# Simple Question Answering with Subgraph Ranking and Joint-Scoring

## TL;DR

This paper introduces a unified framework for simple question answering with knowledge graphs, emphasizing subgraph ranking and subject-relation dependency, leading to state-of-the-art accuracy on the SimpleQuestions dataset.

## Contribution

It proposes a novel subgraph ranking method and a joint scoring CNN model with a new loss function, enhancing simple question answering performance.

## Key findings

- Achieved 85.44% accuracy on SimpleQuestions dataset.
- Improved subgraph selection through a novel ranking method.
- Leveraged subject-relation dependency with a joint scoring CNN.

## Abstract

Knowledge graph based simple question answering (KBSQA) is a major area of research within question answering. Although only dealing with simple questions, i.e., questions that can be answered through a single knowledge base (KB) fact, this task is neither simple nor close to being solved. Targeting on the two main steps, subgraph selection and fact selection, the research community has developed sophisticated approaches. However, the importance of subgraph ranking and leveraging the subject--relation dependency of a KB fact have not been sufficiently explored. Motivated by this, we present a unified framework to describe and analyze existing approaches. Using this framework as a starting point, we focus on two aspects: improving subgraph selection through a novel ranking method and leveraging the subject--relation dependency by proposing a joint scoring CNN model with a novel loss function that enforces the well-order of scores. Our methods achieve a new state of the art (85.44% in accuracy) on the SimpleQuestions dataset.