Towards Optimisation of Collaborative Question Answering over Knowledge Graphs

TL;DR

This paper proposes a machine learning-based method to optimize collaborative question answering over knowledge graphs, significantly improving answer accuracy while reducing feature and component usage.

Contribution

It introduces a feature selection and supervised learning approach for local optimization of CQA frameworks, enhancing performance and efficiency.

Findings

Answers more questions than existing frameworks.

Reduces features by 50%.

Decreases components used by 76%.

Abstract

Collaborative Question Answering (CQA) frameworks for knowledge graphs aim at integrating existing question answering (QA) components for implementing sequences of QA tasks (i.e. QA pipelines). The research community has paid substantial attention to CQAs since they support reusability and scalability of the available components in addition to the flexibility of pipelines. CQA frameworks attempt to build such pipelines automatically by solving two optimisation problems: 1) local collective performance of QA components per QA task and 2) global performance of QA pipelines. In spite offering several advantages over monolithic QA systems, the effectiveness and efficiency of CQA frameworks in answering questions is limited. In this paper, we tackle the problem of local optimisation of CQA frameworks and propose a three fold approach, which applies feature selection techniques with supervised machine learning approaches in order to identify the best performing components efficiently. We have empirically evaluated our approach over existing benchmarks and compared to existing automatic CQA frameworks. The observed results provide evidence that our approach answers a higher number of questions than the state of the art while reducing: i) the number of used features by 50% and ii) the number of components used by 76%.