Probabilistic Belief Embedding for Large-Scale Knowledge Population

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• 作者：Miao Fan ; Qiang Zhou ; Andrew Abel ; Thomas Fang Zheng…
• 关键词：Knowledge population ; Belief embedding ; Entity inference ; Relation prediction ; Triplet classification
• 刊名：Cognitive Computation
• 出版年：2016
• 出版时间：December 2016
• 年：2016
• 卷：8
• 期：6
• 页码：1087-1102
• 全文大小：
• 刊物主题：Neurosciences; Computation by Abstract Devices; Artificial Intelligence (incl. Robotics); Computational Biology/Bioinformatics;
• 出版者：Springer US
• ISSN：1866-9964
• 卷排序：8

文摘

BackgroundTo populate knowledge repositories, such as WordNet, Freebase and NELL, two branches of research have grown separately for decades. On the one hand, corpus-based methods which leverage unstructured free texts have been explored for years; on the other hand, some recently emerged embedding-based approaches use structured knowledge graphs to learn distributed representations of entities and relations. But there are still few comprehensive and elegant models that can integrate those large-scale heterogeneous resources to satisfy multiple subtasks of knowledge population including entity inference, relation prediction and triplet classification.