document: SemTag and seeker : bootstrapping the semantic web via automated semantic annotation This paper describes Seeker , a platform for large-scale text analytics , and SemTag , an application written on the platform to perform automated semantic tagging of large corpora . We apply SemTag to a collection of approximately 264 million web pages , and generate approximately 434 million automatically disambiguated semantic tags , published to the web as a label bureau providing metadata regarding the 434 million annotations . To our knowledge , this is the largest scale semantic tagging effort to date . We describe the Seeker platform , discuss the architecture of the SemTag application , describe a new disambiguation algorithm specialized to support ontological disambiguation of large-scale data , evaluate the algorithm , and present our final results with information about acquiring and making use of the semantic tags . We argue that automated large scale semantic tagging of ambiguous content can bootstrap and accelerate the creation of the semantic web .
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