Table 37 - uploaded by Karim Ouda
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Scientific retrieval systems need to be given domain search terms for searching publications, however, as natural language, search terms provided by users are often fuzzy and limited and some relevant terms are always overlooked in searching. Meanwhile, users always desire to be given domain related keywords to enlighten themselves what other terms...
A very large number of historical manuscript collections are available in image formats and require extensive manual processing in order to search through them. So, we propose and build a search engine for automatically storing, indexing and efficiently searching the manuscript images. Firstly, a handwritten text recognition technique is used to co...
Big geospatial data are archived and made available through online web discovery and access. However, finding the right data for scientific research and application development is still a challenge. This paper aims to improve the data discovery by mining the user knowledge from log files. Specifically, user web session reconstruction is focused upo...
This paper reconstructs the Freebase data dumps to understand the underlying ontology behind Google's semantic search feature. The Freebase knowledge base was a major Semantic Web and linked data technology that was acquired by Google in 2010 to support the Google Knowledge Graph, the backend for Google search results that include structured answer...
Citations
... The questions were made available but without their answers. Hamdelsayed and Atwell [10], Shmeisani et al. [24], Ouda [19], and Hamoud and Atwell [13] also adopted a similar evaluation approach, although Hamoud and Atwell could have used part of their developed QA database for testing. This overview implies that evaluation of Arabic QA research based on Qur'an experts' judgment of systems' returned answers does not warrant fair performance comparisons due to the use of different sets of questions. ...
The absence of publicly available reusable test collections for Arabic question answering on the Holy Qur’an has impeded the possibility of fairly comparing the performance of systems in that domain. In this article, we introduce AyaTEC , a reusable test collection for verse-based question answering on the Holy Qur’an, which serves as a common experimental testbed for this task. AyaTEC includes 207 questions (with their corresponding 1,762 answers) covering 11 topic categories of the Holy Qur’an that target the information needs of both curious and skeptical users. To the best of our effort, the answers to the questions (each represented as a sequence of verses) in AyaTEC were exhaustive—that is, all qur’anic verses that directly answered the questions were exhaustively extracted and annotated. To facilitate the use of AyaTEC in evaluating the systems designed for that task, we propose several evaluation measures to support the different types of questions and the nature of verse-based answers while integrating the concept of partial matching of answers in the evaluation.
... Ta'a et al. (2017) study the relation of Quran and information technology, in terms of searching for classification of al Quran. Ouda (2015) also finds the same related issue as to who build "Intelligence System" also "Semantic Search" for the Quran. ...
... For example, in [3] contrasted their proposed Azhary, an Arabic Language lexical ontology against Arabic WordNet (AWN) in terms of words semantic meanings and relations. Meanwhile, in [25] compared his QuranAnalysis (QA) with the other twelve ontologies according to the nine list criteria proposed in [37]. Application-based is measured by the functionality of the ontology onto an actual software program or a use-case scenario (application). ...
... Both [7,33] implemented their ontologies using Protégé to represent the knowledge and manage the class hierarchy and relationships. In [25] integrated his QA Ontology into QA website to add more functionalities and smartness, and in [30] used PROMPT system to compare the results obtained. Apart from that, there is also a user-based evaluation where human experts assess on how well the ontology meets a set of predefined criteria, standards and requirements. ...
The Holy Quran ontology models are gaining popularity among researchers due to people's demands in understanding this divine book. Due to this, there are many studies and research have been conducted in this area to facilitate people's understanding of the Quran. The Quran knowledge is represented conforming to an ontology within a system framework. This also includes various concepts that are interrelated with the others. From the literature, however, the existing Quranic ontology models do not cover all concepts in the Quran, which limit them to domains such as place nouns, themes, pronouns, antonyms and Islamic knowledge in the Quran. Thus, this research aims to identify relevant research works from various electronic data sources using systematic literature review (SLR) method to provide a comprehensive review of this area. This paper presents a systematic review of the literature related to the existing ontology models, where it leads to disseminating the correct knowledge of the Quran using semantic technologies.
