Step 1. Generate a thesaurus by using a linked data source like DBpedia

As recently reported SWC has developed a tool called SKOSsy which can be used to extract seed thesauri from DBpedia. In our example I will generate a knowledge model describing the domain of “digital photography“. This step took around 15 minutes.

Step 2. Load the thesaurus into PoolParty and improve it to your needs

After the seed thesaurus has been loaded into PoolParty Thesaurus Manager you have many possibilities to enhance the knowledge model further: Add more categories, synonyms, relations etc. In this example I use the seed-thesaurus without any further improvements. This step took approximately 2 minutes.

Step 3. Generate an automatic text extractor on top of your thesaurus

This step took a couple of seconds and ended up in having generated a fast and reliable text mining application on top of PoolParty Extractor, ready to be used to enrich your documents with data from the LOD cloud.

You can try it out here: PPX Live-Demo

To try the extractor on your own, please take a look at the image above which shows a proper configuration, you have to insert the following UUID in the form: d35d4ddb-adc3-4ea5-b027-deacac03e391

Since our example is all about ‘digital photography’, we recommend to use text samples (or some fragments) like these ones to test the quality of PPX based text analytics:

• Digital Camera Image Noise (Results as HTML, RDF/XML)
• Nikon D3S In-depth Review (Results as HTML, RDF/XML)
• Introduction to Shutter Speed in Digital Photography (Results as HTML, RDF/XML)
• Digital Camera Sensors (Results as HTML, RDF/XML)

Let us know what you think about this straight-forward approach and your opinion about the quality of the results. We believe that thesaurus based text mining is in many cases an alternative to some other approaches, especially if you want to to enrich your content with information from the upcoming web of data.

Of course we would be happy to generate other demos in the areas of your interest! Just get in contact with us by using our contact form.

The best paper award went to Pablo N. Mendes, Max Jakob, Andrés García-Silva and Christian Bizer for their contribution “DBpedia Spotlight: Shedding Light on the Web of Documents“.

Abstract: The paper impressively shows how Linked Open Data can be utilized  as background knowledge within document-oriented applications such as search and faceted browsing. As a step towards interconnecting the Web of Documents with the Web of Data, the authors developed DBpedia Spotlight, a system for automatically annotating text documents with DBpedia URIs. DBpedia Spotlight allows users to configure the annotations to their specific needs through the DBpedia Ontology and quality measures such as prominence, topical pertinence, contextual ambiguity and disambiguation confidence. They compare their approach with the state of the art in disambiguation, and evaluate their results in light of three baselines and six publicly available annotation systems, demonstrating the competitiveness of the system. DBpedia Spotlight is shared as open source and deployed as a Web Service freely available for public use.

For the 4th time I-SEMANTICS hosted the Triplification Challenge, an event aiming at stimulating the availability of large quantities of RDF data and showcasing practical applications built on top of them. The Challenge consisted of an unspecific “open data track” and a dedicated “open government data track” for which one winner was selected. The prize money of 1000 Euro each was sponsored by Wolters Kluwer Germany.

The “open data track” award went to Daniel Garijo, Boris Villazón and Oscar Corcho for their contribution “A Provenance-Aware Linked Data Application for Trip Management and Organization“.

Abstract: The authors present El Viajero, an application for exploiting, managing and organizing Linked Data in the domain of news and blogs about travelling. El Viajero makes use of several heterogeneous datasets to help users to plan future trips, and relies on the Open Provenance Model for modeling the provenance information of the resources.

The “open government data track” award went to John Erickson, Yongmei Shi, Li Ding, Eric Rozell, Jin Zheng and Jim Hendler for their contribution “TWC International Open Government Dataset Catalog“.

Abstract: The TWC International Open Government Dataset Catalog (IOGDC) integrates a diverse selection of more than 70 government dataset catalogs from around the world. IOGDC demonstrates a practical dataset catalog metadata model for integrating diverse dataset catalogs collected from the real world and linking those catalogs into Linked Data Cloud. IOGDC’s faceted browsing and search interface provides a scalable and reconfigurable solution for finding and browsing open government datasets which also offers a compelling demonstration of the value of a common metadata model for open government dataset catalogs. We believe that the vocabulary choices demonstrated by IOGDC highlight the potential for useful Linked Data applications to be created from open government catalogs and will encourage the adoption of such a standard worldwide.

All papers are available in the ACM Digital Library.

We thank all participants for their contributions and wish the winners all the best for their future work!

