Semantic Web Company

The Vienna-based company punkt. netServices is just about to release a demo version of their PoolParty service, a SKOS-based thesaurus management tool with linked data capabilities. I had the chance to pre-read a white paper and test their service. Here is a brief overview. You can also try a demo.

Purpose

Poolparty was conceived to facilitate various applications like

• Semantic search engines
• Recommender systems (similarity search)
• Corporate bookmarking
• Annotation- & tag recommender systems
• Autocomplete services and facetted browsing.

These use cases can be either achieved by using PoolParty stand-alone or by integrating it with existing Enterprise Search Engines and Document Management Systems or Enterprise Wikis.

Thesaurus Management

PoolParty is aiming to be easy to use for people without a strong Semantic Web background or special technical skills. The GUI is entirely web-based and utilizes AJAX so the user can e.g. quickly merge two concepts via drag & drop. An overview over the thesaurus can be gained with a tree or a graph view on the concepts.

PoolParty also helps to semi-automatically add concepts to a thesaurus as it can be used to analyse documents (e.g. web pages or PDF files) relevant to a thesaurus’ domain in order to glean candidate terms. This is done by the key-phrase extractor of KEA. The extracted terms can be selected by the user, thereby becoming “free concepts” which later can be integrated into the thesaurus, turning them into “approved concepts”.

Documents can be searched in various ways – either by keyword search in the full text, by searching for their tags or by semantic search and similarity search. The latter takes not only a concept’s preferred label into account, but also its synonyms and the labels of its related concepts are considered in the search. The user might manually remove query terms used in semantic search. Boost values for the various relations considered in semantic search may also be adjusted. In the same way the recommendation mechanism for document similarity calculation works.

PoolParty by default also publishes a Semantic Wiki version of its thesauri, which provides an alternative way to browse and edit concepts. Through this feature anyone can get read access to a thesaurus, and optionally also edit, add or delete labels of concepts. Search and autocomplete functions are available here as well. The Wiki’s XHTML source is also enriched with RDFa, thereby exposing all RDF metadata associated with a concept to be picked up by RDF search engines and crawlers. (See two examples: Cocktail thesaurus &  Standard Thesaurus for Economics)

PoolParty also supports the import of thesauri in SKOS (including several consistency checks) or Zthes format. Those functionalities can also be consumed as stand-alone web services via PoolParty SKOS Services. Additionaly, lists of concepts and their labels can also be imported via CSV files.

Linked (Open) Data

PoolParty not only publishes its thesauri as Linked Open Data (in addition to a SPARQL endpoint), but it also consumes LOD in order to expand thesauri with information from LOD sources.

Concepts in the thesaurus can be linked to e.g. DBpedia  via a service like Georgi Kobilarov’s DBpedia lookup service, which takes the label of a concept and returns possible matching candidates. The system suggests relevant resources from DBpedia and the user can select the one that matches the concept from his thesaurus, thereby creating a skos:exactMatch relation between the concept URI in PoolParty and the DBpedia URI. The same approach can be used to link to other SKOS thesauri available as Linked Data.

Other triples can also be retrieved from the target data source, e.g. the DBpedia abstract can become a skos:definition and geographical coordinates can be imported and be used to display the location of a concept on the map, where appropriate. The DBpedia category information may also be used to retrieve additional concepts of that category as siblings of the concept in focus, in order to populate the thesaurus.

PoolParty is capable of importing a SKOS thesaurus from a Linked Data server, and may also receive updates to thesauri imported this way. This feature has been implemented in the course of the KiWi  project funded by the European Commission. KiWi also contains SKOS thesauri and exposes them as LOD. Both systems can read a thesaurus via the other’s LOD interfaces and may write it to their own store. This is facilitated by special Linked Data URIs that return e.g. all the top-concepts of a thesaurus, with pointers to the URIs of their narrower concepts, which allow other systems to retrieve a complete thesaurus through iterative dereferencing of concept URIs.

Additionally KiWi and PoolParty publish lists of concepts created, modified, merged or deleted within user specified time-frames. With this information the systems can learn about updates to one of their thesauri in an external system. They then can compare the versions of concepts in both stores and may write according updates to their own store.

This means each system decides autonomously which data it accepts and there is no risk of a system pushing data that might lead to inconsistencies into an external store. Data transfer and communication are achieved using REST/HTTP, no other protocols or middleware are necessary. Also no rights management for each external systems is needed, which otherwise would have to be configured separately for each source.

Technology

The software is written in Java and utilizes the SAIL API, so it can be used with various triple stores. The thesaurus management itself (viewing, creating and editing SKOS concepts and their relationships) can be done in an AJAX Frontend based on Yahoo User Interface (YUI). Editing of labels can alternatively be done in a Wiki style HTML frontend. For key-phrase extraction from documents PoolParty uses a modified version of the KEA 5 API, which is extended for the use of controlled vocabularies stored in a SAIL Repository (this module is available under GNU GPL). The analysed documents can be stored and indexed in Lucene/Solr or any other (enterprise) search system along with extracted and semantically related concepts.

