In spite of great efforts that have been made to present systems that support the user's need of the answers from the Holy Quran, the current systems of English translation of Quran still need to do more investigation in order to develop the process of retrieving the accurate verse based on user's question. The Islamic terms are different from one document to another and might be undefined for the user. Thus, the need emerged for a Question Answering System (QAS) that retrieves the exact verse based on a semantic search of the Holy Quran. The main objective of this research is to develop the efficiency of the information retrieval from the Holy Quran based on QAS and retrieving an accurate answer to the user's question through classifying the verses using the Neural Network (NN) technique depending on the purpose of the verses' contents, in order to match between questions and verses. This research has used the most popular English translation of the Quran of Abdullah Yusuf Ali as the data set. In that respect, the QAS will tackle these problems by expanding the question, using WordNet and benefitting from the collection of Islamic terms in order to avoid differences in the terms of translations and question. In addition, this QAS classifies the Al-Baqarah surah into two classes, which are Fasting and Pilgrimage based on the NN classifier, to reduce the retrieval of irrelevant verses since the user's questions are asking for Fasting and Pilgrimage.  Hence, this QAS retrieves the relevant verses to the question based on the N-gram technique, then ranking the retrieved verses based on the highest score of similarity to satisfy the desire of the user. According to F-measure, the evaluation of classification by using NN has shown an approximately 90% level and the evaluation of the proposed approach of this research based on the entire QAS has shown an approximately 87% level. This demonstrates that the QAS succeeded in providing a promising outcome in this critical field.

A Question Answering System on Holy Quran Translation Based on Question Expansion Technique and Neural Network Classification

In this paper, we propose to build a morpho-semantic knowledge graph from Arabic vocalized corpora. Our work focuses on classical Arabic as it has not been deeply investigated in related works. We use a tool suite which allows analyzing and disambiguating Arabic texts, taking into account short diacritics to reduce ambiguities. At the morphological level, we combine Ghwanmeh stemmer and MADAMIRA which are adapted to extract a multi-level lexicon from Arabic vocalized corpora. At the semantic level, we infer semantic dependencies between tokens by exploiting contextual knowledge extracted by a concordancer. Both morphological and semantic links are represented through compressed graphs, which are accessed through lazy methods. These graphs are mined using a measure inspired from BM25 to compute one-to-many similarity. Indeed, we propose to evaluate the morpho-semantic Knowledge Graph in the context of Arabic Information Retrieval (IR). Several scenarios of document indexing and query expansion are assessed. That is, we vary indexing units for Arabic IR based on different levels of morphological knowledge, a challenging issue which is not yet resolved in previous research. We also experiment several combinations of morpho-semantic query expansion. This permits to validate our resource and to study its impact on IR based on state-of-the art evaluation metrics.

Building a morpho-semantic knowledge graph for Arabic information retrieval

With the growing interest in supporting the Arabic language on the Semantic Web (SW), there is an emerging need to enable Arab users to query ontologies and RDF stores without being challenged with the formal logic of the SW. In the domain of English language, several efforts provided Natural Language (NL) interfaces to enable ordinary users to query ontologies using NL queries. However, none of these efforts were designed to support the Arabic language which has different morphological and semantic structures. As a step towards supporting Arabic Question Answering (QA) on the SW, this work presents AR2SPARQL, a NL interface that takes questions expressed in Arabic and returns answers drawn from an ontology-based knowledge base. The core of AR2SPARQL is the approach we propose to translate Arabic questions into triples which are matched against RDF data to retrieve an answer. The system uses both linguistic and semantic features to resolve ambiguity when matching words to the ontology content. To overcome the limited support for Arabic Natural Language Processing (NLP), the system does not make intensive use of sophisticated linguistic methods. Instead, it relies more on the knowledge defined in the ontology and the grammar rules we define to capture the structures of Arabic questions and to construct an adequate RDF representations. AR2SPARQL has been tested with two different datasets and results have shown that it achieves a good retrieval performance in terms of precision and recall.

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AR2SPARQL: An Arabic Natural Language Interface for the Semantic Web

This article proposes across-lingual ontology enrichment (CLOE) approach for enriching ontologies from amultilingual text or other ontologies. Athree-level hierarchical multi-agent model has been b...

