Automatic interpretation of loosely encoded knowledge

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 knowledgebase. We call such alignments "loose speak". We found that loose speak occurs frequently in knowledgebase 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.