# Modeling Global Semantics for Question Answering over Knowledge Bases

Source: [Modeling Global Semantics for Question Answering over Knowledge Bases](<https://www.sota2.com/research/paper/68fa69101e209131cea1ff4f>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-28T13:56:46.125Z

Authors: Peiyun Wu, Yunjie Wu, Linjuan Wu, Xiaowang Zhang, Zhiyong Feng

Published: 2021-01-05T13:51:14.000Z

## Abstract

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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2101.01510>)

[PDF](<https://arxiv.org/pdf/2101.01510.pdf>)