Investigations on Knowledge Base Embedding for Relation Prediction and Extraction
This work addresses the problem of assessing knowledge base embedding effectiveness for researchers in natural language processing, but it is incremental as it primarily evaluates existing methods without introducing new techniques.
The paper evaluated existing knowledge base embedding models for relation prediction and extraction across multiple benchmarks, finding they are generally effective for prediction but fail to improve state-of-the-art neural relation extraction models, highlighting limitations in current strategies.
We report an evaluation of the effectiveness of the existing knowledge base embedding models for relation prediction and for relation extraction on a wide range of benchmarks. We also describe a new benchmark, which is much larger and complex than previous ones, which we introduce to help validate the effectiveness of both tasks. The results demonstrate that knowledge base embedding models are generally effective for relation prediction but unable to give improvements for the state-of-art neural relation extraction model with the existing strategies, while pointing limitations of existing methods.