There's a lot of confusion about what the semantic web is, exactly. There are so many definitions that I can't possibly unify everything in one article. Some people say it's all about linked data, RDF, and ontologies. Some people call it "Web 3.0." (I recently gave a keynote speech at a "Web 3.0" conference, where many of the people had confused "Web 3.0" with "Web in 3D." I'm sure many people in the audience wondered why I wasn't talking about the future 3D web and, instead, was talking about information.) Some people say it will lead us to the singularity. Rather than try to define these terms, I propose we abandon them. I propose we stop talking about complicated solutions and start talking about problems.

Continue reading What is the Semantic Web?

It has been twelve years since Tim Berners-Lee threw up his hands and said "it's all crap, let's do it over" and set off to create the Semantic Web. We've got very little to show for it so far. I firmly believe the work Semantic Web technologists are pursuing is important and the concepts will inevitably be realized and I very much want to see this research become viable. But things are not moving fast enough and the tack semantic researchers are taking simply isn't working.

Semantic Web technology is marred in a chicken/egg paradox. The technologies are generally not useful unless they are adopted and implemented on a large scale and people are not willing to invest in implementing them unless they are useful. This is exacerbated by the fact that there are very high technology, business, and social barriers to implementing the Semantic Web.

Continue reading RDF Semantic Web Research Isn't Working

A large percentage of content that users deal with on a daily basis is created by other users. Every minute more than 90,000 videos and images are uploaded to YouTube, Flickr and other social media websites, yet this represents a relatively small revenue percentage when compared with traditional media. We believe that one reason for this is the publisher's lack of ability to understand high density content that lacks the adequate description. With mobile platforms providing users with easy methods for rich media upload, this problem will rapidly increase.

Continue reading Bueda API Turns Tags into RDF URIs

Oracle 10g Release 2 / Oracle 11g offers a robust, scalable, secure platform to store RDF and OWL data. It allows efficient storage, loading and querying of semantic data. Queries are enhanced by adding relationships (ontologies) to data and evaluated on the basis of semantics. Data storage is in the form of RDF triples (Subject, Predicate, Object) and can scale up to millions of triples. The triples stored in the semantic data store are modeled as a graphed structure. All the data is stored in a single central schema allowing access to users for loading and querying data.

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Today on the Linking Open Data mailing list, Kingsley Idehen of OpenLink Software announced that he is preparing to load the entire LOD cloud into Virtuoso 6.0 Cluster Edition. The datasets are being added to a table on the ESW wiki, making it convenient for anyone doing Semantic Web research to get a hold of the datasets. Once all the datasets are added we should have a better idea of how much linked data there really is out there. This may also raise the bar for other triple stores and force them to develop methods for storing several billion triples.

Continue reading Calling All RDF Dumps

Ontologies classifying and describing services are called service ontologies. The currently used WSDL interface describes a service by specifying the operation name, inputs required for the service invocation, output of the service and its target address for invocation. Human intervention is required in this loop since the current architecture only addresses the syntactical aspects of Web services and lacks choreography mechanisms.

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Semantic Web services follow a life cycle, right from deployment to its invocation.

The life cycle of Semantic Web services comprises different stages like service modeling, service discovery, service definition and service delivery. The life cycle begins with modeling the web service and the service request by the provider and the consumer respectively. Web service descriptions are developed using models like OWL-S, WSMO. Service descriptions are used in the discovery stage on which discovery algorithms, matchmaking techniques are applied. Once a set of service providers are identified for a service requester, service definition takes place to select the concrete service. Finally, the concrete service is delivered to the service requester in the delivery phase.

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Freebase stores millions of entities and assertions about nearly every topic one can ponder (thanks are owed to their seed dataset – Wikipedia – and their amazing community). The amount of information that Freebase stores is incredible, and is a testament to what can be accomplished with the help of a dedicated community and a little (or a lot) of clever software engineering.

Continue reading Can Graphd Scale to Meet Semantic Web Demands?

I just stumbled upon a useful resource from Sindice (the Semantic Web search engine) called the Map of Data. The Map of Data lists sites that export their information via Microformats and embedded RDF (as well which format(s) the sites are using). Each site has been categorized and conveniently placed into lists. The categories include books, people, places, products and listings, social news, events, politics, and more. According to Sindice over 10 billion pieces of reusable information can already be found across 100 million pages.

Over the years I've noticed that the importance of algorithms and data tends to shift back and forth, depending on which at the time is hardest to duplicate (often from a business perspective). This effect seems to be caused by the availability or demand of one side increasing or decreasing, shifting the balance of importance to the other. At one point the world of software was dominated by the proprietary. The organization with the best software (backend, algorithms, etc) was the dominant entity and data (from say, a Web 2.0 perspective) was generally not the focus. This may have partly been the responsibility of a mindset formed during an era with very little storage space and before mass user activity on the Web.

Continue reading Algorithms vs. Data: The Seesaw Effect