Ontological Optimization for Latent Semantic Indexing of Arabic Corpus

Al-Zoghby, Aya M.; Shaalan, Khaled

doi:10.1016/j.procs.2018.10.477

Cited by 6 publications

(4 citation statements)

References 7 publications

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“…The differences in the approaches that exist in the literature review for textual similarity depend on the text representation scheme used before text comparison. There are different text representation schemes suggested by researchers likes Term Frequency-Inverse Document Frequency (TF-IDF) 1 , Latent Semantic Indexing (LSI) 2 , and Graph-based Representation [3], [4], [5]. Due to these ways, the similarity measure to compare text units also differs because one similarity measure may not be convenient for all representation schemes.…”

Section: Introductionmentioning

confidence: 99%

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Textual Similarity Measurement Approaches: A Survey (1)

Abo-Elghit

Al-Zoghby

Hamza

2020

The Egyptian Journal of Language Engineering

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Survey research is appropriate and necessary to address certain research question types. This paper aims to provide a general overview of the textual similarity in the literature. Measuring textual similarity tends to have an increasingly important turn in related topics like text classification, recovery of specific information from data, clustering, topic retrieval, subject tracking, question answering, essay grading, summarization, and the nowadays trending Conversational Agents (CA), which is a program deals with humans through natural language conversation. Finding the similarity between terms is the essential portion of textual similarity, then used as a major phase for sentence-level, paragraph-level, and script-level similarities. In particular, we concern with textual similarity in Arabic. In the Arabic Language, applying Natural language Processing (NLP) tasks are very challenging indeed as it has many characteristics, which are considered as confrontations. However, many approaches for measuring textual similarity have been presented for Arabic text reviewed and compared in this paper.

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Section: Introductionmentioning

confidence: 99%

“…https://www.researchgate.net/publication/305406094_Arabic_WordNet_New_Content_and_New_Applications4 http://godel.iis.sinica.edu.tw/taxonomy/taxonomy-edoc.htm5 https://babelnet.org/6 http://www.linguatools.de/disco/disco_en.html…”

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confidence: 99%

Textual Similarity Measurement Approaches: A Survey (1)

Abo-Elghit

Al-Zoghby

Hamza

2020

The Egyptian Journal of Language Engineering

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“…Despite the Arabic language's importance as the sixth most spoken language in the world [2] and the tremendous growth of Arabic content via the web in recent years, it has been given little attention in the ontology learning field [10][11][12]. Several contributions are available on domain ontologies in English [13][14][15] and other languages.…”

Section: Introductionmentioning

confidence: 99%

“…Eventually, it performs the mapping of features by assigning the concept similarity to the concept features. Unlike the ordinary text mining algorithms [9,10], this property is crucial because merging the concept frequency weights with the concept similarity weights supports the detection of Arabic semantic information and optimizes the ontology learning.…”

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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An optimized iterative clustering framework for recognizing speech

Palanivinayagam

Nagarajan

2020

Int J Speech Technol

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Ontological Optimization for Latent Semantic Indexing of Arabic Corpus

Cited by 6 publications

References 7 publications

Textual Similarity Measurement Approaches: A Survey (1)

Textual Similarity Measurement Approaches: A Survey (1)

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

An optimized iterative clustering framework for recognizing speech

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