Automatic Development of Knowledge Graph Based on NLTK and Sentence Analysis

In this paper, our group came up with a new method that automatically creates knowledge graphs from the natural language. It combines different technologies, such as parse of the sentence, Resources Description Framework (RDF), and NoSQL database. For sentences analysis part, we used NLTK and Stanfo...

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Bibliographic Details
Published in2021 3rd International Conference on Natural Language Processing (ICNLP) pp. 52 - 56
Main Authors Jing, Haoze, Wang, Changying, Cheng, Li, Qi, Junting, Jiang, Shuihan, Zhang, Xiaojuan
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.03.2021
Subjects
Genomics
Knowledge Graph
Manuals
NoSQL databases
parse tree
RDF triplet extraction
Resource description framework
Semantic Web
Semantics
Visual Genome
Visualization
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DOI10.1109/ICNLP52887.2021.00015

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Summary:In this paper, our group came up with a new method that automatically creates knowledge graphs from the natural language. It combines different technologies, such as parse of the sentence, Resources Description Framework (RDF), and NoSQL database. For sentences analysis part, we used NLTK and Stanford coreNLP to get parse trees from natural language sentences. Then extract RDF triplets from parse trees, which can be used to create knowledge graphs. Test and compare them on Visual Genome datasets from Stanford University because they include many images with different regions and their descriptions. Finally, we achieved 68%, 91%, and 92% accuracy in predicting subject, predicate, and object, respectively. The result shows that it's possible to apply for it on the automatic development of knowledge graphs.
DOI:10.1109/ICNLP52887.2021.00015