Prof. Dr. Felix Naumann


Paper accepted at COLING

Research Paper

BEL: Bagging for Entity Linking

Zhe Zuo, Gjergji Kasneci, Toni GruetzeFelix Naumann

Abstract. With recent advances in the areas of knowledge engineering and information extraction, the task of linking textual mentions of named entities to corresponding ones in a knowledge base has received much attention. The rich, structured information in state-of-the-art knowledge bases can be leveraged to facilitate this task. Although recent approaches achieve satisfactory accuracy results, they typically suffer from at least one of the following issues: (1) the linking quality is highly sensitive to the amount of textual information; typically, long textual fragments are needed to capture the context of a mention, (2) the disambiguation uncertainty is not explicitly addressed and often only implicitly represented by the ranking of entities to which a mention could be linked, (3) complex, joint reasoning negatively affects the efficiency. 

We propose an entity linking technique that addresses the above issues by (1) operating on a textual range of relevant terms, (2) aggregating decisions from an ensemble of simple classifiers, each of which operates on a randomly sampled subset from the above range, (3) following local reasoning by exploiting previous decisions whenever possible. In extensive experiments on hand-labeled and benchmark datasets, our approach outperformed state-of-the-art entity linking techniques, both in terms of quality and efficiency.