Modeling Global Semantics for Question Answering over Knowledge Bases
  Semantic parsing, as an important approach to question answering over
knowledge bases (KBQA), transforms a question into the complete query graph for
further generating the correct logical query. Existing semantic parsing
approaches mainly focus on relations matching with paying less attention to the
underlying internal structure of questions (e.g., the dependencies and
relations between all entities in a question) to select the query graph. In
this paper, we present a relational graph convolutional network (RGCN)-based
model gRGCN for semantic parsing in KBQA. gRGCN extracts the global semantics
of questions and their corresponding query graphs, including structure
semantics via RGCN and relational semantics (label representation of relations
between entities) via a hierarchical relation attention mechanism. Experiments
evaluated on benchmarks show that our model outperforms off-the-shelf models.