So far 143 people whose organization already make use of controlled vocabularies answered the question “Which kind of controlled vocabulary do you use or plan to use in your applications?”.
The results so far show that lightweight models like taxonomies and thesauri are somewhat preferred over ontologies:

Taxonomies are the favorite, as 73.6% of participants use or plan to use them, followed by thesauri (62%) and ontologies (61.2%), while simple glossaries lag considerably behind with a usage of 31.4%.

This survey will close in about a week, so please take this chance to make your opinions on this topic count! You can find the questions here, it will take 5-10 minutes to answer them.

All participants will gain access to a report with the results within the following month. The most interesting results will be made public on this blog.

The invited speaker of the “Semantic Enrichment” session was Pam Harley, Vice President, Product & Market Development of Semedica, a division of Silverchair.  Pam gave a high-level account of the possibilities and added value which comes with Semantic Enrichment, in a way suitable for the non-technical audience. I personally benefited particularly from the large variety of reasons for adopting Semantic Technologies in publishing which she presented and discussed in her talk (see also her slides).

My presentation (see also the slides) about the Semantic Web journal was part of the “Rethinking the Structure of Peer Review” session, and was focused on the open and transparent review process which we have adopted for the journal. After the presentation, throughout the event, I received ample feedback and remarks which in particular commended us for setting up a realistic improvement of the review process while avoiding radical changes which are likely to meet too much resistance from researchers. I certainly agree with this assessment. The presentation also contains a bit of information on how the journal is doing (in short: it’s doing great).

The seminar was a very enjoyable experience. In particular, it was enlightening to learn about publisher’s perspectives on scientific publishing, reviewing processes, and emerging revenue models. It was also nice to see that Semantic Web as a technology has a natural place in these discussions and is seeing more and more adoption in practice.

If you’re curious to learn more, have a look at the videos of the presentations.

[Author: Pascal Hitzler]

How to model divergent views on one concept?

Such a central model is not only much harder to manage, but there is also the general problem that differerent departments like marketing, quality assurance, R&D, etc. will have divergent views on the model and its concepts. These different perspectives on one and the same concept are hard to unify in a single model.

Think of a company that sells mobile phones and wants to create a model of its line of products. It wants to utilize this model in the context of its online shop as well as in the context of its user support forum. While the structure of the model (i.e. the relationships between the products) might be very similar or the same in both contexts, there will be differences in which properties of the products are actually relevant in the respective contexts.

In the model of the marketing department there might be a concept for a “Phantastax StamiMaxx” cell phone with a definiton “The StamiMaxx has a powerful battery and is great for professionals who travel a lot”. They might relate it to manufacturer “ACME Corporation” and to several concepts representing different features like “Android OS”, “Multi-touch touchscreen”, etc.
The very same phone has different properties that are interesting from the Quality Assurance departement’s perspective. They might call it by a more specific name like “Phantastax i3000 StamiMaxx S”, have a different definition for it like “3G cell phone implementing the new WTF3000 protocol, …” and relate it to concepts representing known problems and their solutions.

Now they face the task to integrate these different models, as it is not desirable to use a bunch of isolated models within one company.

Support of collaborative work on distributed models

To support this kind of collaborative work on distributed knowledge models, we would like to link the concepts of the models, just as is we link documents in the World Wide Web. Fortunately the Simple Knowledge Organisation System (SKOS) offers mapping properties that can be used to define relationships between concepts from different knowledge models.

E.g. when we want to say that concept “Phantastax StamiMaxx” in the product line thesaurus refers to the same real world entity as concept “Phantastax i3000 StamiMaxx S” in the Quality Assurance thesaurus, then we can use skos:exactMatch to express that. If we want to express that the concepts are merly similar, skos:closeMatch could be used.

The other SKOS mapping properties express a hierarchical (narrowMatch, broadMatch) or an associative (relatedMatch) mapping relation between concepts from different concept schemes. With those we can say that my Samsung Galaxy concept has a skos:broadMatch “Smartphone” in the product line vocabulary and a skos:relatedMatch “ACME Corporation” in a controlled vocabulary about Tech companies.

Modularisation of knowledge models

In this way SKOS thesaurus management systems like PoolParty make it possible to modularise knowledge models, represent concepts in their different contexts and consequently enable collaborative work on those models: The marketing guy can work on his model with the concept properties focused on sales without disrupting the work of the quality assurance expert on her own thesaurus. Later one or both of them can create the skos:exactMatch link between the concepts that are the same, like seen in the “Exact Matching Concepts” box in screenshot of PoolParty below.

