Unsupervised link discovery in multi-relational data via rarity analysis

A significant portion of knowledge discovery and data mining research focuses on finding patterns of interest in data. Once a pattern is found, it can be used to recognize satisfying instances. The new area of link discovery requires a complementary approach, since patterns of interest might not yet be known or might have too few examples to be learnable. We present an unsupervised link discovery method aimed at discovering unusual, interestingly linked entities in multi-relational datasets. Various notions of rarity are introduced to measure the "interestingness" of sets of paths and entities. These measurements have been implemented and applied to a real-world bibliographic dataset where they give very promising results.