Using BERT and Knowledge Graph for detecting triples in Vietnamese text

One of the challenges in constructing Knowledge Graphs from text is verifying the correctness of the produced results. Each language has its unique characteristics, so a Knowledge Graphs construction system may perform better on certain languages and worse on others. In order to detect the most suitable Knowledge Graph construction systems for Vietnamese, in this paper, we propose a method to classify triples extracted from such systems into two categories: Existent and Non-existent. Vietnamese is a low-resource language with limited natural language processing tools and datasets. By combining BERT with a self-constructed Vietnamese Knowledge Graph, we build a classification model to verify the existence of triples in paragraphs. Our results suggest that BERT can learn contextual relations between words from a large amount of text, even for a low-resource language like Vietnamese. BERT’s adaptive capability to detect meaningful triples is also shown and discussed. The outcome of this paper could potentially be used to build more sophisticated systems to solve Knowledge Graph construction and Triple Classification tasks in low resource languages.