Semantic Web Company

As a matter of fact still a lot of “semantic technologies” are around which do nothing else than pure statistical analysis of text. Sure, this is better than simple full text search but there are still quite a lot of opportunities to improve search, especially when it comes to more sophisticated applications like “similarity search”, the search for similar documents to enable cross-reading or recommendation systems.

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.

As a matter of fact things change – the Web of Data is no exception in that respect. While some sources, such as Twitter, are intrinsically dynamic, others change every now and then, potentially in unforeseeable intervals. In the recent Talis Nodalities Magazine, we made a case for Keeping up with a LOD of changes; here I’m going to elaborate a bit more on the current state of Dataset Dynamics and its challenges.

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)

Juan Sequeda, co-chair of the Triplification Challenge 2010 and one of the core figures in the Linked Data movement, gives us his view how the Semantic Web might evolve. His central message: “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.”

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.

Uberblic.org connects structured data from the web. The Berlin-based inventor Georgi Kobilarov gives a brief insight into the mashup service and talks about the challenges when it comes to build applications upon linked data.

You have recently published the service uberblic.org, a Linked Data mashup editor. What was your motivation to develop this tool?

Uberblic.org provides an integrated view of web data. Our goal is to integrate all the structured data on the web, and give web-developers a single point to access to that reconciled data. More than that, we will open up the tools we use to manage the data sources to the community, so that the people can help us curating that repository of free data. We re-publish all the data we import as Linked Data, under the licenses of the original data publishers.

Some of the data sources we import are available in the Linked Open Data cloud as well, but many are not. Linked Data is an elegant way to publish data in a distributed way on the web, but consuming it from that distributed cloud is – at least – impractical. In every real-world application using linked data from the web I’ve seen, organizations built up internal copies of the cloud, and often even reconcile linked data sources. They build their own Linked Data proxies. Uberblic.org helps those users by providing one public proxy for data from the web. Many of our sources get monitored for data changes, and the according data in uberblic is updated in real-time.

Can you give us a brief insight how the tool works? What technology is is built on?

My company, Uberblic Labs, has developed a data integration platform that we use to power uberblic.org. We call it the Uberblic Platform (the name uberblic is derived from the German “Überblick” – English “overview”). This platform enables us to do the full process of “data fusion”: Importing and converting external data sources, mapping the data schemas to a central ontology, filtering out data errors, automatically suggesting duplicates to the user, and merging data from different sources into a single, reconciled representation.

Structured and semi-structured data from the web is an excellent use case for our software platform, since there we come across all the interesting cases of real-world data heterogeneity. But what I think is especially powerful and yet missing in other Linked Data projects I know, is the ability to subscribe to update-feeds. We do that extensively, fetching updates in real-time from Wikipedia and the like.

Our platform is built in Scala and runs a on cluster of machines, with workers communicating through a messaging system. We developed an RDF storage layer on top of a distributed key-values store for storing all provenance information used in the extraction process, currently around 100 million named graphs for uberblic.org. That storage layer does not directly provide SPARQL access, so we push all the output data into a SPARQL endpoint hosted by Talis as well.

What have been the biggest challenges in tackling the integration issues of dispersed data?

It was quite a steep learning curve to do Linked Data not only in an academic environment, but in a reliable, industry-strength set-up. In academia, there was always the excuse that things are just research prototypes. Now that excuse is gone. That’s also where it becomes necessary to manually clean up data. And there are two ways to do that: Either you enable the users to change facts directly in your repository after you have imported the external data (that is what Freebase does), or you facilitate clean-up cycles in the original data source and fetch these updates in real-time. That is what we do.

I believe that data publishing must happen in a distributed style, because then each data source gets taken care of by a specialized group of people using specialized tools. And it’s what you see not only on the web, but also inside organizations and enterprises. But consuming data trough centralized APIs is more than just convenient. We all use Google
or another search engine as a central access point to web pages which are published in a distributed way all over the web, don’t we? Can you imagine today researching a topic on the web without the centralization power of search engines, just by following links across web sites, like in the old days?

When we built the Uberblic Platform, some of the things I imagined to be large headaches, like schema mapping, turned out to work really well. Those pathologic cases you often see in academic “challenges” are – well – pathologic. It’s not necessary to solve them fully automatically through super-intelligent algorithms. Much more important than the sophistication of your algorithms are well designed workflows so that the user becomes a part of the solution. And that’s not about crowd-sourcing or swarm intelligence, the editorial curating of schema mappings and object reconciliation can be done just by a small team of people. If they have the right set of tools.

