BERT based triple classification model using knowledge graph embedding for question answering system

What is it about?

we presented a solution to solve these issues by using the BERT model and the knowledge graph to enhance a question answering system

Featured Image

Why is it important?

We combined content-based and linked-based information for knowledge graph representation learning and classified triples into one of three classes such as base class, derived class, or non-existent class. We then used the BERT model to build two classifiers: BERT-based text classification for content information and BERT-based triple classification for link information. The former was able to make a contextual embedding vector for representing triples that were used to classify into the three above classes. The latter generated all path instances from all meta paths of a large heterogeneous information network by running the Motif Search method of Apache Spark on a distributed environment. After creating the path instances, we produced triples from these path instances. We made content-based information by converting triples into natural language text with labels and considered them as a text classification problem