TENET: Joint Entity and Relation Linking with Coherence Relaxation

2021 
The joint entity and relation linking task aims to connect the noun phrases (resp., relational phrases) extracted from natural language documents to the entities (resp., predicates) in general knowledge bases (KBs). This task benefits numerous downstream systems, such as question answering and KB population. Previous works on entity and relation linking rely on the global coherence assumption, i.e., entities and predicates within the same document are highly correlated with each other. However, this assumption is not always valid in many real-world scenarios. Due to KB incompleteness or data sparsity, sparse coherence among the entities and predicates within the same document is common. Moreover, there may exist isolated entities or predicates that are not related to any other linked concepts. In this paper, we propose TENET, a joint entity and relation linking technique, which relaxes the coherence assumption in an unsupervised manner. Specifically, we formulate the joint entity and relation linking task as a minimum-cost rooted tree cover problem on the knowledge coherence graph constructed based on the document. We then propose effective approximation algorithms with pruning strategies to solve this problem and derive the linking results. Extensive experiments on real-world datasets demonstrate the superior effectiveness and efficiency of our method against the state-of-the-art techniques.
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