sound, is actually tiny, says Alan) and then, secondly, accessing the web of data.

With regard to generating the Web of Data, Alan distinguished between top down and bottom up approaches, counting to the former the creation of the web of data from legacy sources (i.e. where you take existing data and semantically lift them, e.g. from structured data) or web scraping such as DBpedia’s extraction of data from Wikipedia.

N.B.: This notion of ‘top-down’ does not imply a hierarchical relationship, but rather means that there is already a plan for what is going to be put on the web of data (e.g. ‘all semi-structured information on Wikipedia’ or ‘dataset XY from project Z’). The bottom-up idea here implies that data is added as the result of an action, or interaction, as the user/s go, e.g. relationships are created as the user expands his or her social network. For instance on Amazon, user interaction is used to generate semantics: People do not tell Amazon what they like, they simply buy it.

Having relationships of course does not imply yet that these relationships are part of the Semantic Web. Or, as Alan put it, “why should I be RDFizing my online presence if none of my friends are?”

Please take a look at the PDF of the Alan’s slides (2,4 MB) – what I cannot reproduce here is a chart he developed, which was very useful for describing current scenarios on the web and which posed a twofold question:

Does a website/platform have the web of data implemented? YES/NO
Is the web of data on ta website/platform apparent to the user? YES/NO

The possible combinations (YES/YES, YES/NO, NO/YES, NO/NO) provide a good heuristic tool for describing what is currently available, with and without the Semantic Web. Take, for instance, the shiny interface of Talis’ Project Cenote: Cenote’s vision is to “make library data visible in many contexts, inside and outside of the library, making the data much more accessible and visible to a wider audience – benefiting current and potential users of library services wherever they are.” On Cenote, the user doesn’t see that it’s got the Web of Dat in it – it is actually implemented, but not in a way that is apparent to the user.

On the other end of the spectrum, you have a platform like Facebook: Alan referred to Facebook as “the user’s own web of data”, i.e. web of relationships: The user is aware of these relationships (they actually shape his interaction and communication with the site), and the (numerous!) apps on Facebook continually add relationships, but, regrettably, insulated from one another and not using RDF (and don’t you try to take data out of Facebook!).

Two examples of public data that Alan cited and that grow as people/institutions add data do them are Freebase (the “open database of the world’s information” – see previous posts on this blog about Freebase) and Swivel. Swivel allows people, institutions, anyone to upload and explore data, also featuring official data sources such as (links go to their Swivel pages): New York Federal Reserve Bank, UNESCO Institute for Statistics, DukeResearch or EUROSTAT. According to Alan, there is already more data on Swivel now than in the whole Linked Data cloud.

Alan also mentioned the Social Graph API – o yesterday evening Luca Hammer (one of the web 2.0 people who had joined the Open Hacking Session) introduced me to the Wordpress Plugin “Meet your commenters” – Meet you commenters uses Social Graph to find social relations on the web, and adds these data to the commenter profiles it creates in Wordpress.

Image via WikipediaOn a different note: I took sometime today to explore Alan’s homepage and found the cute Christmas Cracker’s application which was first developed in 1999 and which is now also available on Facebook. As trivial as it may sound at first – sending virtual Christmas Crackers (with more than 5000 possible combinations!) is a good showcase for developing Human Interaction Scenarios, and a number of papers have been written about the application. Here is the casestudy which Alan recommends to begin with: Designing experience – virtual Christmas Crackers.

The abstract and a list of links to all websites and demos Alan discussed can be found here. Full reference: A. Dix and R. Cyganiak (2008). Using the Web of Data. Keynote at WOD-PD 2008 | Web of Data Practitioners Days, Vienna, Austria – Oct 22-23, 2008. http://www.hcibook.com/alan/papers/WOD-PD-2008/

Even if you have not met Richard Cyganiak in person, you have certainly come across one of his creations: The Linked Data Cloud. Richard is a research assistant at DERI Galway. In his demo, he gave us the opportunity to gain hands on experience, introducing a tool he dubbed Snorql, which is basically an easier to use version of a SPARQL-endpoint, as it already has the required prefixes ‘pre-installed’:

Using the Snorql interface, we could explore the dataset we had created collaboratively during Keith Alexander and Yves Raimond’s session. Writing SPARQL queries manually can be a challenge, but is next to impossible if you (like me) don’t know the syntax. But today we could just copy and paste all the queries from a website Richard had put up prior to his session – thanks a lot for the excellent preparation and demonstration!

