We opted for Semantic MediaWiki (SMW) and the extensions Semantic Forms and Semantic Drilldown. In this blog post we’ll take a look at the handy features you get with these. This short tutorial is based on my SMW demonstration at the Web of Data Practitioners’ Days in Vienna two weeks ago.

As the book is in German, the wiki is set up in German, too, but that shouldn’t be a problem for understanding the demonstrated features. For the following examples, we have created a mirror of our productive wiki, so don’t hesitate to edit and play with this mirror wiki (we might refresh it occasionally, so don’t write any data into the wiki that you don’t also have stored elsewhere). This tutorial is going to take you through the following SMW features:

• Automatically created lists
• Faceted search
• Semantic queries
• Entering data via forms
• RDF export

So let’s see what these features hold for us.

• Automatically created lists

A common problem in wikis like Wikipedia is the (amount of) effort it requires to create and maintain various lists like the list of the EU’s largest cities. It’s an equally laborious and error-prone activity to keep such lists up to date; as a result, there are a lot of useful Wikipedia lists we can imagine that don’t exist at all, like a list of the world’s largest corporations with a CEO younger than 35.

In SMW it is easy to create all kinds of lists with queries. This page for the book’s table of contents is an example. View its source to see the inline queries used to generate the page (click to enlarge or view the source on the wiki):

As a result, the list is generated afresh any time the table of contents page is called up. If the data on an article’s page has changed, it will also be updated in that list – while in regular MediaWikis one has to manually update the data in both places (the article, and the list), which, apart from the extra work, also makes errors and inconsistencies much more likely.

• Faceted search

Take at look at the list of articles page… it uses Semantic Drilldown, so you can narrow the displayed articles down by author (“geschrieben von” = “written by”) or by topic (“Schlagwort”): For instance, select a topic from the tagcloud style list and you’ll be presented with a list of articles covering the subject in question. You can add another topic (which makes the set of results larger) or additionally specify an author (which further narrows it down).

• Semantic queries

A straightforward approach to exploring the values of a semantic property is to just look at the property’s wiki page. E. g. view a list of everyone’s blog URLs on this page: Property:Weblog.

More sophisticated queries can be made on the Special:Ask page.
To display all organisations located in Vienna or Graz just copy paste

[[Category:Organisation]][[Hat Sitz in::Wien||Graz]]

into the left hand box (“Hat Sitz in” = “is based in”).

Now go a step further and ask for the name and email address of all persons that work for a organisation located in Vienna or Graz and print their e-mail addresses. Again, copy and paste

[[Category:Person]][[arbeitet bei::<q>[[Category:Organisation]][[Hat Sitz in::Wien||Graz]]</q>]]

into the query box and copy and paste

Email

into the box for additional printouts

What is also very useful is that you can save any query on its own page, so users don’t have to know or remember the syntax. Here is the query page for people living in Vienna or Graz: Concept:Persons_working_in_Vienna_or_Graz,

• Entering data via forms

This feature is one of the essential advantages that SMW holds over a regular MediaWiki: Most of the content of our wiki is automatically generated from data which have comfortably been entered by filling in forms. This means a regular user doesn’t have to edit any wiki text to be able to contribute content.

See for yourself:

Log in with testuser and testuserpw, go to the page Form:Person and enter a name (e.g. your own name; click to enlarge).

When you fill in the fields “Works for”, “Friends” and “Topics” you will notice that an autocomplete feature assists you by suggesting pages that are already in the wiki. This helps to avoid multiple wiki pages for a single entity.

Before you submit the form, copy this line and paste it into the “free text” box – it’s the line that calls up the appropriate template, in this case for a person:

{{Template:Person_additional}}

In combination with the data you entered, this will generate the actual wiki page.

There is one thing you need to observe: You will probably find that your information is missing in some of the page’s paragraphs. If that happens just click on “edit” and save the page again, after which the page content will be updated properly.

In this way, a properly set up SMW with Semantic Forms enables the users to enter data without knowing how to write wiki syntax, and assists him or her in the data entering process.

• RDF export

To top it off, SMW enables you to link your data into the LOD cloud and to reuse properties and classes from external ontologies. Take a look at this exported RDF/XML containing data about the author Andreas Blumauer. You’ll see a lot of foaf terms reused and also an owl:sameAs pointing to his personal URI.

This sums up the introductory tutorial – if you want to learn more about SMW, then check out the wiki document for the complete tutorial or see SMW’s own documentation.

Author: Thomas Schandl, SWC

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Multimedia content, as Ansgar pointed out, is hardly annotated, badly organized, and hardly ever looked at again – just think of the 300 something pics you might take on an average week-end getaway, and which you never touch again. Annotating multimedia content requires a lot of work and dedication – but most of the time, these pictures eventually dissappear in the “digital shoe box” that is your photo management software.

The most obvious remedy is to annotate content as early as possible, ideally when creating the content, ideally already on your portable camera (formerly known as: mobile phone:) Ansgar suggested to provide incentives for people to encourage picture annotation – professionals could for instance receive a higher financial reward if the deliver already annotated pictures. And of course there are ‘Games with a purpose’ such as Google Image Labeler, where players tag images in pairs, with and against each other, and are rewarded with the entertainment factor of the game.

