Semantic Enrichment of Hadith Corpus - Knowledge Graph Generation from Islamic Text

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Nigar Azhar Butt
Amna Basharat
Amna Binte Kamran

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Guest Editors KG Gen from Text 2023

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Knowledge graphs from text have garnered substantial interest across various domains due to their potential to facilitate efficient information retrieval and knowledge exploration. However, knowledge graph generation from textual sources presents unique challenges, particularly in the Islamic domain, where primary sources of knowledge are texts in Arabic, which exhibit complex linguistic and cultural nuances. This paper presents a comprehensive methodology for generating a knowledge graph from the hadith corpus. Hadith, a fundamental resource in the Islamic domain, stands as one of the primary sources of Islamic legislation, encompassing the sayings, actions, and silent approvals of the Prophet Muhammad ﷺ. Leveraging Natural Language Processing techniques, we systematically extract, annotate, and interlink semantic entities and relationships from the hadith corpus, extend the SemanticHadith ontology for entity organisation, and compute textual similarities to establish semantic connections. We generate a comprehensive knowledge graph by applying these methods to six hadith collections, facilitating efficient information retrieval and knowledge exploration in the Islamic domain. This is an essential step towards annotating and linking the hadith corpus to allow semantic search to support scholars or students in creating, evolving, and consulting a digital representation of Islamic knowledge. The SemanticHadith knowledge graph is freely accessible at
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