What are the next plans with uberblic.org? Where will the journey go?

Uberblic.org will continue to integrate more interesting and useful data sources from the web, and we will start making more APIs available to web developers to build their applications on top. We are also looking for partners who are interested in developing applications and have been struggling in the past to get the cross-source data from the web they need.

The work on improving uberblic.org will also benefit our Uberblic Platform, and hence our clients who use that same software for integrating organizational data sources with each other and with the web of data.

About Georgi Kobilarov

Georgi is founder and managing director of Uberblic Labs, a company based in Berlin specialized in Linked Data integration. He worked as a research associate in the Web-based Systems Group at Freie Universität Berlin and as a visiting researcher at Hewlett Packard Labs Bristol. As co-founder and lead developer of DBpedia, he was also a day-one contributor to the Linking Open Data project. Georgi is consulting with the BBC on several Linked Data related projects. He organizes the Web of Data Meetup London, a bi-yearly gathering of the UK Linked Data community. Georgi graduated with a Diplom in business administration from Freie Universität Berlin and has many years of work experience as a software developer. Visit his blog: http://blog.georgikobilarov.com

Marco Neumann is an Information Scientist and CEO of KONA a consulting and technology service company based in New York City. The Semantic Web activist is an invited expert to the W3C HTML 5 working group. He recently started a discussion on the challenges and difficulties in bringing the Semantic Web into business. SWC asked him for some additional comments.

Marco, you recently initiated a discussion in a Google Group on the difficulty to change Semantic Web standards. What was the background of the discussion? Where do you perceive a need for action?

It’s not so much about changing this existing standards but the challenge to bring them into the world of practitioners and standards developers. The language used in W3C recommendations quite frequently requires advanced topic knowledge and familiarity with the jargon of the discussion about the respective technologies. I recently discussed this with a senior standards maven at the W3C and got the answer that the recommendations can’t be changed retrospectively and that they are intended to be used primarily by vendors for implementation purposes.

Well this might be the case but I also got the impression that Tim Berners-Lee objective for the W3C is primarily to meet the needs of a larger community. And the W3C took this into account for most of the Semantic Web recommendations in the past. Something I still find amazing is the fact that the work process at the W3C is partially and the recommendations are entirely publicly accessible. Though we definitely still need more and better tools to work with semantic web data, higher quality documentation and last but not least more user adoption on the web.

Critics of the Semantic Web often refer to the slow uptake of Semantic Web standards by industry. Is standards adoption actually a valid and sufficient metric to evaluate the maturity of a standard? What would be needed to accelerate the uptake?

I think we might see a similar scenario to the uptake of HTML in the early 90s, a relatively small number of technology mavens will pave the way towards making the Semantic Web more attractive as a technology solution for a wide range of applications and will successfully publish open data before we see business application developers make use of Semantic Web standards.

The availability of trustable and quality approved RDF data is crucial for the success of the Semantic Web. Given the fact that the aggregation business on the WWW is highly concentrated the corresponding formula is simple: If Google just consumes but does not give back RDF the Semantic Web won’t scale. Do you agree?

Yes and no. Yes we need better and more semantic data on the Web, but we will also need better ways to deal with trust in a lightweight and web friendly fashion. I currently see a number of semi automated approaches emerging  that could scale on the web. An example are distributed user based recommendation systems to validate authenticity, open Wikipedia style community evaluation and content curation a la freebase. Increased public accountability for data producers might be an interesting venue as well. In regards to Google I’d say web search engines will go where the web goes. A problem I might see arising is that web search engines will initially develop their own standards to deal with the emerging Semantic Web and confuse users on the web or might pursue a time consuming power play with the W3C. I see a little bit of that in the current discussion in the HTML 5 working group.

As we know from social sciences technological standards are necessary but always incomplete and unsatisfactory. From a standards design and outreach perspective: What would it need to make the Semantic Web flourish?

I’m not sure if we really know all that much about the laws of innovation and the evolution of technology standards at this point. If we draw from the short experience with the World Wide Web I would come to the conclusion that innovation takes place in small to medium size teams that pursue an independent vision of how services should be delivered and how the technology should be designed. In addition Tim Berners-Lee’s encourages the production of lots and lots of data to bootstrap the Semantic Web and create a pull for services in the industry. And indeed we really see some traction for example with the Linked Open Data and Open Government initiatives. It’s definitely an exciting time to be on the Semantic Web!

