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 oflarge open source Knowledge Bases and the continuous focus on improving NEL performance has led to the creation of automatedbenchmark solutions during the last decade. The benchmarking of NEL systems offers a valuable approach to understand a NELsystem’s performance quantitatively. However, an in-depth qualitative analysis that helps improving NEL methods by identifying errorcauses usually requires a more thorough error analysis. This paper proposes a taxonomy to frame common errors and applies thistaxonomy in a survey study to assess the performance of four well-known Named Entity Linking systems on three recent gold standards.

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