Enrich your knowledge: Get connected with the LOD Cloud

Going a step further the models could be connected to external knowledge, e.g. a source from the Linked Open Data (LOD) Cloud. Once we establish links to LOD hubs like DBpedia, we can import additional information for their concepts or use it to establish whether similar concepts from different models really refer to the same real world resource.

Les Kneebone is Project Manager at Education Services Australia Ltd.
Among other projects he is responsible for Schools Online Thesaurus (ScOT).

PoolParty Team asked Les a couple of questions about thesaurus management, linked data and the semantic web. Here is a short summary of this interview:

Why did you choose thesauri to organize your information? What kind of problems are you able to solve with this approach?

A thesaurus approach was chosen rather than a subject headings approach because we assumed (and continue to assume) that post-coordinate indexing will drive vocabulary-assisted discovery.

Which role does SKOS and/or Linked Data play in order to achieve your goals?

ScOT concepts are now published as URIs. This approach solves the problem of different ScOT versions in disparate systems.

What are the most important values you generate for your stakeholders? What kind of applications can be built or have been built on top of your thesauri?

The Achievement Standards Network (ASN) provides a model for profiling curriculum statements and linking those statements to education resources using various rdf vocabularies. By profiling curriculum statements to learning resources, more precise matching is achieved.

What are the most important arguments to use Semantic Web standards and linked data, especially in education?

The Australian education sector is characterized by many disparate systems in different education jurisdictions. Semantic web technologies are one solution to linking education data in Australia.

Why did you choose PoolParty to manage your thesauri?

We had already identified SKOS as an important standard for ScOT so it was natural to select PoolParty as a our new thesaurus management tool.

What are your future plans and next steps? How do you manage to get your thesauri used, how are you going to build an “eco-system” around your work? (Do you plan to publish ScOT on the LOD cloud? Under which licenses?)

Our vocabularies are currently for non-commercial use and we don’t anticipate any change to the license at this stage. The ScOT license requires attribution, permits derivatives that must be shared, and is for non-commercial use.

Read the full interview here.

Another basic concept of KML is „Point“. Geo RDF provides properties like „geo:long“ or „geo:lat“ which express longitude and latitude of a POI and thus makes it possible to uniquely identify certain points on a map using RDF standards.

Thus it is possible to map the geo conventions of ARML to the geo conventions of the Semantic Web which are mainly based on Geo RDF.

As soon as a placemark has received a URI it is also possible to expose it as linked data and interlink it with repositories like Geonames, DBpedia or LinkedGeoData (which is based on Open Street Map) to generate Linked Geodata.

ARML makes it possible to link / make a  relation between a „Provider“ and a „Placemark“. Thus it is also possible to use a URI to describe a provider and link it to a placemark using the typical triple-struture imminent to RDF.

OpenARML/Wikitude uses tags to describe certain things. These tags are currently represented as literals (strings), seperated by commas. This poses that obstacle that these tags can hardly be processed by machines. With RDF each tag would be assigned a URI, thus changing it from a literal to a resource, which further can be represented in SKOS/RDF, another Semantic Web specification of the W3C.

ARML/Wikitude also offers attributes to describe POIs like phone, URL, email, attachment etc. which all of them could be represented by Semantic Web defacto standards like FOAF, SIOC etc.

Summing up, ARML/Wikitude documents could relatively easily be transformed in valid RDF / Linked Data Graphs. This could help to enrich AR-applications with data from the LOD (Linked Open Data) cloud. Vice versa data generated by ARML applications could be exposed as Linked Data.

As a pragmatic approach we recommend  to generate on top of existing Wikipedia URLs the corresponding DBpedia URIs which would directly transform ARML placemarks into a resource as part of the existing LOD cloud.

As soon as placemarks are mapped to DBpedia additional metadata could be added to a placemark which opens up totally new perspectives on content enrichment in ARML environments enabling new and exciting AR-applications.

We want to thank Martin Lechner from Salzburg based Mobilizy for a fruitful discussion we had so far on this topic.

BTW: check out the paper by Reynolds et al. (2010) from DERI on “Exploiting Linked Open Data for Mobile Augmented Reality“

Providers of first generation semantic technologies calculate rather basic “semantic networks” by co-occurency analysis which results sometimes in  disappointing results. Bearing in mind that Google just bought a company (“Google buys Metaweb“) which has been working on one of the largest knowledge bases in the world, we could assume that some of the last miles towards a semantic search engine can be achieved by applying thesauri or other structured knowledge bases.