The slide below shows what has happened (or will happen) to the process of creating photo books in the digital age and the age of mashups:

After all, this is the age of the social semantic web, so why not try and (re-)use the content, structure and contexts that other users have already created on the web? Content augmentation, for the scope that Ansgar is concerned with, consists in the reuse of content and structures (e.g. from sources such as Flickr and Wikipedia, Geonames) made possible through the definition of rules, e.g.:

• If there are two or less pictures on a page*
• then automatically augment the page with additional photos using location information.

* Page here means a page in the album you are currently working on – you probably took a picture of yourself and your friend in Paris, and even though you went to the Centre Pompidou, you forgot to actually take a pic of the building itself – well, let the web be your library!

So the goal is clear: develop a procedure for applying automatic content augmentation in the creation of good photo books.

But what makes a ‘good’ photo book anyway? Here are some of the results of a structural analysis of real, human-created photobooks conducted at CeWe Color:

• % of photos with faces: 36%
• Number of album pages: 16.96
• Photos per page: 6.69
• Text fields per page: 1.45
• % of pages with text: 87%

There are many rules that can be established from the structural analysis, which can be applied in turn in the creation of photoboooks, e.g. rules like this one,

• If the text located in the upper third of a page
• if the font size is equal or larger that 16 points
• if the number of words is less than 10
• if there is no caption on the page that has a bigger font size
• then this page is the title

Ansgar recommended xSmart, which he described as a “context-driven authoring tool for page-based multimedia presentations.”

Ansgar’s presentation was followed by two more: one by Yves Raimond on Interlinking Music on the Web of Data, and one on Interlinking Multimedia – in spite of better intentions, I did not manage to cover these two in detail, but at least I gathered the links to relevant resources from all three sessions…

Links for Ansgar Scherp’s session

• Continuous Media Markup Language (CMML) (see also: CMML on Wikipedia)
• COMM – A Core Ontology for Multimedia
• Caliph and Emir – Java & MPEG-7 based tools for annotation and retrieval of digital photos and images
• X- COSIM – a framework for Cross(X)-COntext Semantic Information Management

Links for Yves Raimond’s session

• Music Ontology Specification
• The Timeline Ontology
• The Event Ontology
• Functional Requirements for Bibliographic Records
• www.SonicVisualiser.org – a program for viewing and analysing the contents of music audio files
• www.dbtune.org – music-related RDF
• Yves Raimond, Christopher Sutton and Mark Sandler 2008: Automatic Interlinking of Music Datasets on the Semantic Web. (PDF, 467 KB)
• Interview with Yves Raimond: Finding vegetarian music: What B.B. King and the Beastie Boys have in common
• DB-Tune Facet Demo
• Henry 1 and 2 – a SWI-Prolog N3 parser/reasoner, and DSP-driving SPARQL end point

Links for Michael Hausenblas’ session

• InterlinkingMultimedia.info – a wiki dedicated to Interlinking multimedia (iM), “a light-weight bottom-up approach to interlink multimedia content on the Web of Data”.
• Rammx – RDFa-deployed Multimedia Metadata
• CaMiCatzee – multimedia interlinking concept demonstrator.

Last not least: Ansgar Scherp allowed us a sneak peek of SemaPlorer, a Large-scale Semantic Faceted Browsing Application for Multimedia Data that is going to be revealed on Dec 2, 2008, at the BOEMIE Bootstrapping Ontology Evolution with Multimedia Information Extraction) workshop in Koblenz. Here is an abstract:

Navigating large media repositories is a tedious task, because it requires frequent search for the `right’ keywords, as searching and browsing do not consider the semantics of multimedia data. To resolve this issue, we have developed the SemaPlorer application. SemaPlorer facilitates easy usage of Flickr data by allowing for faceted browsing taking into account semantic background knowledge harvested from sources such as DBpedia, GeoNames, WordNet and personal FOAF files. The inclusion of such background knowledge, however, puts a heavy load on the repository infrastructure that cannot be handled by off-the-shelf software. Therefore, we have developed SemaPlorer’s storage infrastructure based on Amazon’s Elastic Computing Cloud (EC2) and Simple Storage Service. We apply NetworkedGraphs as additional layer on top of EC2, performing as a large, federated data infrastructure for semantically heterogeneous data sources from within and outside of the cloud. Therefore, SemaPlorer is scalable with respect to the amount of distributed components working together as well as the number of triples managed overall.
Steffen Staab, Information Systems and Semantic Web (ISWeb), University of Koblenz-Landau, Germany

Thank you, thank you, thank you, it was a lovely event with an unusually high amount of processable input!

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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Ansgar Bernardi, deputy head of the Knowledge Management Department at Deutsches Forschungszentrum für Künstliche Intelligenz (DFKI, or the German Research Center for Artificial Intelligence) and Nepomuk’s coordinator, explains, “The basic problem that we all face nowadays is how to handle vast amounts of information at a sensible rate.” [...] “The point is, you have a vast amount of information on your desktop, hidden in files, hidden in emails, hidden in the names and structures of your folders. Nepomuk gives a standard way to handle such information.”

