NeuType: A Simple and Effective Neural Network Approach for Predicting Missing Entity Type Information in Knowledge Bases
This addresses incomplete type information for entities in knowledge bases, which is crucial for information access tasks, but it appears incremental as it builds on existing neural approaches.
The paper tackled the problem of predicting missing entity type information in knowledge bases by proposing two neural network architectures that use entity descriptions and optional related entity data, achieving significant improvements over the state of the art on the DBpedia dataset.
Knowledge bases store information about the semantic types of entities, which can be utilized in a range of information access tasks. This information, however, is often incomplete, due to new entities emerging on a daily basis. We address the task of automatically assigning types to entities in a knowledge base from a type taxonomy. Specifically, we present two neural network architectures, which take short entity descriptions and, optionally, information about related entities as input. Using the DBpedia knowledge base for experimental evaluation, we demonstrate that these simple architectures yield significant improvements over the current state of the art.