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Developing A BERT Based Triple Classification Model Using Knowledge Graph Embedding for Question Answering System
Developing A BERT Based Triple Classification Model Using Knowledge Graph Embedding for Question Answering System
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Date
2022
Authors
Phan Hồ Viết Trường
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Abstract
The current BERT-based question answering systems use a question and a contextual text to find the answer. This causes the
systems to return wrong answers or nothing if the text contains irrelevant contents with the input question. Besides, the systems
haven’t answered yes-no and aggregate questions yet. Besides that, the systems only concentrate on the contents of text
regardless of the relationship between entities in the corpus. This systems cannot validate the answer. In this paper, we presented
a solution to solve these issues by using the BERT model and the knowledge graph to enhance a question answering system. 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. Our proposed solution outperformed other embedding methods with an average accuracy of
92.34% on benchmark datasets and the Motif Finding algorithm with an average executive time improvement of 37% on the
distributed environment.
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Keywords
BERT based triple classification model,
Knowledge graph embedding,
Meta-path,
Motif finding