Discovery of inference rules for question-answering
Natural Language Engineering
MindNet: acquiring and structuring semantic information from text
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Extracting and evaluating general world knowledge from the Brown corpus
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Types of common-sense knowledge needed for recognizing textual entailment
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AMIE: association rule mining under incomplete evidence in ontological knowledge bases
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Extracting meronyms for a biology knowledge base using distant supervision
Proceedings of the 2013 workshop on Automated knowledge base construction
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Our long-term interest is in machines that contain large amounts of general and scientific knowledge, stored in a "computable" form that supports reasoning and explanation. As a medium-term focus for this, our goal is to have the computer pass a fourth-grade science test, anticipating that much of the required knowledge will need to be acquired semi-automatically. This paper presents the first step towards this goal, namely a blueprint of the knowledge requirements for an early science exam, and a brief description of the resources, methods, and challenges involved in the semi-automatic acquisition of that knowledge. The result of our analysis suggests that as well as fact extraction from text and statistically driven rule extraction, three other styles of automatic knowledge base construction (AKBC) would be useful: acquiring definitional knowledge, direct 'reading' of rules from texts that state them, and, given a particular representational framework (e.g., qualitative reasoning), acquisition of specific instances of those models from text (e..g, specific qualitative models).