Modeling Global Semantics for Question Answering over Knowledge Bases

Semantic parsing, as an important approach However, the state-of-the-art semantic parsing approaches to question answering over knowledge bases utilize relational semantics of query graphs with pay little attention (KBQA), transforms a question into the complete to the structure semantics of a question. The structure query graph for further generating the correct logical semantics is an important part of the whole semantics query. Existing semantic parsing approaches of questions (e.g., Figure 1), especially in complex questions mainly focus on relations matching with paying where the complexity of a question often relies on its complicated less attention to the underlying internal structure structure. As a result, existing works only consider relational of questions (e.g., the dependencies and relations semantics cannot always perform complex questions between all entities in a question) to select the better. So it is necessary to pay more attention to the structure query graph. In this paper, we present a relational semantics of questions together with relational semantics graph convolutional network (RGCN)-based model when semantic parsing in KBQA. However, to model multirelational gRGCN for semantic parsing in KBQA.