Leveraging Deep Neural Networks and Knowledge Graphs for Entity Disambiguation

TL;DR

This paper introduces a deep neural network-based model that leverages knowledge graphs to improve entity disambiguation by accurately measuring semantic relatedness, significantly reducing errors compared to previous methods.

Contribution

The paper presents a novel deep semantic relatedness model (DSRM) that directly trains on large-scale knowledge graphs to enhance entity disambiguation accuracy.

Findings

19.4% reduction in disambiguation errors on Dataset A

24.5% reduction in disambiguation errors on Dataset B

Outperforms state-of-the-art relatedness approaches

Abstract

Entity Disambiguation aims to link mentions of ambiguous entities to a knowledge base (e.g., Wikipedia). Modeling topical coherence is crucial for this task based on the assumption that information from the same semantic context tends to belong to the same topic. This paper presents a novel deep semantic relatedness model (DSRM) based on deep neural networks (DNN) and semantic knowledge graphs (KGs) to measure entity semantic relatedness for topical coherence modeling. The DSRM is directly trained on large-scale KGs and it maps heterogeneous types of knowledge of an entity from KGs to numerical feature vectors in a latent space such that the distance between two semantically-related entities is minimized. Compared with the state-of-the-art relatedness approach proposed by (Milne and Witten, 2008a), the DSRM obtains 19.4% and 24.5% reductions in entity disambiguation errors on two publicly available datasets respectively.