Automatic extraction of ontological relations from Arabic text

Zamil, Mohammed G. H. al; Al-Radaideh, Qasem A.

doi:10.1016/j.jksuci.2014.06.007

Cited by 29 publications

(22 citation statements)

References 8 publications

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“…Because of ontologies importance in the semantic web, many research developed several ontology learning systems and approaches in different languages such as OntoLearn, Alvis, Text2Onto, and SPRAT [10]. Such systems aren't well for syntactically ambiguous languages such as Arabic compared to English [11]; where several challenges are facing the process of Ontology learning with Arabic text. Such challenges are the absence of capitalization, absence of diacritics, complicated morphology and the lack of resources.…”

Section: B Ontology Learningmentioning

confidence: 99%

“…This algorithm is considered a low-cost approach for the automatic extraction of semantic lexical relations from text. However, it is inefficient in Arabic text that leads some researchers such as [11] to deal with the Arabic language to overcome such drawbacks. (3) the third phase is to avoid having redundant patterns; and (4) final phase is filtering and aggregation of the pattern.…”

Section: Semantic Relation Extractionmentioning

confidence: 99%

“…Zamil et al, (2014) proposed a technique that is implemented as an enhanced version of Hearst's algorithm. The steps of this technique are as follows:(1) analyzing Arabic text using lexical-semantic patterns of the Arabic language according to a set of features such as POS tag features; (2) building lexical syntactic patterns of Arabic text by enhancing the algorithm of Hearst;…”

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confidence: 99%

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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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Section: B Ontology Learningmentioning

confidence: 99%

Section: Semantic Relation Extractionmentioning

confidence: 99%

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confidence: 99%

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An Overview of Ontology Learning Process from Arabic Text

Muhammed

Azim

Gheith

2020

The Egyptian Journal of Language Engineering

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“…In order to avoid these shortcomings, ontology learning has evolved to automate or semi-automate the construction of ontologies. Ontology learning includes knowledge extraction through two principle tasks: concepts extraction (which constitute the ontology) and extracting the semantic relations that link them [7][8][9].…”

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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“…Major works in Arabic include the extraction of rhetorical relations [15] [16] and general semantic relations explicitly stated in a text [17], Wikipedia relations [18], synonyms [19], relations between named entities [20], verbs [21], spatial relations [22], grammatical relations [23], and ontological relations [24] [25]. The only study in our review that explicitly mentions antonymy as one of the extracted relations is [19], which uses morpho-lexical patterns applied on a set of Wikipedia articles as a corpus to enrich WordNet, but the results obtained for antonyms were 0%.…”

Section: Introductionmentioning

confidence: 99%

Ontological Lexicon Enrichment: The Badea System For Semi-Automated Extraction Of Antonymy Relations From Arabic Language Corpora

Al-Yahya

Al-Malak

Aldhubayi

2016

MJCS

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Ontological lexicons are considered a rich source of knowledge for the development of various natural language processing tools and applications; however, they are expensive to build, maintain, and extend. In this paper, we present the Badea system for the semi-automated extraction of lexical relations, specifically antonyms using a pattern-based approach to support the task of ontological lexicon enrichment. The approach is based on an ontology of "seed" pairs of antonyms in the Arabic language; we identify patterns in which the pairs occur and then use the patterns identified to find new antonym pairs in an Arabic textual corpora. Experiments are conducted on Badea using texts from three Arabic textual corpuses: KSUCCA, KACSTAC, and CAC. The system is evaluated and the patterns' reliability and system performance is measured. The results from our experiments on the three Arabic corpora show that the pattern-based approach can be useful in the ontological enrichment task, as the evaluation of the system resulted in the ontology being updated with over 300 new antonym pairs, thereby enriching the lexicon and increasing its size by over 400%. Moreover, the results show important findings on the reliability of patterns in extracting antonyms for Arabic. The Badea system will facilitate the enrichment of ontological lexicons that can be very useful in any Arabic natural language processing system that requires semantic relation extraction.Keywords: Antonym Extraction, Ontology, Arabic Lexicon, Semantic Relation, Arabic NLP INTRODUCTIONA lexicon is defined as "the knowledge that a native speaker has about a language. This includes information about the form and meanings of words and phrases, lexical categorization, the appropriate usage of words and phrases, relationships between words and phrases, and categories of words and phrases" [1]. Lexicon is an essential element for natural language processing (NLP) applications. For some applications, such as machine translation, lexicon is a critical resource [2]. An important aspect of such lexicons that renders them effective, reusable, and sharable within the community is building them upon standards such as semantic Web standards in the form of ontologies. An ontological lexicon is a lexicon designed using an ontological model and developed as an ontology. Ontological lexicons play a vital role in NLP applications such as language analysis, semantic annotation, summarization, machine translation, sense disambiguation, generation of lexical-competence questions used in standard language tests, and other applications that rely on implicit information in the text.Although ontological lexicons provide a rich source of knowledge for NLP applications, like other types of computational lexicons, they are expensive to build, maintain, and extend [2] [3] [4]. Moreover, the task of relation extraction is essential in any ontological lexicon development. Relation extraction focuses on the extraction of structured relations from unstructured sources such as Web documents ...

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Automatic extraction of ontological relations from Arabic text

Cited by 29 publications

References 8 publications

An Overview of Ontology Learning Process from Arabic Text

An Overview of Ontology Learning Process from Arabic Text

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

Ontological Lexicon Enrichment: The Badea System For Semi-Automated Extraction Of Antonymy Relations From Arabic Language Corpora

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