Richard also showed a couple of RDF browsers in action, e.g. the Tabulator Plugin (“a Firefox extension which allows Firefox to handle data as well as documents”), or the Marbles Linked Data browser which is running right on beckr.org/marbles; enter, for instance http://api.talis.com/stores/wod-pd-sandbox/items/People/JanaHerwig (learn more about Marbles here).

Thank you, Alan and Richard – the combination of talk and demo was indeed a perfect intro towards using the Web of Data.

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In recent debates within the KiWi – Knowledge in a Wiki project, the need arose to further refine and find a common understanding of the type of knowledge that is (ideally) managed and processed using (semantic) wikis. One of the proposals evolved around a conceptualization of knowledge put forward by Gabi Reinmann-Rothmeier, also dubbed the “Munich Modell” (Münchner Modell).

In the Munich Modell, knowledge comes in three states of matter: solid (like ice), liquid (like water) and gas (like water vapor).

“Frozen” knowledge is knowledge in its most tangible, manageable form, for instance the type of verified, expert-endorsed information you would find in an encyclopedia like the Encylopedia Britannica.

“Gaseous” knowledge, on the other hand, is knowledge in its least consolidated form: think for instance of the type of heated debate you might have with folks in a pub, which is arguably the least structured, most uncontrollable, but also the most engaging type of knowledge!

And the “liquid” form of knowledge, eventually, is the common knowledge of day-to-day-life. It’s probably fair to say that it becomes obvious mostly when in the process of changing its state of matter: When it is calibrated against “frozen” or informational knowledge or when it is debated, becomes “gaseous” knowledge that informs action. (If you’d like to know more about the Munich model and are able to read German, you might want to download the original article here – PDF, 365 KB).

When talking about knowledge that is managed, used or, respectively, that evolves online, I think it also makes sense to pay some attention to the type of community that is preferred by particular online tools or environments. The particular flavour of knowledge, in this sense, is simultaneously characterized and shaped by the state of matter of knowledge and the form of the community that applies.

N.B. The following is not an immediate translation of the “Munich model”, but rather a reconceptualization which tries to also consider that different community models (and their implementation through IT) also play a role for the whole spectrum of knowledge management on and with the web (e.g. for online communication and interaction, online publishing and documentation and maintenance of web infrastructures).

Web-Flavour 1: The Blogosphere – gas, gas, gas!

Hmm… sniff it! This is the flavour I like best because it is my flavour. On the blogosphere (and twittersphere), knowledge is exchanged, developed further and evolves almost like in a pub debate… (more…)

Just got in touch with Vuk Miličić from Faviki recently – Faviki has been selected as a featured project on Google code, and in that context, Vuk describes the process of how Faviki retrieves its suggestions in a little more detail. It’s really interesting! It also sheds more light on the way that DBpedia is used in Faviki: Not immediately for the retrieval of tags, but for the translation of tags – long live the smartness of linked data!

1. Faviki fetches a web page and extracts a core text (without HTML and non-relevant content).
2. Then it tries to figure out if a content is in English. If it isn’t, it is sent to Google language API, which detects the original language automatically, translates it into English and returns the translation.
3. The content is then sent to and analyzed by Zemanta API, which then finds relevant links. Faviki uses links from English Wikipedia – titles are used as semantic tags.
4. If users language is not English, we must translate them. Using DBpedia datasets “Links to Wikipedia Article” , we can find names of Wikipedia’s titles in one of 13 languages. These datasets actually contain the connections between English Wikipedia articles and articles from Wikipedia in other languages.
5. Finally, suggested tags are offered to a user.

Read the whole blog post on Vuk’s Faviki blog

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An implementation of Semantic MediaWiki in public administration reiceved a jury award yesterday in the final ceremony of the highly coveted multimedia state award (Staatspreis Multimedia) 2008 in Vienna: Centre for Public Administration KDZ’s platform for the cooperation of administrations (Plattform Verwaltungskooperation) in Austria, Germany, Italy and Switzerland received praise for its use of open, semantic technologies in their effort to further the collaboration between administrations and administrative staff. Those of you who can read German: read the response from Bernhard Krabina, KDZ, here or contact him here, if you’d like to learn more. The top state award itself went to HPC Dual, a combination of electronic and physical mail delivery.

