Abstract
Knowledge graph (KG), a visual representation of text data as a semantic network, holds enormous promise for the development of more intelligent robots. It leads to significant potential solutions for many tasks like question answering, recommendation, and information retrieval. However, this area is confined to using English text only. Since low-resource languages are now being used in the world of AI, it is necessary to develop a semantic network for them as well. In this research work, the authors provide state-of-the-art techniques for automatic knowledge graph construction for the Hindi language, which is still unexplored in ontology. Constructing a knowledge graph faces several hurdles and obstacles in the linguistic domain, primarily when it deals with the Hindi language. With an emphasis on the Indian perspective, this research intends to introduce a novel approach ‘HKG’ for knowledge graph construction framework for Hindi. It also implements the LSTM model to evaluate the accuracy of newly constructed knowledge graphs and compute different evaluation metrics such as accuracy and F1-score. This knowledge graph evaluates the accuracy of 87.50 using Doc2Vec word embedding with a train-test split of 7:3.
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