Improving Arabic Information Retrieval Results Semantically Using Ontology

This research proposes a new way to improve the search outcome of Arabic semantics by abstractly summarizing the Arabic texts (Abstractive Summary) using natural language processing algorithms(NLP),Word Sense Disambiguation (WSD) and techniques of measuring Semantic Similarity in Arabic WordNet Ontology.

Semantic similarity between tow sentences in arabic

Text Similarity is an important task in several application fields, such as information retrieval, plagiarism detection, machine translation, topic detection, text classification, text summarization and others. Finding similarity between two texts, paragraphs or sentences, is based on measuring, directly or indirectly, the similarity between words. There are two known types of words similarity: lexical and semantic. The first one handles the words as a stream of characters: words are similar lexically if they share the same characters in the same order. The second type aims to quantify the degree to which two words are semantically related. As an example they can be, synonyms, represent the same thing or they are used in the same context. In this article we focus our investigation on measuring the semantic similarity between Arabic sentences using several representations