Relation Prediction for Answering Natural Language Questions over knowledge Graphs

Large-scale knowledge graphs (KGs) play a critical role in question answering over KGs (KGs-QA). Despite of large scale, KGs suffer from incompleteness which results in the low-precision of KGs-QA. Since existing researches of relation prediction process each triple independently, the hidden relations which are inherently present in the neighborhood can not be captured. Complementarily, to capture both entity and relation features in a given entity's neighborhood, an entity importance estimation network of attention-based graph embedding is proposed, which consists of the attention-based graph embedding module and the entity importance estimation module. The new embedding of an entity from its n-hop neighbor is learned by an attention-based graph embedding module, and then the learned embedding is integrated into the entity importance estimation module to find entities of high importance in n-hop neighbors of the central entity. Multi-hop relations are encapsulated and an auxiliary edge of n-hop neighbors is introduced, which realizes the relation prediction task. In addition, we are the first to realize KGs-QA while realizing relation prediction, which alleviates the phenomenon of missing relations and the low-precision problem of KGs-QA. As expected, KGs-QA's F1 increases significantly when relation prediction is invoked. In 80% missing relation, we observe a F1 increase of 21, 17, 18 and 16 points over the no-relation prediction configuration for SQ, WQ, GQ and QALD-5, respectively.