A Framework for Building an Arabic Multi-disciplinary Ontology from Multiple Resources

Hawalah, Ahmad

doi:10.1007/s12559-017-9460-x

Cited by 7 publications

(10 citation statements)

References 34 publications

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“…However traditional Natural Language processing (NLP) barely copes with some aspects of language that require a cognitive and social perspective and suggest to shift from a word-based to a concept-based processing. This results in a multidisciplinary approach called sentic computing, which claims the importance of semantic features to study some aspects of cognitive communication trough the automatic analysis of the written language [5] [6] [7]. Moreover, different issues, like people's intentionality or sociocultural environment, can change the way in which they express themselves as well as the language people use to make an opinion explicit.…”

Section: Automatic Detection Of Peoples' Emotions In What They Say Ormentioning

confidence: 99%

“…But the 48% of the internet contents are written in other languages, being Spanish language the second one among them. Thus there is a significant risk to miss essential information in texts written in other languages [17] for which there is an important lack of resources [7]. As a consequence an increasing interest arises to develop research and, consequently linguistic resources, for other languages [17].…”

Section: Automatic Detection Of Peoples' Emotions In What They Say Ormentioning

confidence: 99%

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Detection of Sarcasm and Nastiness: New Resources for Spanish Language

et al. 2018

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The main goal of this work is to provide the Cognitive Computing community with valuable resources to analyse and simulate the intentionality and/or emotions embedded in the language employed in social media. Specifically, it is focused on the Spanish language and online dialogues, leading to the creation of Sofoco (Spanish Online Forums Corpus). It is the first Spanish corpus consisting of dialogic debates extracted from social media and it is annotated by means of crowdsourcing in order to carry out automatic analysis of subjective language forms, like sarcasm or nastiness. Furthermore, the annotators were also asked about the context need when taking a decision. In this way, the users' intentions

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Section: Automatic Detection Of Peoples' Emotions In What They Say Ormentioning

confidence: 99%

Section: Automatic Detection Of Peoples' Emotions In What They Say Ormentioning

confidence: 99%

Detection of Sarcasm and Nastiness: New Resources for Spanish Language

et al. 2018

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“…Finding many different patterns for the one Arabic word is one of the characteristics of the Arabic language where each word can consist of one or more prefixes, a stem or root, and one or more suffixes in different combinations, that lead to complicated morphology. The lack of Arabic linguistic resources and tools represents another problem to the process of the semantic relation extraction and Ontology learning [13]. These challenges also affect knowledge extraction from Arabic text.…”

Section: B Ontology Learningmentioning

confidence: 99%

“…The authors discussed the challenges of constructing ontology from Arabic texts and the solutions for each but there aren't results to show. Hawalah, (2018) proposed another approach for building an Arabic ontology from multiple resources (publicly available directory, rich data from the Internet, and Arabic online directory) [13]. The proposed approach consisted of two main phases: (1) building and extracting an Arabic ontology from a publicly available directory,…”

Section: Automatic Ontology Constructionmentioning

confidence: 99%

An Overview of Ontology Learning Process from Arabic Text

Muhammed

Azim

Gheith

2020

The Egyptian Journal of Language Engineering

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Ontology learning plays an important role in many fields especially in the Semantic Web. The success of the Semantic Web depends on the quality of its ontologies. There is a lot of research work interested in Ontology Learning for Arabic texts. Most of these works focused on three main issues: extracting the terms, extracting the semantic relations, and building the ontology from the Arabic text. In this paper, first, we present the Arabic challenges that were reasons for developing few Arabic Ontology Learning systems. Second, we make a research comparison based on the techniques used and their results. Third, we pointed out the limitations and comments of research works on Arabic Ontology Learning. Finally, we concluded the paper and outlined our future research direction in this area.

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“…The instant growth in the quantity of information accessed via the Internet has caused difficulty and frustration for those trying to find a particular piece of information. Likewise, the various kinds of information resources that exist on the Internet constitute an enormous quantity of information in the form of web pages, e-libraries, blogs, e-mails, e-documents, and news articles, all containing huge amounts of data [1]. Such information is unstructured or semi-structured, which means that the knowledge discovery process is challenging.…”

Section: Introductionmentioning

confidence: 99%

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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A Framework for Building an Arabic Multi-disciplinary Ontology from Multiple Resources

Cited by 7 publications

References 34 publications

Detection of Sarcasm and Nastiness: New Resources for Spanish Language

Detection of Sarcasm and Nastiness: New Resources for Spanish Language

An Overview of Ontology Learning Process from Arabic Text

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

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