Unsupervised relation detection using automatic alignment of query patterns extracted from knowledge graphs and query click logs

Traditional methods for building spoken language understanding systems require manual rules or annotated data, which are expensive. In this work, we present an unsupervised method for bootstrapping a relation classifier, which identifies the knowledge graph relations present in an input query. Unlike existing work, we utilize only one knowledge graph entity instead of two for mining relevant query patterns from query click logs. As a result, the mined patterns can be used to infer both explicit relations (where the objects of the relations are expressed in the queries) and implicit relations (where the objects of the relations are being asked about). Using only the mined queries, the final classifier achieves an F-measure of 55.5%, which is significantly higher than the previous unsupervised learning baselines.