About Marco Neumann

Marco Neumann is an Information Scientist and CEO of KONA a consulting and technology service company based in New York City. KONA provides semantic technologies to businesses solutions and adds value to products and services in a highly networked economy. In addition Marco currently acts as an Invited Expert to the W3C on the HTML 5 working group and is the director of the global semantic social network lotico.com.

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.

George Anadiotis is an expert on artificial intelligence with academic roots at the Vrije Universiteit, Amsterdam. In February 2009 he took the position as R&D Director at the Greek technology company IMC. I met him in September at I-SEMANTICS 2009 where he and his team contributed to the Triplification Challenge. In their paper Linked Data for the Masses they were pondering about the pragmatic value of Linked Data from an inbound and outbound perspective.  In his words:

We started experimenting with the technical infrastructure needed and created some proof-of-concept applications. Part of this work was enabling Linked Data access for the front-end infrastructure we used, Liferay portal. We decided on the appropriate vocabularies for the type of content we wanted to publish (FOAF, SIOC and MOAT mainly), delved on the internals of Liferay and used D2R to map its relational database to the vocabularies of choice, also using techniques to improve performance as much as possible. Since Liferay itself is also based on the notion of communities, we thought our work would be more widely applicable and useful, so we chose to submit it for review at the Triplification Challenge and make it available to the community as open source software. Our applications have gradually matured and are about to be deployed in our commercial projects, while at the same time we are now making the Liferay Linked Data Module available as a Sourceforge project and we are working with Liferay management in order to disseminate this effort to the community and also include it in a future release of the software.

Read the full interview here.

The Semantic Web Company created a series of online seminars (aka webinars) for you to acquire basic and practical knowledge about methologies, technologies and standards of the Semantic Web. In 90 minute sesseions we will cover the business aspects of topics such as content engineering, Knowledge Management, business intelligence, e-Business and more.

In order to allow for a high level of interaction, the attendance is limited to ten participants and ample time for questions and discussion with our experts is designated. Each webinar works as a stand-alone module, so you can pick and choose some of them or book the whole series of 6 webinars.

We’ll kick off with a session about Semantic Wikis on Thursday 22nd of October. A German language version will be held at 9 a.m., alternatively you can atted an English version at 6 p.m. CET.

Each Thursday we cover a different topic such as Semantic Search, Corporate Thesaurus Management, Text Mining on the Corporate Semantic Web, Linking Open Data and Semantic Advertising.

In order to participate you only need broadband access to the internet, Windows or a Mac and a fairly up-to-date browser. For detailed system requirement see the webinar overview.

We hope to talk to you in one or more of these sessions!

Some new releases around Apple´s iPhone family, like the new OS3.0 or the new 3G S have stimulated another big hype around this “little darling”. I took a look at another facet, namely: Has the Semantic Web entered the iPhone realm yet (or vice versa)? Experts have been talking about the need for semantically enhanced mobile applications for years, so let´s see, if they are in place already.

Searching for “semantic web” in the AppStore delivers six results, one of them called “SemanticWb” is obviously an interesting match. The application “extracts current life sciences and health care knowledge and place them conveniently at your fingertips on your iPhone”. The application offers search suggestions and moderated search and retrieves articles from PubMed or genetic disorders which are related to the search term. Good start, this is a neat iPhone application which should be interesting for medical doctors and related professions.

Another application on the iPhone which is related to the semantic web is the “English wordnet dictionary” based on WordNet from Princeton University.

So, not much semantic web on the iPhone so far – I thought until Evriverse was released some weeks ago. The iPhone version of evri.com offers a new way to find connections between all kind of things. Similar to OpenCalais Evri can extract people, places, organisations, products etc. from unstructured information like news or blogs. The innovation around Evriverse is the way how complex search queries around “anything” can be formulated by just touching the screen. For example, if you are looking for information about “Tim Berners-Lee” the application not only offers auto-complete but also suggests related people, organisations etc. to refine any search query. Such relations are updated constantly and are based on the semantic analysis of news and blogs.

Evriverse offers the most comfortable way to do news research on the iPhone today. It shows how semantic technologies can enhance user experience on a mobile device and it will path the way to more semantic (web) apps on the iPhone.