Framing Named Entity Linking Error Types

Abstract

Named Entity Linking (NEL) and relation extraction forms the backbone of Knowledge Base Population tasks. The recent rise of
large open source Knowledge Bases and the continuous focus on improving NEL performance has led to the creation of automated
benchmark solutions during the last decade. The benchmarking of NEL systems offers a valuable approach to understand a NEL
system’s performance quantitatively. However, an in-depth qualitative analysis that helps improving NEL methods by identifying error
causes usually requires a more thorough error analysis. This paper proposes a taxonomy to frame common errors and applies this
taxonomy in a survey study to assess the performance of four well-known Named Entity Linking systems on three recent gold standards.
Keywords: Named Entity Linking, Linked Data Quality, Corpora, Evaluation, Error Analysis