STYLISTIC OF THE QUR’AN: READING THE STORY OF SULAIMAN

This paper examines the stylistic of Sulaiman's story in the Qur’an and its chronology. The language style has a building in the historical social context of the Arab community when it came down to the Qur’an. This article aimed to find the chronology and function of each language style used in the story of Sulaiman. This study was a library research with descriptive and analytical data presentation methods. The primary data were taken from the Qur’an in Surah Şād (30–40). A hetorical and chronological analysis wass used in this research. The finding of this study showed that the rhetoric of Sulaiman's story in Surah Şād still used a high style of language. Based on these findings, a strong language style is a characteristic of the Sulaiman's story to strengthen the prophet's heart, console the prophet and give the encouragement of spreading da’wa of Muhammad. This study contributes to a complete understanding of language style

Unifying linguistic annotations and ontologies for the Arabic Quran

The WACL’2 Second Workshop on Arabic Corpus Linguistics was held in conjunction with the Corpus Linguistics 2013 conference. Following on from the successful first WACL in 2011, as well as the related LRE-REL event in 2012, WACL-2 again took place at Lancaster University. The aim of this series of workshops is to create a venue for exploring progress in the field of research into the Arabic language using corpora, from across the many areas of corpus linguistics and computational linguistics where the analysis of Arabic structure and usage is an active issue. The scope of the workshop encompasses both (a) the design, construction and annotation of Arabic corpora, and (b) the use of corpora in research on the Arabic language – in any relevant area, including (but not limited to!) lexis and lexicography, syntax, collocation, NLP systems and analysis tools, contrastive and historical studies, stylistics, and discourse analysis. All varieties of Arabic – including the different Colloquial Arabics as well as Classical/Qur’anic and Modern Standard forms of the language – are within the workshop's purview

The Semantic Annotation of the Quran Corpus Based on Hierarchical Network of Concepts Theory

This Quran as the central religious text of Islam is widely regarded as the finest work in classical Arabic literature and plays an important role in Islam world. This paper studied and analyzed the Quran Chinese and English data, built the Quran Chinese and English words semantic knowledge base in which the grammar and semantic information of the Quranic words were described based on HNC theory, built the Quran semantic annotation corpus in which the part of speech and semantic description were annotated. The corpus with semantic annotation can help us identify the same meaning with different word forms. This paper proposed a new method of semantic analysis to solve the semantic similarity problem of natural language processing, which will benefit both the research on the semantic analysis in Natural Language Processing and the development of the Islamic Cultures

Using Linguistic Analysis to Translate Arabic Natural Language Queries to SPARQL

The logic-based machine-understandable framework of the Semantic Web often challenges naive users when they try to query ontology-based knowledge bases. Existing research efforts have approached this problem by introducing Natural Language (NL) interfaces to ontologies. These NL interfaces have the ability to construct SPARQL queries based on NL user queries. However, most efforts were restricted to queries expressed in English, and they often benefited from the advancement of English NLP tools. However, little research has been done to support querying the Arabic content on the Semantic Web by using NL queries. This paper presents a domain-independent approach to translate Arabic NL queries to SPARQL by leveraging linguistic analysis. Based on a special consideration on Noun Phrases (NPs), our approach uses a language parser to extract NPs and the relations from Arabic parse trees and match them to the underlying ontology. It then utilizes knowledge in the ontology to group NPs into triple-based representations. A SPARQL query is finally generated by extracting targets and modifiers, and interpreting them into SPARQL. The interpretation of advanced semantic features including negation, conjunctive and disjunctive modifiers is also supported. The approach was evaluated by using two datasets consisting of OWL test data and queries, and the obtained results have confirmed its feasibility to translate Arabic NL queries to SPARQL.Comment: Journal Pape

Finding a Way Through the Crowd: How Keyword Choices Affect Discoverability in Crowdsourced Archival Tagging

This study explores the challenges archivists face when determining how to structure a crowdsourced tagging initiative in their collections. Specifically, the study aims to research the differences in controlled vocabulary tagging and folksonomy tagging in volunteer based digital archival tagging initiatives. The current literature focuses on the merits of crowdsourced metadata in its various forms, but few sources explore which formats lead to better discoverability. In order to begin to answer this question, five library, museum, and archives professionals were interviewed and asked to discuss their institution’s crowdsourcing projects in depth. Their answers were then mined for overarching themes and insights into crowdsourcing and vocabulary type. In the end, it was discovered that there is no one correct vocabulary system for crowdsourcing, but that by answering key questions about specific institutions, collections, and volunteers, a unique approach can be created for each new project to ensure the best outcome.Master of Science in Library Scienc

AR2SPARQL: An Arabic Natural Language Interface for the Semantic Web

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

Joint Alignment of Segmentation and Labelling for Arabic Morphosyntactic Taggers

We present and compare three methods of alignment between morphemes resulting from four different Arabic POS - taggers as well as one baseline method using only provided labels. We combined four Arabic POS - taggers: MADAMIRA (M A), Stanford Tagger (ST), AMIRA (AM), Farasa (FA); and as the target output used two Classical Arabic gold standards: Quranic Arabic Corpus (QAC) and SALMA Standard Arabic Linguistics Morphological Analysis (SAL). We justify why we opt to use label for aligning instead of word form. The problem is not trivial as it is tackling six different tokenisation and labelling standards. The supervised learning using a unigram model scored the best segment alignment accuracy, correctly aligning 97 % of morpheme segments. We then evaluated the alignment methods extrinsically, in terms of their effect in improving accuracy of ensemble POS - taggers, merging different combinations of the four Arabic POS - taggers. Using the best approach to align input POS taggers, ensemble tagger has correctly segmented and tagged 88.09% of morphemes. We show how increasing the number of input taggers raise the accuracy, suggesting that input taggers make different errors