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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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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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.

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

Author: Pascal Hitzler

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

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 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… it does have the extra advantage though that you can add links, cite resources and that you get to keep your blog posts (or tweets or equivalents thereof) for later reference or debate. Different people approach blogging differently – the approach I would favour in the context of this definition is a form of blogging that invites dialog in that it allows others to comment and react, and where contributors aren’t anonymous, distant institutions, but are addressable using their personas/identities on the web. As such, contributions are often marked or tinted by the views and personality of that real-life person behind a persona/identity. As a short cut, think of this flavour as the flavour of the social media tag cloud.

Web-Flavour 2: Wikipedia – evolving slowly with the flow

Wouldn’t you agree that Wikipedia is like a sea of knowledge? It is fed by brooks and rivers (in this analogy: for instance the article and contributions that are invited on the Wikipeda Community portal) that make it rise and swell like tidal waves would, but mostly by millions, billions and trillions of tiny drops that trickle in on a daily basis. In comparison to the blogosphere, the world inside wikipedia is a rather neat and orderly one: Titles of pages are unique, and were they aren’t, there are disambiguation pages (like this one) in place. Even though articles are written by real humans (I assume), there is no visible author attached to an article (unless you start developing an interest for Discussion Pages, e.g. this one; most people don’t). Wikipedia is the sea of knowledge we bathe in on a day-to-day-basis without even noticing – just try to remember how many and which Wikipedia pages you have looked at today or this week – can you? Most probably not. It’s the result of a community effort, but it’s not about views and opinions of individuals, but about what we all know together or would know together if we could wire our brains to one another (tired of the Wikipedia examples? Check out Factolex instead, a collaborative, micro-content encyclopedia that allows you to extract and conceptualize bite-sized pieces of knowledge as you go).

This is the flavour I like to have around me every day, because it makes things easier without asking for a huge effort. It’s also the flavour of thesauri and metadata schemata.

Web-Flavour 3: The unfinished structure of ontologies

Flavor three is NOT (as one might expect) the flavour of the Encyclopedia Britannica Online (which is one huge data silo and therefore not relevant for my scope of interests) … instead, I would argue that it’s the flavour of the web’s infrastructures and of knowledge infrastructures like ontologies. Think of the geometry of snow flakes: they all follow rules but none of them is like another. The open world assumption of ontologies also applies to snow flakes – just because you haven’t seen a particular shape doesn’t mean it doesn’t exist! Nobody has the patent for building snowflakes – Wilson Bentley in his famous snowflake shots just captured an expression of rules that are out there, in the world, belonging to the world. Ontologies capture the structure of what, to the best of knowledge and belief, can be said about the world. Anyone can build an ontology, but we prefer to have experts do that job: members of the scientific community which has its own ranking and weeding mechanisms in place.

Flavour three is the flavour of things we wish to be able to rely on on the web, and where we can invest a trust that is much greater than the trust we invest in people. More like the trust we invest in, say, the laws of nature.

So what is the flavour of a Semantic Wiki?

A good mix of flavour two and three, I would argue. A Semantic Wiki is a vessel for the sea of relevant knowledge (relevant for instance for the members of a particular team), but enhances it with the structure of the domain knowledge that applies.

Having said that: A semantic wiki would be much spicier if it also had a bit of the flavour of the blogosphere and social media (i.e. flavour one), as there are tasks where a bit of a debate, a bit of a controversial exchange and the ability to respond to people directly is highly valuable! Just as water, knowledge goes through a cycle of different states of matter, and knowledge is not processed by segregated individuals, but in communities and through networks of people.

Before publishing this, I wanted to get some feedback in particular from KiWi members working on enabling technologies – here is Peter Dolog‘s take; Peter is an Associate Professor in the Information Systems Unit at the Dept. of Computer Science Aalborg University:

I like the distinction and comparison of knowledge to some natural elements like gas, liquids, solids or snowflakes. These give a good metaphor for understanding when talking about different flavours of knowledge. It is also fascinating to see how humans move between these three categories by participating in different social processes or simply by studying these things.

It is, however, a bit more difficult to see how this can be done or supported in the most suitable form on the web or in the intranets of companies. At the same time it seems to me, from the discussions we have had in the KiWi project, that semantic wiki platforms could indeed facilitate this. Wikis naturally provide the social contexts for contributions. Semantic wikis with tags and ontology management seem to be a first step towards a flexible knowledge consolidation infrastructure where one can move easily between these categories; and other technologies such as natural language processing and automated reasoning can help further. Personalization can further provide and adjust views on the knowledge according to preferences.

I am happy that we can study these phenomena in the KiWi project at least to a certain extent and perhaps contribute to this as well. I am confident that this is relevant also for the industry and especially for large distributed companies where externalization of knowledge is a must.

So there is a lesson to be had: When building a knowledge management system using the web, think of the three states of knowledge, but most importantly, also think of the form of community and community processes that are required or preferable to allow future users to really put that knowledge to work – melt it, share it, heat it, debate it, freeze it, keep it, let it evolve!

Image sources on Wikicommons:
Water vapour by Markus Schweiss
Wake at Boelge Stor by Malene Thyssen
Ice Crystals by Petr Dlouhý

Author: Jana Herwig

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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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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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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