Graph Centrality Measures for Boosting Popularity-Based Entity Linking

TL;DR

This paper evaluates five graph centrality measures to improve entity linking accuracy, finding that simple measures like Degree centrality can outperform more complex algorithms across various datasets.

Contribution

It introduces the application of multiple centrality measures to enhance entity disambiguation in graph-based entity linking systems.

Findings

Degree centrality outperforms other measures in accuracy and speed

Simple centrality measures can effectively boost entity linking results

Performance varies across different domains and datasets

Abstract

Many Entity Linking systems use collective graph-based methods to disambiguate the entity mentions within a document. Most of them have focused on graph construction and initial weighting of the candidate entities, less attention has been devoted to compare the graph ranking algorithms. In this work, we focus on the graph-based ranking algorithms, therefore we propose to apply five centrality measures: Degree, HITS, PageRank, Betweenness and Closeness. A disambiguation graph of candidate entities is constructed for each document using the popularity method, then centrality measures are applied to choose the most relevant candidate to boost the results of entity popularity method. We investigate the effectiveness of each centrality measure on the performance across different domains and datasets. Our experiments show that a simple and fast centrality measure such as Degree centrality can outperform other more time-consuming measures.