Learning Systems of Concepts with an Infinite Relational Model
Learning Systems of Concepts with an Infinite Relational Model
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发表时间：
2006-07
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通讯作者：
Charles Kemp;J. Tenenbaum;T. Griffiths;Takeshi Yamada;N. Ueda
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作者：
Charles Kemp;J. Tenenbaum;T. Griffiths;Takeshi Yamada;N. Ueda
概念之间的关系在语义知识中占有很大的比重。我们提出了一个非参数贝叶斯模型，发现系统的相关概念。给定涉及多个实体集的数据，我们的模型发现每个集合中的实体类型以及可能或可能的类型之间的关系。我们将我们的方法应用于四个问题：聚类对象和功能，学习本体，发现亲属系统，并发现政治数据的结构。
Relationships between concepts account for a large proportion of semantic knowledge. We present a nonparametric Bayesian model that discovers systems of related concepts. Given data involving several sets of entities, our model discovers the kinds of entities in each set and the relations between kinds that are possible or likely. We apply our approach to four problems: clustering objects and features, learning ontologies, discovering kinship systems, and discovering structure in political data.