Question Answering over Knowledge Graphs with Neural Machine Translation and Entity Linking

TL;DR

This paper presents a novel KGQA method combining neural machine translation with entity linking, effectively addressing out-of-vocabulary issues and improving answer accuracy over large knowledge graphs like Wikidata.

Contribution

The approach integrates entity linking with NMT to handle unseen entities, reducing entity-related errors in question answering systems over knowledge graphs.

Findings

Outperforms pure NMT in entity handling

Reduces entity-related errors significantly

Maintains competitive accuracy with template dependence

Abstract

The goal of Question Answering over Knowledge Graphs (KGQA) is to find answers for natural language questions over a knowledge graph. Recent KGQA approaches adopt a neural machine translation (NMT) approach, where the natural language question is translated into a structured query language. However, NMT suffers from the out-of-vocabulary problem, where terms in a question may not have been seen during training, impeding their translation. This issue is particularly problematic for the millions of entities that large knowledge graphs describe. We rather propose a KGQA approach that delegates the processing of entities to entity linking (EL) systems. NMT is then used to create a query template with placeholders that are filled by entities identified in an EL phase. Slot filling is used to decide which entity fills which placeholder. Experiments for QA over Wikidata show that our approach outperforms pure NMT: while there remains a strong dependence on having seen similar query templates during training, errors relating to entities are greatly reduced.