Extracting semantic relations from the Quranic Arabic based on Arabic conjunctive patterns

Bentrcia, Rahima; Zidat, Samir; Marir, Farhi

doi:10.1016/j.jksuci.2017.09.004

Cited by 12 publications

(13 citation statements)

References 14 publications

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“…The rule-based approaches [12,20,21,[60][61][62] rely on patterns comprising all the possibly-correlated linguistic sequences commonly executed in a form of finite-state transducers or even regular expressions. Despite those methods being beneficial for a limited domain, besides their better analysis quality, they cannot act in a good way, in particular, the creation of the manually hand-crafted patterns is so laborious with regard to effort and time.…”

Section: Literature Review On Arabic Ontology Learningmentioning

confidence: 99%

“…In summary, only a few studies have considered automatic ontology learning from Arabic text [4,9,12,[19][20][21][22][23]. These works fall into one of the following three categories: handcrafted rule-based methods [12,20,21], machine learning methods [9,19,20,22], and hybrid rule-based/machine learning methods [4,23]. The studies that have introduced rule-based approaches for ontology learning are based on extracting the semantic relationships between Arabic concepts or Arabic named entities, and utilize the same technique, which can identify linguistic patterns from a given corpus.…”

Section: Introductionmentioning

confidence: 99%

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A Novel Hybrid Genetic-Whale Optimization Model for Ontology Learning from Arabic Text

2019

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Ontologies are used to model knowledge in several domains of interest, such as the biomedical domain. Conceptualization is the basic task for ontology building. Concepts are identified, and then they are linked through their semantic relationships. Recently, ontologies have constituted a crucial part of modern semantic webs because they can convert a web of documents into a web of things. Although ontology learning generally occupies a large space in computer science, Arabic ontology learning, in particular, is underdeveloped due to the Arabic language’s nature as well as the profundity required in this domain. The previously published research on Arabic ontology learning from text falls into three categories: developing manually hand-crafted rules, using ordinary supervised/unsupervised machine learning algorithms, or a hybrid of these two approaches. The model proposed in this work contributes to Arabic ontology learning in two ways. First, a text mining algorithm is proposed for extracting concepts and their semantic relations from text documents. The algorithm calculates the concept frequency weights using the term frequency weights. Then, it calculates the weights of concept similarity using the information of the ontology structure, involving (1) the concept’s path distance, (2) the concept’s distribution layer, and (3) the mutual parent concept’s distribution layer. Then, feature mapping is performed by assigning the concepts’ similarities to the concept features. Second, a hybrid genetic-whale optimization algorithm was proposed to optimize ontology learning from Arabic text. The operator of the G-WOA is a hybrid operator integrating GA’s mutation, crossover, and selection processes with the WOA’s processes (encircling prey, attacking of bubble-net, and searching for prey) to fulfill the balance between both exploitation and exploration, and to find the solutions that exhibit the highest fitness. For evaluating the performance of the ontology learning approach, extensive comparisons are conducted using different Arabic corpora and bio-inspired optimization algorithms. Furthermore, two publicly available non-Arabic corpora are used to compare the efficiency of the proposed approach with those of other languages. The results reveal that the proposed genetic-whale optimization algorithm outperforms the other compared algorithms across all the Arabic corpora in terms of precision, recall, and F-score measures. Moreover, the proposed approach outperforms the state-of-the-art methods of ontology learning from Arabic and non-Arabic texts in terms of these three measures.

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Section: Literature Review On Arabic Ontology Learningmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Novel Hybrid Genetic-Whale Optimization Model for Ontology Learning from Arabic Text

2019

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“…They furthermore show that the views on sacred words rest on the implicit ground and this explains why certain theories about the sacred words can become contentious and problematic. Bentricia, Zidat, and Farhi (2017) investigate the semantics of the Quranic Arabic by extracting the semantics in an approach based on the Arabic conjunctive patterns. looks at the language change and proposes a model to account for the brutal language change.…”

Section: Semantic and Symbolic Function Of Sacred Wordsmentioning

confidence: 99%

A Cognitive Approach to the Semantics in the Sacred Context: Semantic and Symbolic Function of Sacred Words