A demo application was recently developed by PoolParty team where one can find out how thesauri will improve search results on top of second generation semantic technologies. With PoolParty SKOS based controlled vocabularies can be managed and also can be enriched with linked data. PoolParty Tag & Content Recommender analyzes virtually any text or website to recommend corresponding tags, concepts from (in this case) STW (Standard Thesaurus für Wirtschaft), DBpedia and respective articles from Wikipedia.

STW which was developed by the German National Library of Economics (ZBW) provides vocabulary on any economic subject: about 6,000 standardized subject headings and about 18,000 entry terms to support individual keywords.

This background knowledge is used in this demo app to improve the search for similar documents dramatically:

Similarity between two documents can be calculated not only on a key-phrase basis but also on a rather conceptual basis. Even if two documents do not have one single word or phrase in common they can be identified as “similar documents”.

This can be achieved because thousands of important relations between economic subjects are represented in the domain specific thesaurus. Thus, in this special case best results are achieved with documents from economics (for instance from Econstor) but of course for other recommender systems thesauri from other domains can be used instead of STW.

Nevertheless, also this approach can be improved and this development is underway: SKOS thesauri enriched with Linked Data do an even better job. This kind of third generation semantic technologies are currently developed by LASSO project and LOD2 project, two innovative projects in the area of linked data and the semantic web.

Let us first step a back a bit and have a look what Dataset Dynamics are and why this is important. In the Web of Linked Data we typically deal with datasets, for example, from the biomedical domain or the media industry on the one hand, and entities, such as a certain protein or people on the other. For the entity-level case established HTTP caching mechanism can be leveraged (see the Caching Tutorial and Things Caches Do). Further, with Memento, a HTTP-based versioning mechanisms has been proposed as well as implemented, adding a “time dimension” to HTTP (see Fig. 1).

Fig. 1 Memento Framework (Source: "An HTTP-Based Versioning Mechanism for Linked Data" Herbert Van de Sompel, Robert Sanderson, Michael Nelson, Lyudmila Balakireva, Harihar Shankar, Scott Ainsworth, LDOW 2010)

Dataset-level changes

However, tackling dataset-level changes is a rather new field with no agreed-upon, even less standardised solution handy. The main problem is that a dataset typically talks about many thousands to millions of distinct entities, which makes it impractical to apply entity-level solutions for a range of use cases, such as link maintenance or replication (see also Fig. 2).

Fig. 2 Change frequency vs. change volume

I often hear these days: “it seems there is no solution for handling of dataset-level changes”; nevertheless, I think quite the opposite it true. There are plenty of proposed solutions from both the academia and practitioners, targeting different challenges in the areas of:

• Change discovery – how do I find out about about dataset changes?
• Propagating changes - if there is a change, how is the change communciated to a consumer?
• Change semantics – how do I learn what has changed (has been added, removed, etc.)?

Some proposals on the table are integrated approaches (such as DSNotify, SemanticPingback, Talis Changeset) while others focus on certain aspects (like the dady vocabulary for discovery or the Graph Update Ontology for change semantics) or deal concrete environments, for example sparqlPuSH for SPARQL enpdoints.

A Dataset Dynamics Manifesto

No matter on what (set of) solutions the community eventually agrees on to address the handling of dataset-level changes, it should adhere to the following principles:

• light-weight
• distributed and scalable
• standards-based

Obviously, a light-weight (and ideally RESTful) approach lowers the barriers to adoption and enables a quick uptake. When I say light-weight, I mean it both in terms of protocol and code. It should be easy to integrate in RDF stores and libraries and available in all common Web programming languages including but not limited to Java, PHP, .NET family, etc.

Just as the Web of Data is a globally distributed dataspace, handling of changes should be done in a distributed fashion. There will be many different publishers and consumers (such as agents, indexer, consolidator platforms, etc.) of datasets with different requirements and capabilities. A distributed approach can cope with this challenge in a cost- and performance-efficient way. Tightly connected to this: It has to scale. Today, we’re dealing with some hundreds of LOD datasets. In the next couple of years, this will likely explode into the millions and hence one needs to be able to deal with such a growth. The same, just sooner, is true for the number of consumers of the changes.

Last but not least the Dataset Dynamics solution should be based on standards. It doesn’t necessarily need to be RDF for all of the challenges as outlined above. For example, Atom offers a standardised, extensible and widely accepted format to propagate changes; to take this further Pubsubhubbub can be utilised to enable a standardised, distributed publisher-subscriber scheme (Fig 3.)

