Tag Archives: Wikipedia

Andreas Blumauer

DBpedia, UMBEL & the Future Web’s Ecology – interview with Mike Bergman & Sören Auer

by , Category: Linked Data & Open Data, Mashups & Web services, Ontology Engineering

Sören AuerThe 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):

Sören Auer (image above), Mike Bergman (image below)

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.

Mike BergmanIn 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…
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Jana Herwig

Session 4: Using the Web of Data [WOD-PD]

by , Category: Conferences & Events, Linked Data & Open Data

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.

Alan Dix giving a talk

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.

Two Christmas crackersImage 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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Jana Herwig

Which flavour does knowledge have on the web?

by , Category: Knowledge Management, Software Development

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… Continue reading →

Jana Herwig

Why Faviki is able to suggest tags in 13 languages

by , Category: Linked Data & Open Data, Mashups & Web services, Tools & Software

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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