Singh¹

2018

ELR

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One of the intriguing features of language interaction with society and culture is the position of certain words as sacred within that society. Thus, it is important to analyze the social process through which sacred words present their particular features. In this paper, we show how sacred words gain their symbolic prominence. Furthermore, we propose a cognitive-semantic model based on the hypothesis of historic automaticity chain that explains well the reason behind the loss of semantics of the sacred words. In this paper, we compare some sacred words across many Indo-European languages and analyze how the very same sacred words lost ground to other words and became almost empty of semantics and word origin, while still preserving the symbolic notion. This study brings the notion of abstraction to the sacred word framework and clarifies the ways the mind processes sacred semantics. In order to support our hypothesis, we performed two small-scale psycho-linguistic experiments and the results confirmed our hypothesis.Keywords: semantics, cognitive process, symbolic, function, sacred words, experiment, sociolinguistics Semantic and Symbolic Function of Sacred WordsSacredness of words has always been a vehicle to transfer values and moralities from one culture to another, from one faith to another, and so on. However, there is a clear evidence of the difficulty to translate sacred words. It is therefore important to investigate the reasons behind this apparent loss of words while translating sacred words from one language to another. It is even more interesting when the basic sacred words are hard to explain for their followers and the semantics seems missing in the whole definition. Most often sacred words have obscure origin and linguists after years of research still have hard times to find the very origin of such words used in a daily fashion by millions. When we look at the sacred context, rituals and religious ceremonies present us with a realm of sacred words which semantics seem hard to explain and to define. Tambiah (1968) investigates the importance and position of words in ritual contemporary anthropology and analyzes the intelligibility of sacred words to cultures wherein these words were first formed. Veal and Hao (2008) investigate metaphor as a phenomenon that manipulates the conventions of semantic description, and present a computational model that is as flexible as the metaphor in order to interpret and generate metaphors in the language context.Comparative analysis of the semantics and grammar in the sacred context is of importance and many aspects of the difficulty dealing with the sacred words are better understood in a comparative context. Tournadre (2010) puts emphasis on the irregularities of describing grammar rules between cultures of different religions and analyzes the classical Tibetan cases and their trans-categoriality based on the treatises and grammars written specially for sacred books. White (1944) investigates the claim that certain words in American Indian languages are...

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“…Several works aim to construct an ontology, (Suresh & Zayara, 2014) used a syntactic and semantic probability-based naive Bayes classifier to extract concept relations from the unstructured text for the automatic construction of ontology, for that a list of attributes and associations of the given seeds concept are automatically extracted. In reference (Bentricia et al, 2017) proposed an approach based on conjunctive patterns to extract semantic relations from Quranic Arabic corpus to enrich automatic construction of Quran ontology. The role to construct such ontology is to provide precise and comprehensive knowledge to the world, with the aim to reduce the role of expert knowledge to built ontology.…”

Section: Related Workmentioning

confidence: 99%

Query Expansion Using Medical Information Extraction for Improving Information Retrieval in French Medical Domain

Ghoulam

Barigou

Belalem

et al. 2018

International Journal of Intelligent Information Technologies

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Many users' queries contain references to named entities, and this is particularly true in the medical field. Doctors express their information needs using medical entities as they are elements rich with information that helps to better target the relevant documents. At the same time, many resources have been recognized as a large container of medical entities and relationships between them such as clinical reports; which are medical texts written by doctors. In this paper, we present a query expansion method that uses medical entities and their semantic relations in the query context based on an external resource in OWL. The goal of this method is to evaluate the effectiveness of an information retrieval system to support doctors in accessing easily relevant information. Experiments on a collection of real clinical reports show that our approach reveals interesting improvements in precision, recall and MAP in medical information retrieval.

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Extracting semantic relations from the Quranic Arabic based on Arabic conjunctive patterns

Cited by 12 publications

References 14 publications

A Novel Hybrid Genetic-Whale Optimization Model for Ontology Learning from Arabic Text

A Novel Hybrid Genetic-Whale Optimization Model for Ontology Learning from Arabic Text

A Cognitive Approach to the Semantics in the Sacred Context: Semantic and Symbolic Function of Sacred Words

Query Expansion Using Medical Information Extraction for Improving Information Retrieval in French Medical Domain

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