A Benchmark and Comprehensive Survey on Knowledge Graph Entity Alignment via Representation Learning
This is an incremental contribution that addresses the need for better benchmarks and understanding in entity alignment for knowledge base applications in AI.
The paper tackles the problem of enriching knowledge bases through entity alignment by providing a comprehensive survey and benchmark of representation learning techniques, proposing two new datasets and conducting experiments that reveal techniques using attribute triples and relation predicates as features perform best.
In the last few years, the interest in knowledge bases has grown exponentially in both the research community and the industry due to their essential role in AI applications. Entity alignment is an important task for enriching knowledge bases. This paper provides a comprehensive tutorial-type survey on representative entity alignment techniques that use the new approach of representation learning. We present a framework for capturing the key characteristics of these techniques, propose two datasets to address the limitation of existing benchmark datasets, and conduct extensive experiments using the proposed datasets. The framework gives a clear picture of how the techniques work. The experiments yield important results about the empirical performance of the techniques and how various factors affect the performance. One important observation not stressed by previous work is that techniques making good use of attribute triples and relation predicates as features stand out as winners.