... This dataset includes 244 scientific articles, out of which 144 are for training and 100 are for test. (2) Quran English translation by Yousaf Ali [50,51]. (3) 500N-KPCrowd [52] dataset that is composed of news stories. ...
Automatic key concept extraction from text is the main challenging task in information
extraction, information retrieval and digital libraries, ontology learning, and text analysis.
The statistical frequency and topical graph-based ranking are the two kinds of potentially powerful and leading unsupervised approaches in this area, devised to address the problem. To utilize the potential of these approaches and improve key concept identification, a comprehensive performance analysis of these approaches on datasets from different domains is needed. The objective of the study presented in this paper is to perform a comprehensive empirical analysis of selected frequency and topical graph-based algorithms for key concept extraction on three different datasets, to identify
the major sources of error in these approaches. For experimental analysis, we have selected TF-IDF, KP-Miner and TopicRank. Three major sources of error, i.e., frequency errors, syntactical errors and semantical errors, and the factors that contribute to these errors are identified. Analysis of the results reveals that performance of the selected approaches is significantly degraded by these errors. These findings can help us develop an intelligent solution for key concept extraction in the future.
The purpose of this research is to build an ontology of the masters appearing in the Babylonian Talmud (BT). The ontology built so far has been shared as a Linked Open Data and it will be linked to existing vocabularies. This work has been developed in the context of the Babylonian Talmud Translation Project, where more than eighty Talmudists are working together, since 2012, at the translation (comprehensive of explicative notes and glossaries) of the Talmud into Italian. The construction of the resource has involved the application of tools leveraging on computational linguistics approaches. The ontology, already describing more than 500 masters, constitutes the first portion of a more comprehensive Talmudic Knowledge Base where the text itself, the terminology, the entities, and the concepts constituting the BT will be formalized and linked to each other.
This research attempts to build a new model of semantic analysis to the Qur’anic text, which is called encyclopedic semantics. This research contributes to the improvement of Qur’anic semantic analysis of Izutsu and as an alternative model. This article employs a qualitative method and research and development (R & D) with analytical descriptive to the data gathered. Analysis of the data consist of several steps, namely analyzing Izutsu’s model of Qur’anic semantics, identifying and verifying several limitations on Izutsu’s model, building a new design of Qur’anic semantics to perfection, and lastly, demonstrating the new model in the semantics applied to the Qur’anic text. This research shows that based on the laxity found in Izutsu’s model, this research creates a new model of semantics analysis to the Qur’an called encyclopedic semantics as an alternative model to the existing model from Izutsu. The semantics model of encyclopedic perfects the Izutsu model with significant differences. The semantics model of encyclopedic aims at rendering the meanings of the Qur’an from global to particular. In contrast, the Izutsu model is to achieve meanings from particular to global. Besides, encyclopedic model of semantics is part of mawdlu’iy (thematic) method of interpreting the Qur’an. This model gains its legitimation from Islamic tradition. The sample of this model is shown in the application of the Qur’anic word maṭar.
Our Artificial Intelligence research group at the University of Leeds has collected, analysed and annotated Classical Arabic corpus resources: the Quranic Arabic Corpus with several layers of linguistic annotation; the QurAna Quran pronoun anaphoric co-reference corpus; the QurSim Quran verse similarity corpus; the Qurany Quran corpus annotated with English translations and verse topics; the Boundary-Annotated Quran Corpus; the Quran Question and Answer Corpus; the Multilingual Hadith Corpus; the King Saud University Corpus of Classical Arabic; and the Corpus for teaching about Islam. We have also developed Modern Arabic corpus resources spanning several genres and language types: Arabic By Computer; the Corpus of Contemporary Arabic; the Arabic Internet Corpus; the World Wide Arabic Corpus; the Arabic Discourse Treebank; the Arabic Learner Corpus; the Arabic Children’s Corpus; and the Arabic Dialect Text Corpus. These corpus resources have informed Arabic corpus linguistics and Artificial Intelligence research, and development of Arabic text analytics tools.
