Simple Question Answering with Subgraph Ranking and Joint-Scoring
This work addresses a key bottleneck in knowledge-based question answering for applications requiring precise fact retrieval, though it is incremental in nature.
The paper tackled the problem of simple question answering over knowledge graphs by improving subgraph selection and leveraging subject-relation dependencies, achieving a new state-of-the-art accuracy of 85.44% on the SimpleQuestions dataset.
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.