Automatic interpretation of loosely encoded knowledge
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Knowledge is critical for a variety of artificial intelligence problems. A key challenge in using knowledge-based systems is how to align one's encoding with the idiosyncrasies in the existing knowledge base. We call such misalignments "loose speak". We found that loose-speak occurs frequently in knowledge base interactions with such regularity that it can be interpreted automatically by a machine. We created a loose-speak interpreter based on a unified approach that is capable of interpreting the different forms of loose speak, and we evaluated it through empirical studies in different domains and on different tasks.