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Automatic knowledge extraction from documents

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4 Author(s)
J. Fan ; IBM Research Division, Thomas J. Watson Research Center, Yorktown Heights, NY, USA ; A. Kalyanpur ; D. C. Gondek ; D. A. Ferrucci

Access to a large amount of knowledge is critical for success at answering open-domain questions for DeepQA systems such as IBM Watson™. Formal representation of knowledge has the advantage of being easy to reason with, but acquisition of structured knowledge in open domains from unstructured data is often difficult and expensive. Our central hypothesis is that shallow syntactic knowledge and its implied semantics can be easily acquired and can be used in many areas of a question-answering system. We take a two-stage approach to extract the syntactic knowledge and implied semantics. First, shallow knowledge from large collections of documents is automatically extracted. Second, additional semantics are inferred from aggregate statistics of the automatically extracted shallow knowledge. In this paper, we describe in detail what kind of shallow knowledge is extracted, how it is automatically done from a large corpus, and how additional semantics are inferred from aggregate statistics. We also briefly discuss the various ways extracted knowledge is used throughout the IBM DeepQA system.

Note: The Institute of Electrical and Electronics Engineers, Incorporated is distributing this Article with permission of the International Business Machines Corporation (IBM) who is the exclusive owner. The recipient of this Article may not assign, sublicense, lease, rent or otherwise transfer, reproduce, prepare derivative works, publicly display or perform, or distribute the Article.  

Published in:

IBM Journal of Research and Development  (Volume:56 ,  Issue: 3.4 )