# Integrating Subgraph-aware Relation and DirectionReasoning for Question Answering

Source: [Integrating Subgraph-aware Relation and DirectionReasoning for Question Answering](<https://www.sota2.com/research/paper/68fa69b11e209131cea237a2>)

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

Data updated: 2025-10-25T23:05:39.232Z

Authors: Xu Wang, Shuai Zhao, Bo Cheng, Jiale Han, Yingting Li, Hao Yang, Ivan Sekulic, Guoshun Nan

Published: 2021-04-01T03:04:36.000Z

## Abstract

Question Answering (QA) models over Knowledge Bases (KBs) are capable of providing more precise answers by utilizing relation information among entities. Although effective, most of these models solely rely on fixed relation representations to obtain answers for different question-related KB subgraphs. Hence, the rich structured information of these subgraphs may be overlooked by the relation representation vectors. Meanwhile, the direction information of reasoning, which has been proven effective for the answer prediction on graphs, has not been fully explored in existing work. To address these challenges, we propose a novel neural model, Relation-updated Direction-guided Answer Selector (RDAS), which converts relations in each subgraph to additional nodes to learn structure information. Additionally, we utilize direction information to enhance the reasoning ability. Experimental results show that our model yields substantial improvements on two widely used datasets.

## 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/2104.00218>)

[PDF](<https://arxiv.org/pdf/2104.00218.pdf>)