Fig. 3 Pubsubhubbub - a standard-based, distributed publisher-subscriber-hub system (Source: http://docs.google.com/present/view?id=ajd8t6gk4mh2_34dvbpchfs)

As I’ve outlined above, it might still be too early for a conclusion on how to deal with dataset-level changes. However, people interested in this area have gathered already in the Dataset Dynamics group where solutions are discussed and implemented, potentially leading to a W3C standardisation work.

As an aside: in case you’re at the WWW2010 in Raleigh (NC, USA) these days, you may want to join the break-out meeting on Dataset Dynamics during the W3C Linked Open Data track on 29 April 2010.

(This blog post was written by Michael Hausenblas)

Linked Data itself has grabbed a lot of attention inside the Semantic Web community recently. But what about the outside perspective? Could linked data be called the killer app for the Semantic Web?

I foresee two things happening with Linked Data. One is from the web development perspective (the so-called Web 2.0 developers) and the other is from the enterprise perspective. The web development community will sooner than later realize that Linked Data will enable easy integration of data and therefore will ease the pain of consuming data from different data sources. Thanks to big organizations such as BBC, New York Times, Reuters, Best Buy, etc. web developers will start paying attention to this “new thing” called Linked Data.

What we need is that the inside Semantic Web community starts to create applications on top of current Linked Data so when the outside web development community starts to pay attention, they have something to chew on. We (the semantic web community) needs to start speaking the web development language. There is still a big gap. I have had personal experiences with people in the web development community who think that RDF is XML and because they hate XML, they will never consider it. This is false and this is something that we need to change.

From the enterprise perspective, Linked Data is another data integration solution. Data integration has been a problem since day one of relational databases. I believe enterprises will be open to consider new solutions with new technologies. I’m hoping to see new startups tackling the enterprise domain. Imagine being able to query “get all my clients from cities whose population is greater than 1 million” even though I don’t have the data about population of cities in my database.

Is Linked Data the killer app for the Semantic Web? Before I answer that, I would like to ask, what was the killer app of the Web? Was it the browser? Was it e-commerce? Was it search? Was it Amazon or Ebay or Google? I believe Linked Data will enable new killer apps, apps that are only possible thanks to Linked Data. The browser was only possible because of HTML. So let’s ask ourselves what is possible because of Linked Data, and there we will find our killer app.

One of the core deficiencies of the young open data cloud is the little amount of interlinks between datasets. Is it just a matter of time to overcome this or are there other measures needed to turn the existing datasets into a true giant global graph?

I like to remind myself that this new wave of semantic web technologies is an extension of the current web. Therefore we should analyze how the web evolved in the beginning. Initially, everything were a bunch of documents on the web in which people manually created links to other documents. When Google started, it created an incentive to offer quality links between documents. This also created data hubs. If you write a blog post about a book, most probably you will link to the web document of that book either on Amazon and/or Wikipedia. I believe that this will happen with Linked Data. Once there is an incentive to create quality links, these links will start to show up. And then users will start linking to the data hubs of their interest.

Open Governmental Data is a big issue at the moment. The US and UK government have started to apply Linked Data principles to turn this vision into reality. Lots of other countries are following. What do you expect from this trend?

I believe that Linked Data will take off thanks to the initiative of governments. We always talk about the chicken and egg problem of the semantic web. Once we have organizations that don’t even think about it and are just interested in putting their data on the web, the semantic web will start to grow. If Bookstore ABC puts their data on the web, it may not be so meaningful. But if the US and UK government puts their data on the web, following the Linked Data principles, then people can wake up and say “ok, so this is for real. Let me start paying attention to this”.

You are one of the chairs of the Triplification Challenge 2010. Can you give us a brief insight what to expect from this year’s challenge? What are the conditions to participate?

The Triplification Challenge this year has grown and is very exciting. For the first time, it is offering two different tracks.

The first track, the Open Track will accept submissions on three areas 1) new datasets that are published following the Linked Data principles and that show potential benefit, 2) generic methods, mechanisms and approaches of creating Linked Data from legacy datasets and 3) applications that make use of Linked Data.

The second track is the New York Times track which will accept submissions of applications that make use of the New York Times Linked Data and one or more government dataset. The objective is to create an application powered by Linked Data that would be of interest to any constituent of that government.

I personally believe that the year 2010 is the year of creating Linked Data applications and the Triplification Challenge is the way to be part of it.