EARL: Joint Entity and Relation Linking for Question Answering over Knowledge Graphs

TL;DR

This paper introduces EARL, a joint entity and relation linking framework for question answering over knowledge graphs, outperforming existing methods through two innovative strategies: GTSP-based and machine learning-based.

Contribution

The paper presents a novel joint linking framework with two distinct solution strategies, enhancing accuracy over state-of-the-art approaches.

Findings

Both strategies outperform current state-of-the-art methods.

The GTSP-based approach uses approximate solvers for efficiency.

The machine learning approach exploits connection density for improved predictions.

Abstract

Many question answering systems over knowledge graphs rely on entity and relation linking components in order to connect the natural language input to the underlying knowledge graph. Traditionally, entity linking and relation linking have been performed either as dependent sequential tasks or as independent parallel tasks. In this paper, we propose a framework called EARL, which performs entity linking and relation linking as a joint task. EARL implements two different solution strategies for which we provide a comparative analysis in this paper: The first strategy is a formalisation of the joint entity and relation linking tasks as an instance of the Generalised Travelling Salesman Problem (GTSP). In order to be computationally feasible, we employ approximate GTSP solvers. The second strategy uses machine learning in order to exploit the connection density between nodes in the knowledge graph. It relies on three base features and re-ranking steps in order to predict entities and relations. We compare the strategies and evaluate them on a dataset with 5000 questions. Both strategies significantly outperform the current state-of-the-art approaches for entity and relation linking.