Automatic Construction of Inference-Supporting Knowledge Bases

@inproceedings{Clark2014AutomaticCO,
  title={Automatic Construction of Inference-Supporting Knowledge Bases},
  author={Peter Clark and Niranjan Balasubramanian and Sumithra Bhakthavatsalam and Kevin Humphreys and J. Clint Kinkead and Ashish Sabharwal},
  year={2014}
}
  • Peter Clark, Niranjan Balasubramanian, +3 authors Ashish Sabharwal
  • Published 2014
While there has been tremendous progress in automatic database population in recent years, most of human knowledge does not naturally fit into a database form. For example, knowledge that "metal objects can conduct electricity" or "animals grow fur to help them stay warm" requires a substantially different approach to both acquisition and representation. This kind of knowledge is important because it can support inference e.g., (with some associated confidence) if an object is made of metal… CONTINUE READING

Results and Topics from this paper.

Key Quantitative Results

  • In preliminary experiments, the inference system currently scores 60% on the target task (nondiagram multiple choice questions on the NY Regents 4th grade science exam), using a small (68 question) dataset of unseen test data, 5% above the best retrieval-based bag-of-word method we have developed.