OaRCM: An Amalgamation of Ontology and Deep Learning Approach for Effective Relation Classification of Mosquito Vector Biocontrol Agents

Abstract

Relation classification predicts the semantic association and their correlated concepts from a corpus and categorizes them into group of relations that play a major role in text summarization, sentimental analysis, knowledge graph development, and so on. It reduces training time and computation cost by utilizing fine-tuned pre-trained models and representation techniques. The group of relations is helpful for effective classification as compared to open-domain text classification. In general, rule-based, supervised, unsupervised, weakly supervised, distantly supervised, and few-shot learning methods are used for relation classification. Still, the preparation of manually annotated training data, extraction of noisy words and unrelated relations, and data sparsity need to be considered. Relation classification through deep learning may learn implicit or biased relations, leading to incorrect classifications, and difficulty in understanding predictions. The incorporation of ontology can address these issues by providing relational knowledge and context. The combination of deep learning and ontology for relation classification provides a more powerful and robust model to improve representation learning and generalization, complex relationship identification, and entity predictions. An amalgamation of ontology-driven deep learning models (OaRCM) to classify the semantic relations among the mosquito vector biocontrol agents is proposed. Long short-term memory (LSTM), bidirectional long short-term memory (BiLSTM), and bidirectional gated recurrent unit (BiGRU) neural networks are considered for relation classification. SemEval-2010 Task 8 and the manually annotated dataset on MosqVecBA are used. A knowledge graph is constructed to visualize the inter-relationships among mosquito biocontrol agents. The proposed system can be used to develop a recommendation system, question-answering, and a semantic search engine for various domains such as medical, biological, chemical, and so on.

Keywords

ontology deep learning relation classification knowledge graph mosquito vector biocontrol agents

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