Knowledge Graphs and NLP: Integrating Structured Knowledge into NLP Systems for Better Reasoning and Contextual Understanding

Authors: Gaurav Kashyap

DOI: https://doi.org/10.5281/zenodo.14535780

Short DOI: https://doi.org/g8wjrn

Country: USA

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Abstract: By using machine learning techniques, Natural Language Processing (NLP) has made tremendous progress in comprehending and producing human language. Nevertheless, despite recent developments, there are still obstacles in the way of allowing machines to use structured knowledge meaningfully and reason contextually. By offering structured, semantic knowledge that can support NLP models, Knowledge Graphs (KGs), which depict relationships between entities in a graph structure, present a promising answer to these problems. This study examines how Knowledge Graphs (KGs) can improve reasoning, context comprehension, and information retrieval in natural language processing (NLP) systems. Additionally, we look at existing methods for integrating KGs with NLP models, such as graph-based neural networks, and emphasize how they affect different NLP tasks like text summarization, named entity recognition, and question answering. The difficulties and potential paths for integrating Knowledge Graphs and NLP to enhance performance in practical applications are covered in the paper's conclusion.
A new method for organizing and utilizing structured data is knowledge graphs, which offer a means of illustrating the connections between important ideas, entities, and facts. Knowledge graphs can improve natural language processing (NLP) systems' capacity to reason about text, comprehend context, and produce more precise and pertinent results. In order to improve named entity recognition, text classification, and question answering, among other NLP tasks, this paper investigates the integration of knowledge graphs with NLP.

Keywords: Knowledge Graphs, Natural Language Processing, Structured Knowledge, Reasoning, Contextual Understanding

Paper Id: 231886

Published On: 2023-09-06

Published In: Volume 11, Issue 5, September-October 2023

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