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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Today in the morning, I wrote a short blog philosophizing about linked data and the value for enterprises. I asked a couple of questions and in its core I was wondering: “Which services and keyplayers will drive the web of data in the next few months?”

In the meantime I had the pleasure to listen to Talis´ Semantic Web Gang Podcast (January 2009 with Tom Tague from Calais) and some answers came into my mind:

1. Some service providers will provide the highest accuracy regarding the links or tags (and the “things behind them) they provide for a given ressource or document (like Open Calais does). Tom Tague mentioned in the podcast quite often how important disambiguation is to provide the highest quality.
2. Some will provide end-points to a given “thing” like a company, a person etc. in addition to free ones like DBpedia, but they always will try to refer to established URIs like the ones in DBpedia or Open Calais URIs, e.g. IBM´s URI @ Calais). Those companies will provide more facts, for example about a person, as those which are available now for free. They will build on the LOD infrastructure and will live in symbiosis with group number 3. They will control to whom additional facts will be given to but they will build exactly on the same interoperable framework as the “Linking Open Data” community does.
3. Some companies will build applications on top of the linked data infrastructure. They have two kinds of knowledge: Who has the best end-points to a complex “thing” which consists of a couple of other atomic things (which necessarily exist in the web of data)? Who is interested in such a mashup?

My prediction: One possible business model will be pretty much the same as iTunes is built upon at the moment: You can listen to a song for free – but only a couple of seconds , if you want more, you pay 99 cents.

If you want to know a little bit about Werner Faymann (who is Austria´s prime minister) you go to an application which makes use from DBpedia (or the like) starting at http://dbpedia.org/page/Werner_Faymann.

If you pay 99 cents (or a bit more…) you get even more facts about Mr. Faymann, nicely mash-uped with other facts from the LOD cloud and together with special content from some other linked data sources, produced with relatively low costs due the high interoperability the Semantic Web provides – thanks to W3C and the whole community.

Virtuoso+DBpedia+OntoWiki together with several industry relevant uses cases – that´s about the formula of the OntoWiki project, which was launched yesterday in Leipzig.

Sören Auer and his team from AKSW at Uni Leipzig are the coordinators of this EU funded project which supports the development of innovative software products. All industry partners are SMEs which offer services for different fields like E-learning, E-tourism or Business Intelligence. Leipzig and OpenLink Software will work on an integration of OntoWiki & Virtuoso.

The first day of the meeting was, of course, dedicated to socialize and get to know each other. The mixture of the project team turned out to be well chosen – and in the evening we flew at higher game: We had a nice overview over Leipzig standing on the highest building of the town.

On the second day of the meeting Orri Erling, Program Manager at OpenLink Software, came up with an idea which is pretty forward: Why shouldn´t we provide OntoWiki as a Linked Data Browser, e.g. on top of DBpedia etc.? One possible outcome of this project.

Some other use cases which make already use of the existing OntoWiki system were demonstrated: Take a look at Vakantieland (…and start to plan your holidays in the Netherlands) and also at LinkedGeoData where a nice user interface can be tried out.

The Kick-Off Meeting will proceed with two workshops dedicated to semantic technologies and to Application Development with the OntoWiki Framework. Thanks to Sören and his team for the excellent hosting of this event!

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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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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The TRIPLE-I 2008 conference ended three days ago, yet there are a couple of loose ends I’d still like to tie up. First of all: Linked Data. Tom Heath was invited to give a keynote on “Humans and the Web of Data” – there are a variety of roles in which people may come across Tom and his LOD related work:

He administrates the site LinkedData.org (on behalf of the Linked Data community), he is the creator of Revyu.com (“Review anything!”), which won him the 1st prize in the Semantic Web Challenge 2007, he was a co-organizer of the Linked Data on the Web Workshop at this year’s World Wide Web conference in Beijing, and he was an interviewee in my 12 seconds definitions mission @ TRIPLE-I – see his micro definition of Linked Data in the vid below. (To learn more about Tom and the different roles he fulfils, look here).

Tom Heath explains Linked Data TRIPLE-I 2008 on 12seconds.tv

His keynote was not so much an introduction to Linked Data (I should expect that a conference like TRIPLE-I/I-Semantics would typically attract people who at least have an idea of what Linked Data is about), but rather a confirmation that the Web of Data is no longer a fiction, but a fact. One of the often cited proofs is the growth of the LOD dataset cloud over the last year, as shown in the image below (clicky for biggy, visualization created by Richard Cyganiak).

