Knowledge Completion for Generics using Guided Tensor Factorization
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Given a knowledge base (KB) rich in facts about common nouns or generics, such as "all trees produce oxygen" or "some animals live in forests", we consider the problem of deriving additional such facts at a high precision. While this problem has received much attention for named entity KBs such as Freebase, little emphasis has been placed on generics despite their importance for capturing general knowledge. Different from named entity KBs, generics KBs involve implicit or explicit quantification, have more complex underlying regularities, are substantially more incomplete, and violate the commonly used locally closed world assumption (LCWA). Consequently, existing completion methods struggle with this new task. We observe that external information, such as relation schemas and entity taxonomies, if used correctly, can be surprisingly powerful in addressing the challenges associated with generics. Using this insight, we propose a simple yet effective knowledge guided tensor factorization approach that achieves state-of-the-art results on two generics KBs for science, doubling their size at 74%-86% precision. Further, to address the paucity of facts about rare entities such as oriole (a bird), we present a novel taxonomy guided submodular active learning method to collect additional annotations that are over five times more effective in inferring further new facts than multiple active learning baselines.
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