Abstract: We propose a new method to extract semantic knowledge from the world-wide-web for both supervised and unsupervised learning using the Google search engine in an unconventional manner. The approach is novel in its unrestricted problem domain, simplicity of implementation, and manifestly ontological underpinnings. We give evidence of elementary learning of the semantics of concepts, in contrast to most prior approaches. The method works as follows: The world-wide-web is the largest database on earth, and it induces a probability mass function, the Google distribution, via page counts for combinations of search queries. This distribution allows us to tap the latent semantic knowledge on the web.
and from the paper itself...
A comparison can be made with the Cyc project [14]. Cyc, a project of the commercial venture Cycorp, tries to create artificial common sense. Cyc’s knowledge base consists of hundreds of microtheories and hundreds of thousands of terms, as well as over a million hand-crafted assertions written in a formal language called CycL [20]. CycL is an enhanced variety of first-order predicate logic. This knowledge base was created over the course of decades by paid human experts. It is therefore of extremely high quality. Google, on the other hand, is almost completely unstructured, and offers only a primitive query capability that is not nearly flexible enough to represent formal deduction. But what it lacks in expressiveness Google makes up for in size; Google has already indexed more than eight billion pages and shows no signs of slowing down.
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