An Ontology-based Name Entity Recognition NER and NLP Systems in Arabic Storytelling

Abstract

Ontology is a descriptive model representing domain knowledge with robust specifications that solve interoperability between humans and machines. In this work, a practical methodology presented for Arabic Storytelling ontology construction for domain ontology extraction from unstructured Arabic story documents. However, the manual construction of ontologies is a time-consuming and challenging process. Still, ontology construction and learning, which extracts ontological knowledge from various data types automatically or semiautomatically, can overcome the bottleneck of knowledge acquisition. This paper intends to investigate the problem of automatically construct and build an Arabic storytelling ontology based on Arabic named entity recognition (NER) from unstructured story text. This paper presents a system designed based on Machine Learning (ML) approach. The system framework is a combination of five main stages: The first stage determines the requirement analysis-second document pre-processing using NLP tasks. The third is Conceptualization. The fourth stage is formal design and construction, and the final step is evaluation.

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“…Named Entity Recognition (NER) is a core NLP method that leverages machine learning and linguistic patterns to identify and categorize specific elements like people, organizations, places, and dates in text, offering insights into document structure. NER aids ontology development by extracting entities that can be mapped to ontology concepts [7]. In order to predict relations between entities in a given sentence, relation extraction techniques are of high value.…”