NeuType: A Simple and Effective Neural Network Approach for Predicting Missing Entity Type Information in Knowledge Bases
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.
PDF AbstractTasks
Datasets
Add Datasets
introduced or used in this paper
Results from the Paper
Submit
results from this paper
to get state-of-the-art GitHub badges and help the
community compare results to other papers.
Methods
No methods listed for this paper. Add
relevant methods here
