Discovering structure in the universe of attribute names

Abstract

Recently, search engines have invested significant efiort to answering entity{attribute queries from structured data, but have focused mostly on queries for frequent attributes. In parallel, several research efforts have demonstrated that there is a long tail of attributes, often thousands per class of enti-ties, that are of interest to users. Researchers are beginning to leverage these new collections of attributes to expand the ontologies that power search engines and to recognize entity{ attribute queries. Because of the sheer number of potential attributes, such tasks require us to impose some structure on this long and heavy tail of attributes. This paper introduces the problem of organizing the at-tributes by expressing the compositional structure of their names as a rule-based grammar. These rules offer a compact and rich semantic interpretation of multi-word attributes, while generalizing from the observed attributes to new un-seen ones. The paper describes an unsupervised learning method to generate such a grammar automatically from a large set of attribute names. Experiments show that our method can discover a precise grammar over 100,000 at-tributes of Countries while providing a 40-fold compaction over the attribute names. Furthermore, our grammar en-ables us to increase the precision of attributes from 47% to more than 90% with only a minimal curation e-ort. Thus, our approach provides an effcient and scalable way to ex-pand ontologies with attributes of user interest.