An Efficient Semantic Relation Extraction Method For Arabic Texts Based On Similarity Measures
Semantic relation extraction is an important component of ontologies that can support many applications e.g. text mining, question answering, and information extraction. However, extracting semantic relations between concepts is not trivial and one of the main challenges in Natural Language Processing (NLP) Field. In this paper, we propose a method for semantic relation extraction between concepts. The method relies on the definition of concept context and the semantic similarity measures to extract relations from domain corpus. In this work, we implemented algorithm for concept context construction and for similarity computation based on different semantic similarity measures. We analyze the proposed methods and evaluate their performance. The preliminary experiments showed that the best results precision of 83% are obtained with Lin measure at minimum confidence =0.50 and precision of 85% with the Cosine and Jaccard similarity measures. The main advantage is the automatic and unsupervised operation; it doesn't need any pre labeled training data. Also used effectively for relation extraction in various domains. The results show the high effectiveness of the proposed approach to extract relations for Arabic ontology construction.