Using a Knowledge Graph and Query Click Logs for Unsupervised Learning of Relation Detection

We present unsupervised methods for training relation detection models from the semantic knowledge graphs of the semantic web. The detected relations are used to synthetically generate natural language spoken queries against a back-end knowledge base. For each relation, we leverage the complete set of entities that are connected to each other in the graph with the specific relation, and search these pairs on the web. We use the snippets that the search engine returns to create examples that can be used as the training data for each relation. We further refine the annotations of these examples using the knowledge graph itself and a bootstrap approach. Furthermore, we use the URLs returned for the pair by the search engine to mine additional examples from the search engine query click logs. In our experiments, we show that, we can achieve relation detection models that perform 59.9% macro F-measure on the relations that are in the knowledge graph without any manual labeling, resulting in a comparable performance with supervised training.