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

Now that Freebase is available as Linked Data a big question that comes to mind is whether these two major projects will move to assimilate one another. DBpedia and Freebase – two endeavors primarily focused on curating unstructured and semi-structured data about everything and releasing it back into the wild (with structure) – get the bulk of their information from Wikipedia, so the amount of topical overlap is assumed to be extremely high. DBpedia gains new information when it extracts data from the latest Wikipedia dump, whereas Freebase, in addition to Wikipedia extractions, gains new information through its userbase of editors.

Continue reading Cross-Pollinating DBpedia and Freebase

At ISWC2008 Freebase released its new RDF service for generating RDF representations of Freebase topics, allowing Freebase to be used as Linked Data! To obtain the RDF data for a topic send a GET request to http://rdf.freebase.com/rdf/some.topic.id where "some.topic.id" is replaced by the desired topic identifier (slashes in the identifier must be replaced by dots). Topic data can be represented as N3, RDF/XML or Turtle depending on the preferences expressed in your client's HTTP Accept header. Try it out with the Freebase topic Semantic Web.

Continue reading Freebase Officially Linked Data with Release of RDF Service