Also published yesterday was an interview with Matthias Schindler, former member of board of Wikimedia Germany, at the occasion of the publication of a physical Wikipedia, i.e. a one-volume encyclopedia in print (publisher: Wissen Media, a Bertelsmann division). According to the English Wikipedia, “the volume is planned to include abbreviated entries for the 50,000 most commonly used search terms of the prior two years. The book is to be priced at 19.95 euros, with one euro from every sale going to the German chapter of the Wikimedia Foundation.”

The interviewers also asked Schindler for his “encyclopedic Wikipedia dream” – I hope his response will catch on in the Wikimedia chapters worldwide:

I would one day like to see a large edition of Wikipedia (including a German language edition), which makes use of the Semantic MediaWiki extension. The dream in a nutshell, without consideration of the current state of research and development: A wikipedia that can be read not only by humans, but also by computers, a Wikipedia that can offer concrete answers to concrete questions and that creates content individually for users, something that they can make use of; great if Wikipedia played the role of the first, mainstream Semantic Web application. While this is still in the process of coming together, there are enough other things for us to do.

(btw, my translation).

Concrete answers to concrete questions, a personalized Wikipedia – I am not even aiming that high at the moment.

Just consider the absurd amount of lists in Wikipedia, all of which are maintained manually. Take for instance the list of hardcore punk bands, the list of fictional countries (to be distinguished from the list of European fictional countries) or the list of military operations.

How often do you think these need an update? And if a new hardcore punk band is added – will the creators of the new article think about adding it to the list? What about articles which make make a reference to or mention things that are or should be on a particular list?

As a list has the inherent claim of being complete, it shouldn’t be left to humans to create and maintain them – leave that to the machines! Vote Semantic MediaWiki for Wikipedia!

Author: Jana Herwig

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In May, a new bookmarking service, Faviki, started which, unlike other bookmarking services, comes to the public semantically enhanced. ReadWriteWeb already had a first look at it and described it as follows:

Faviki is a new social bookmarking tool that offers something that services like Ma.gnolia, del.icio.us, and Diigo do not – semantic tagging capabilities. What this means is that instead of having users haphazardly entering in tags to describe the links they save, Faviki will suggest tags to be used instead. However, unlike other services, Faviki’s suggestions don’t just come from a community of users and their tagging history, but from structured information extracted straight out of the Wikipedia database. Faviki’s backend uses DBpedia, a community-maintained database created by extracting structured info from Wikipedia and turning that into a database which you can query.

What Faviki does, from a user’s perspective, is to suggest tags based on Wikipedia/DBpedia terms – one of the side effects of this procedure being that e.g. “Safety (disambiguation)” can also be chosen as a possible tag – I am not so sure yet whether this is an option that makes sense (although one can probably argue that it neither does any harm, because people should be smart enough not to use such tags). And as the above screen shot of Faviki’s tag cloud reveals, it currently seems to be mainly used by people who are interested in the semantic web and search engines (with semantic search being the most promising area of application of semantic technologies). It’s probably going to take a while (if ever) before Faviki is going to reach such a diverse user-base as can be guessed from del.icio.us’ tag cloud – but then again: Maybe Faviki isn’t going to need that, as it doesn’t rely on collective tagging, but already benefits from Wikipedia’s diversity of entries!

As was also regretted by ReadWriteWeb: It’s a pity that there is currently no opportunity to import tags from del.icio.us or other services to Faviki. Who is going to win the bookmarking race? Del.icio.us has the advantage of a broad user-base, and many users already have their networks of fellow bookmarkers which they probably wouldn’t want to give up (I personally wouldn’t). Bibsonomy has the advantage of an extra feature that allows to bookmark publications and later export them as a uniformly formatted bibliography. If I could make a wish, I’d rather have a service that brings together the best of Faviki, Bibsonomy AND del.icio.us!

Related Websites:
Faviki Blog on Wordpress.com
del.icio.us tag cloud