At a high level of generalization, Nepomuk has three main aspects, according to Bernardi. First, there is a standard framework for annotating pieces of information so that connections can be made between them. Second, there are ontologies, the sets of “documented shared understanding” or common concepts that can be defined for particular types of information, such as bio-science or computer desktop use. Finally, there are the tools for making or using the annotations and ontologies, what Bernardi calls the “workspaces that connect to other workspaces and help you in your day to day activities of collecting information, structuring it, making sense of it, and creating new information and communicating it.”

Leo has provided the relevant download links for those who “want to get their hands dirty” with Nepomuk (as he put it) on his blog. Leo Sauermann and Ansgar Bernardi also contributed an article about the Semantic Desktop to the recently published Social Semantic Web volume – a preview of the article is available here (in German – I’m sorry!).

Brian Davis‘ part of the talk focused on Lifting and Human Language Technology (HLT) for the Semantic Desktop – Semantic Lifting means to capture semantics and translate them into ontologies. Human language technology (HLT), in its broadest sense, can be described as computational methods for processing and manipulating language (for instance text analysis).

One of the goals of the Semantic Desktop is speech act detection for email – speech act here as defined by John Searle. At its most basic definition, a speech act is simply an utterance, but is also often understood more specifically as an illocutionary act (which is a term introduced by John L. Austin in How to do things with words), or a ‘performative utterance’, meaning that by saying something, one actually does something. For instance, the sentence “Please have the document ready for Workshop 1.” contains an instruction: It informs the reader about the requirements for a particular event, and asks him or her to meet these requirements.

Brian also introduced Roundtrip Ontology Authoring (ROA), which is a process that allows non-expert users to author or amend an ontology by using simple, easy to learn, controlled natural language. The process is a combination of Controlled Language for Information Extraction (CLIE) and Text Generation which is developed on top of GATE. ROA is documented on the the Nepomuk website; for further information about CLIE, read this article by Valentin Tablan, Tamara Polajnar, Hamish Cunningham and Kalina Bontcheva: User-friendly ontology authoring using a controlled language (PDF, 64 KB).

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First session – Web of Data 101 by Yves Raimond and Keith Alexander – explained the implications of the move from a Web of Documents to a Web of Data: With the Semantic Web architecture, data can be made explicit on the web. Data here means not only data contained in documents, but data describing persons, cities, bands, events, finally arriving at the “Web of Things” (see also this presentation by Dave Raggett, W3C, – PDF 2,7 MB). The Web of Data wouldn’t be a Web if the data weren’t interlinked – here is an overview of the principles of Linked Data:

• always use URIs as names for things
• more specifically, use HTTP URIs so that people can look up those names on the web
• when someone looks up an URI, provide useful RDF information (RDF is the data model used for data on the web of data)
• include RDF statements that link to other URI (otherwise it wouldn’t be a web).

Please also watch out for what is already happening and is going to happen in the future on www.bbc.co.uk/music/beta. This beta site is powered by MusicBrainz, the open content music database that is also part of the Linked Data cloud. Yves is collaborating with the BBC in the Programmes ontology project, the aim of which is to provide a simple vocabulary for describing programmes.

Yves’ intro was followed by a Turtle hacking session led by Keith Alexander. Turtle is a serialisation format for RDF, i.e. a format in which you can write RDF statements. The Turtle session is documented here on Keith’s Talis website. Even though I copied and pasted most of the code, I didn’t manage to produce a piece of valid code in N3 right away (i.e. not valid according to this validator). It only worked after I had removed the statements about who I know or what I am interested in – without these connections, what remains is a bit boring, I guess. But this looks like I managed to post at least something to the test store!

EDIT: Problem was that I had terminated the statements to soon, with a dot where a semicolon should have been; the demo didn’t allow me to overwrite the first post to the store, but here is my FOAF self-description in Turtle:

@prefix foaf:<http://xmlns.com/foaf/0.1/> .
@prefix owl:<http://www.w3.org/2002/07/owl#> .
@prefix people:<http://api.talis.com/stores/wod-pd-sandbox/items/People/> .

people:JanaHerwig a foaf:Person ;
foaf:name “Jana Herwig” ;
foaf:nick “digiom” ;
foaf:homepage <http://digiom.wordpress.com> ;
owl:sameAs <http://dbtune.org/last-fm/jezobeljones> ;
foaf:knows people:MichaelHausenblas, people:YvesRaimond, people:WolfgangHalb ;
foaf:topic_interest <http://dbpedia.org/resource/Semantic_Web>, <http://dbpedia.org/resource/Web>, <http://dbpedia.org/resource/Popular_Culture>, <http://dbpedia.org/resource/Lolcat>.

Achieved with zero Semantic coding skills – the Web of Data cannot be so hard to achieve:)

EDIT: Did do the update, too – just posted my first SPARQL query to this endpoint. Are the results going to be preserved in this link? Here is the query “by foot”:

PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX people: <http://api.talis.com/stores/wod-pd-sandbox/items/People/>
DESCRIBE people:JanaHerwig