At the same time – and this was accordingly acknowledged by a later presentation given by Wolfgang Halb which had been prepared collaboratively by Tom, Wolfgang, Michael Hausenblas and Yves Raimond – it’s not just the sheer number of triples on the web that counts. Over the course of one year, the efforts of the Linked Data community (who seek to populate the web with open data, data in RDF) generated 4 billion triples – but only 3 million interlinks.

Their paper was an attempt to measure the size of the Semantic Web based on interlinks. A brief excerpt from the conclusion:

We have identified two different types of datasets, namely single- point-of-access datasets (such as DBpedia), and distributed datasets (e.g. the FOAF-o-sphere). At least for the single-point-of-access datasets it seems that automatic interlinking yields a high number of semantic links, however of rather shallow quality. Our finding was that not only the number of triples is relevant, but also how the datasets both internally and externally are interlinked. Based on this observation we will further research into other types of Semantic Web data and propose a metric for gauging it, based on the quality and quantity of the semantic links. We expect similar mechanisms (for example regarding automatic interlinking) to take place on the Semantic Web.

Another point raised by Tom in his key note was the issue of trust: According to his research, there are five parameters that have an influence on whether we trust a source or recommendation on the web or not: experience , expertise, impartiality (we don’t trust a travel agent, because we can’t help but believe that she is mainly going to recommend the offer of her ‘favourite’ clients), affinity, and track record, with experience, expertise and affinity being the most important ones. A semantic people search engine Tom presented, Hoonoh.com (currently in alpha), thus allows to weight search results according to these three criteria.

Tom’s concluding statement emphasized that Linking Data makes sense not for the sake of it, but for the sake of being at the service of humans: “A web of machine-readable data is even more interesting from a human than from a machine perspective,” for instance in search engines like Hoonoh.com

I’ve just published an interview I did with Jiri and Ryan from zembly, the new ‘wiki for code’, where “users can share, clone and modify widgets and applications for Facebook, Meebo, iPhone and more”. I also raised the question how zembly matters to the Semantic Web/LOD-community – here is their answer and a link to an example of an application for the Semantic Web (login is required to gain access – free beta invites can be obtained from the widget in the sidebar of our blog – scroll down):

Q: What could be possible applications for the Semantic Web Community and how could the Linked Data Community benefit from zembly?

A: This is a great question! While social element is very key to us (social platforms provide identity services, social graphs, and distribution channels), zembly is also about building situational apps, which are often based on various data sources. zembly is great in accessing web APIs – it’s just a single JavaScript statement to access many of them.

When you combine aspects of common vocabulary and common access mechanisms of the semantic web on top of it, widgets and services suddenly become even more interoperable. So I think the Linked Data Community will benefit greatly. With zembly, it’s incredibly easy to create and host applications that leverage the data web. And we would like to make it easy to build providers for the data web too. The basic pieces are already in. Now it’s just a matter of putting them together.

Here is link to a service Jiri built for querying dbpedia: The service automatically extracts all of Sean Connery’s film partners and makes the triples available in JSON format (access only after login, so you’d need to get your beta invite first).

Zembly were also a sponsor at Facebook’s F8 conference, here’s a look back on the conference by Jiri on the zembly blog.

Related articles:

The full interview with Ryan Kennedy and Jiri Kopsa on our website
My first blog post about zembly from July 8 with a brief introduction for using it

News has reached me this morning that UMBEL has now been publicly released! UMBEL is a new vocabulary for the Semantic Web – I first learned about it when Andreas Blumauer returned from LinkedData Planet where he had met up with Mike Bergman from Zitgist LLC who are working on UMBEL.

Here is the release announcement Mike communicated via email yesterday:

UMBEL (Upper Mapping and Binding Exchange Layer) [1] is a lightweight ontology for relating Web content and data to a standard set of 20,000 subject concepts. Based on OpenCyc [2], these subject concepts have defined relationships between them, and can act as semantic binding nodes for any data or Web content. A further 1.5 million named entities have been extracted from Wikipedia and mapped to the UMBEL reference structure with cross-links to YAGO [3] and DBpedia [4]. The system can easily be extended with additional dictionaries of named entities, including ones specific to enterprises or domains.

UMBEL is provided as open source under the Creative Commons 3.0 Attribution-Share Alike license. The complete ontology with all subject concepts, definitions, terms and relationships can be freely downloaded [see 5]. All subject concepts and named entities are available as Linked Data [see 5]. Five volumes of documentation [5] are also available.

The release is accompanied by about a dozen Web services [6] for using or manipulating UMBEL, along with a new introductory slide show [7]. Additional release information may be found on Fred’s [8] or my [9] separate blog postings. We welcome those with interest or suggestions for improvements to do so through the UMBEL discussion forum [10]. We will shortly be putting easier services online for such input.

So, enjoy! We look forward to your commentary, suggestions and putting UMBEL under production-grade stress. We know will be doing the same!

Regards, Mike

Great release! They have also given us access to a media-oriented article which you can read on our portal.