Contextual Relation Extraction (CRE) is mainly used for constructing a
knowledge graph with a help of ontology. It performs various tasks such as
semantic search, query answering, and textual entailment. Relation extraction
identifies the entities from raw texts and the relations among them. An
efficient and accurate CRE system is essential for creating domain knowledge in
the biomedical industry. Existing Machine Learning and Natural Language
Processing (NLP) techniques are not suitable to predict complex relations from
sentences that consist of more than two relations and unspecified entities
efficiently. In this work, deep learning techniques have been used to identify
the appropriate semantic relation based on the context from multiple sentences.
Even though various machine learning models have been used for relation
extraction, they provide better results only for binary relations, i.e.,
relations occurred exactly between the two entities in a sentence. Machine
learning models are not suited for complex sentences that consist of the words
that have various meanings. To address these issues, hybrid deep learning
models have been used to extract the relations from complex sentence
effectively. This paper explores the analysis of various deep learning models
that are used for relation extraction.

使用深度学习技术来从多个句子的语境中识别适当的语义关系，以解决现有机器学习模型难以从复杂句子中提取关系的问题。本文探讨了用于关系提取的各种深度学习模型的分析。