A learning-based ontology alignment approach using inductive logic programming

Karimi, Hamed; Kamandi, Ali

doi:10.1016/j.eswa.2019.02.014

Cited by 18 publications

(14 citation statements)

References 20 publications

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“…More commonly, e-learning is being effectively used by the universities for the conventional forms of education and administration. Typically, e-learning involves asynchronous and asynchronous modes of communication (Saleena and Srivatsa, January, 2015;Kang et al, 2020;Karimi and Kamandi, 2019;Gao et al, 2018). The synchronous modes of communication are alike the traditional form of learning, where the electronic data is used to carry out the learning process in real time.…”

Section: Introductionmentioning

confidence: 99%

Ontology alignment evaluation for online assessment of e-learners: a new e-learning management system

Rajakumar

Yenduri

Vyas

et al. 2021

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Purpose This paper aims to propose a new assessment system module for handling the comprehensive answers written through the answer interface. Design/methodology/approach The working principle is under three major phases: Preliminary semantic processing: In the pre-processing work, the keywords are extracted for each answer given by the course instructor. In fact, this answer is actually considered as the key to evaluating the answers written by the e-learners. Keyword and semantic processing of e-learners for hierarchical clustering-based ontology construction: For each answer given by each student, the keywords and the semantic information are extracted and clustered (hierarchical clustering) using a new improved rider optimization algorithm known as Rider with Randomized Overtaker Update (RR-OU). Ontology matching evaluation: Once the ontology structures are completed, a new alignment procedure is used to find out the similarity between two different documents. Moreover, the objects defined in this work focuses on “how exactly the matching process is done for evaluating the document.” Finally, the e-learners are classified based on their grades. Findings On observing the outcomes, the proposed model shows less relative mean squared error measure when weights were (0.5, 0, 0.5), and it was 71.78% and 16.92% better than the error values attained for (0, 0.5, 0.5) and (0.5, 0.5, 0). On examining the outcomes, the values of error attained for (1, 0, 0) were found to be lower than the values when weights were (0, 0, 1) and (0, 1, 0). Here, the mean absolute error (MAE) measure for weight (1, 0, 0) was 33.99% and 51.52% better than the MAE value for weights (0, 0, 1) and (0, 1, 0). On analyzing the overall error analysis, the mean absolute percentage error of the implemented RR-OU model was 3.74% and 56.53% better than k-means and collaborative filtering + Onto + sequential pattern mining models, respectively. Originality/value This paper adopts the latest optimization algorithm called RR-OU for proposing a new assessment system module for handling the comprehensive answers written through the answer interface. To the best of the authors’ knowledge, this is the first work that uses RR-OU-based optimization for developing a new ontology alignment-based online assessment of e-learners.

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Section: Introductionmentioning

confidence: 99%

Ontology alignment evaluation for online assessment of e-learners: a new e-learning management system

Rajakumar

Yenduri

Vyas

et al. 2021

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“…Different matchers have been proposed on a yearly basis (Thiéblin et al, 2019;Mohammadi et al, 2019Mohammadi et al, , 2018Chauhan et al, 2018;Xue and Wang, 2017) because of the fact that ontology alignment is a complex matter, which can be tackled by many approaches Multiple ontology matchers 151 such as by using linguistic techniques (Kolyvakis et al, 2018;Wu et al, 2016;Van Hage et al, 2005), the graph theory (Quintero et al, 2018;Li et al, 2018;Zang et al, 2016) and mathematical logic (Karimi and Kamandi, 2019;Jayasri and Rajmohan, 2015;Sengupta and Hitzler, 2015;Janowicz and Wilkes, 2009). In addition, each matcher tries to solve the problem taking into account a subset of characteristics.…”

Section: Introductionmentioning

confidence: 99%

A framework to aggregate multiple ontology matchers

Souza

Siqueira

Nunes

2019

IJWIS

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Purpose Although ontology matchers are annually proposed to address different aspects of the semantic heterogeneity problem, finding the most suitable alignment approach is still an issue. This study aims to propose a computational solution for ontology meta-matching (OMM) and a framework designed for developers to make use of alignment techniques in their applications. Design/methodology/approach The framework includes some similarity functions that can be chosen by developers and then, automatically, set weights for each function to obtain better alignments. To evaluate the framework, several simulations were performed with a data set from the Ontology Alignment Evaluation Initiative. Simple similarity functions were used, rather than aligners known in the literature, to demonstrate that the results would be more influenced by the proposed meta-alignment approach than the functions used. Findings The results showed that the framework is able to adapt to different test cases. The approach achieved better results when compared with existing ontology meta-matchers. Originality/value Although approaches for OMM have been proposed, it is not easy to use them during software development. On the other hand, this work presents a framework that can be used by developers to align ontologies. New ontology matchers can be added and the framework is extensible to new methods. Moreover, this work presents a novel OMM approach modeled as a linear equation system which can be easily computed.

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“…By comparison to the other languages, several studies have been conducted for learning ontology from English text [24][25][26], which has achieved the largest number of contributions among the other languages. Some of these studies presented rule-based approaches [24], and the others proposed machine-learning-based approaches [25,26]. In [24], the authors presented a rule-based approach for learning the English ontology in which the inductive logic programming was used to obtain ontology mapping.…”

Section: Introductionmentioning

confidence: 99%

“…Some of these studies presented rule-based approaches [24], and the others proposed machine-learning-based approaches [25,26]. In [24], the authors presented a rule-based approach for learning the English ontology in which the inductive logic programming was used to obtain ontology mapping. This method described the ontology in OWL format and then interpreted it into first-order logic.…”

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 learning-based ontology alignment approach using inductive logic programming

Cited by 18 publications

References 20 publications

Ontology alignment evaluation for online assessment of e-learners: a new e-learning management system

Ontology alignment evaluation for online assessment of e-learners: a new e-learning management system

A framework to aggregate multiple ontology matchers

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

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