Use of artificial intelligence in biblical citation recommendations in the New Testament

Religion occupies a prominent place in people’s daily lives and is made explicit to the public or the faithful through preaching or exposition of their sacred texts. The Holy Bible is the religious literature of Christianity, and its text has a unique nature of interpretation and knowledge extraction, that is, through the reading done by specialists (theologians). However, an automated knowledge extraction or that involves some automatic mechanism intelligence to support the interpretation (hermeneutics) of the Biblical text is not observed in the literature. Probably this gap in the literature is caused by the complexity of the biblical textual corpus and the multiplicity of genres it has, being an interpretative challenge even for human specialists. Therefore, this article primarily seeks to build an automated way through artificial intelligence (AI) to provide contextual biblical quotations from the four gospels of the New Testament for the construction of sermons or development of homiletics, which is the art of producing religious sermons for teaching and interpretation of the Biblical message. The methodology used in this article seeks to employ artificial intelligence techniques to implement the proposed solution, that is, a hybrid recommendation system to quote texts from Biblical passages. The AI techniques involved are text mining, natural language processing and supervised learning. Secondarily, this work aims to verify whether the combination of natural language processing techniques and machine learning can provide subsidies for the recovery or extraction of knowledge from complex textual corpus analogous to the biblical corpus. The results show that the proposed hybrid recommendation system is capable of extracting semantic and contextual meaning from the Biblical text, fundamental in the construction of homiletics. The performance evaluation metrics indicate the robustness of the results and consequently validate the findings of this research. Therefore, the combination of these techniques can be extrapolated by the scientific community to aid in the interpretive recovery of complex textual corpus.