CLOE: a cross-lingual ontology enrichment using multi-agent architecture

The semantic resources are important parts in the Information Retrieval (IR) such as search engines, Question Answering (QA), etc., these resources should be available, readable and understandable. In semantic web, the ontology plays a central role for the information retrieval, which use to retrieves more relevant information from unstructured information. This paper presents a semantic-based retrieval system for the Arabic text, which expands the input query semantically using Arabic domain ontology. In the proposed approach, the search engine index is represented using Vector Space Model (VSM), and the Arabic's place nouns domain ontology has been used which constructed and implemented using Web Ontology Language (OWL) from Arabic corpus. The proposed approach has been experimented on the Arabic Quran corpus, and the experiments show that the approach outperforms in terms of both precision and recall the traditional keyword- based methods.

/pdf/ontology-based-query-expansion-for-arabic-text-retrieval-4jliiw5rsz.pdf

Ontology-based Query Expansion for Arabic Text Retrieval

Arabic language is the most spoken languages in the Semitic languages group, and one of the most common languages in the world spoken by more than 422 million. It is also of paramount importance to Muslims, it is a sacred language of the Islamic Holly Book (Quran) and prayer (and other acts of worship) in Islam is performed only by mastering some of Arabic words. Arabic is also a major ritual language of a number of Christian churches in the Arab world and it is also used in writing several intellectual and religious Jewish books in the Middle Ages. Despite this, there is no semantic Arabic lexicon which researchers can depend on. In this paper we introduce Azhary as a lexical ontology for the Arabic language. It groups Arabic words into sets of synonyms called synsets, and records a number of relationships between words such as synonym, antonym, hypernym, hyponym, meronym, holonym and association relations. The ontology contains 26,195 words organized in 13,328 synsets. It has been developed and contrasted against AWN which is the most common available Arabic lexical ontology.

Azhary: An Arabic Lexical Ontology

Arabic language is the most spoken languages in the Semitic languages group, and one of the most common languages in the world spoken by more than 422 million. It is also of paramount importance to Muslims, it is a sacred language of the Islamic Holly Book (Quran) and prayer (and other acts of worship) in Islam is performed only by mastering some of Arabic words. Arabic is also a major ritual language of a number of Christian churches in the Arab world and it is also used in writing several intellectual and religious Jewish books in the Middle Ages. Despite this, there is no semantic Arabic lexicon which researchers can depend on. In this paper we introduce Azhary as a lexical ontology for the Arabic language. It groups Arabic words into sets of synonyms called synsets, and records a number of relationships between words such as synonym, antonym, hypernym, hyponym, meronym, holonym and association relations. The ontology contains 26,195 words organized in 13,328 synsets. It has been developed and contrasted against AWN which is the most common available Arabic lexical ontology. keywords: Arabic Language, Ontology, WordNet, Semantic Web, Arabic Lexicon.

/pdf/azhary-an-arabic-lexical-ontology-4gynv7obtb.pdf

Despite the enormous number of documents and the huge amount of information contained in the Internet, the information search process is still difficult and unsatisfactory. The main reason behind this is that the search engines use the traditional search which depends on the keywords not on the concepts as semantic search engines.  Tim Berners - Lee  invented the Semantic Web in 2001 and until now many researches about semantic web search engines have appeared, however the Semantic Web researches in Arabic language are still few especially with regard to the Islamic information domain. Total number of Muslim countries is about 57 countries and a population of Muslims around the world is about 1.62 billion by more than 23% of the world's population. This large number believes on one book (The Holy Quran) and needs to retrieve information from it accurately and easily. The Holy Quran, which God revealed to the Prophet Muhammad as Muslims believe, has been worshipped for 1,400 years. The Holy Quran is written in the classical Arabic language. This ancient language has undergone a lot of modifications. For this reason, the Holy Quran became not fully understood by the majority of Muslims. To solve this problem, we present in this paper, the ISWSE as an Islamic Semantic Web Search Engine which searches in the Holy Quran. The proposed system is based on Islamic Ontology and uses Azhary  as a lexical ontology  for the  Arabic language . The paper also introduces experimental results and performance evaluation for the proposed system.

/pdf/iswse-islamic-semantic-web-search-engine-318y4yavci.pdf

ISWSE: Islamic Semantic Web Search Engine

With the tremendous growth of the web and the contents difference, users need specialized accurate results depending on their behavior and varying according to their interest. In this paper, we introduce A Personal Search Engine which provides results relevant to the user's interest. Our search engine depends on three factors to ensure relevant and accurate results. The first factor is the degree of importance of the document category to the user. The second factor is the user's interest page rank which depends on the user's browsing of the page. The third factor is the degree of relevance of the document.

Hossam Ishkewy

Papers

Azhary: An Arabic Lexical Ontology

Azhary: An Arabic Lexical Ontology

ISWSE: Islamic Semantic Web Search Engine

Personal search engine based on user interests and modified page rank