This paper proposes an ontology-based method for extracting semantic triples from Arabic news articles, addressing unique challenges of Arabic text processing. Our approach combines named entity recognition, part-of-speech tagging, dependency parsing, and sentiment analysis, mapped to a domain ontology designed for conflict analytics. Our methodology enhances Arabic NLP tools with domain-specific rules, implementing a hybrid architecture integrating CAMeL Tools and FARASA for improved entity extraction. Experimental results on 5,000 Modern Standard Arabic news articles demonstrate 96.06% named entity coverage, with balanced recognition across locations (43.5%), organizations (25.7%), and persons (24.8%). Network analysis reveals hierarchical information patterns, with news articles serving as central nodes and locations as contextual anchors, forming distinct communities of entities. Furthermore, sentiment analysis shows predominant neutral (51.3%) and positive (45.3%) polarities despite the conflict context, reflecting journalistic objectivity. Despite current limitations in Arabic NLP tools and resources, our approach demonstrates effective semantic knowledge extraction through combined ontological and sentiment frameworks.
