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Question answering over knowledge graphs using BERT based relation mapping
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AI
DOI：10.1111/exsy.13456.png)
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Abstract
En 中文
A knowledge graph (KG) is a structured form of knowledge describing real-world entities, properties and relationships as a graph. Question answering over knowledge graphs (KGQA) allows people to ask questions in natural language and extract answers from KG accurately and more quickly. The main task of a KGQA is to convert a natural language query to the corresponding structured query form like SPARQL. However, generating the precise SPARQL query from a question is challenging and highly error-prone. Here we propose a question-answering framework that uses KG to answer simple questions without using SPARQL. Question classification, dependency parsing, entity linking, BERT-based relation finding and answer extraction constitute the main modules of the approach. We have used the DBpedia as the KG and tested the end-to-end system with a subset of QALD-4, LC-QuAD and SimpleQuestions datasets. Results show considerable improvement compared to other approaches in terms of F1-score.
Keywords:
entity and relation mapping
natural language processing
question answering over knowledge graphs
question classification
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