Using concept similarity in cross ontology for adaptive e-Learning systems

Saleena, B.; Srivatsa, S. K.

doi:10.1016/j.jksuci.2014.03.007

Cited by 29 publications

(17 citation statements)

References 6 publications

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“…In this way, common features tend to increase similarity and non‐common ones tend to diminish it. This approach is originated from Tversky model and has been applied in molecular biology, adaptive e‐learning and ontology merging and alignment Information‐based approaches calculates statistical specification of concepts based on a corpus or other data sources .…”

Section: Discussionmentioning

confidence: 99%

Using Fuzzy Ontology to Improve Similarity Assessment: Method and Evaluation

Safaeipour

Zarandi

Bastani

2017

Int. J. Intell. Syst.

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Assessing semantic similarity is a fundamental requirement for many AI applications. Crisp ontology (CO) is one of the knowledge representation tools that can be used for this purpose. Thanks to the development of semantic web, CO‐based similarity assessment has become a popular approach in recent years. However, in the presence of vague information, CO cannot consider uncertainty of relations between concepts. On the other hand, fuzzy ontology (FO) can effectively process uncertainty of concepts and their relations. This paper aims at proposing an approach for assessing concept similarity based on FO. The proposed approach incorporates fuzzy relation composition in combination with an edge counting approach to assess the similarity. Accordingly, proposed measure relies on taxonomical features of an ontology in combination with statistical features of concepts. Furthermore, an evaluation approach for the FO‐based similarity measure named as FOSE is proposed. Considering social network data, proposed similarity measure is evaluated using FOSE. The evaluation results prove the dominance of proposed approach over its respective CO‐based measure.

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Section: Discussionmentioning

confidence: 99%

Using Fuzzy Ontology to Improve Similarity Assessment: Method and Evaluation

Safaeipour

Zarandi

Bastani

2017

Int. J. Intell. Syst.

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“…Another analogy that can be used in driver categorization comes from domain of e-learning i.e. categorization of learners performed at concept level [17] that in turn is evaluated based on percent concepts covered in knowledge. After assessments, learners are classified based upon two aspects intellectually i.e.…”

Section: Literature Surveymentioning

confidence: 99%

Context aware ontology‐based hybrid intelligent framework for vehicle driver categorization

Sarwar

Zia

Qayyum

et al. 2019

Trans Emerging Tel Tech

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In public vehicles, one of the major concerns is driver's level of expertise for its direct proportionality to safety of passengers. So before a driver is subjected to certain type of vehicle, he should be thoroughly evaluated and categorized with respect to certain parameters instead of only one-time metric of having driving license. These aspects may be driver's expertise, vigilance, aptitude, experience years, cognition, driving style, formal education, terrain, region, minor violations, major accidents and age group etc. The purpose of this categorization is to ascertain suitability of a driver for certain vehicle type(s) to ensure passengers' safety. Currently, no driver categorization technique fully comprehends the implicit as well as explicit characteristics of drivers dynamically. In this paper, machine learning based dynamic and adaptive technique named D-CHAIT (Driver Categorization based on Hybrid Artificial Intelligence Techniques) is proposed for driver categorization with an objective focus on driver's attributes modeled in DriverOntology. A supervised mode of learning has been employed on a labeled dataset, having diverse profiles of drivers with attributes pertinent to drivers' perspectives of demographics, behaviors, expertise and inclinations. A comparative analysis of D-CHAIT with three other machine learning techniques (Fuzzy Logic, Case Based Reasoning, and Artificial Neural Networks) is also presented. The efficacy of all techniques was empirically measured while categorizing the drivers based on their profiles through metrics of accuracy, precision, recall, f-measure performance and associated costs. These empirical quantifications assert D-CHAIT as a better technique than contemporary ones. The novelty of proposed technique is signified through preprocessing of feature attributes, quality of data, training of machine learning model on more relevant data and adaptivity.

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“…As a rough reflection of the semantic relations, the results are not satisfactory (Olivares & Guzman, 2004). In addition to the concept, the structure of the ontology also contains a considerable amount of semantics, which can provide rich information for mapping (Saleena & Srivatsa, 2015). Naturally, many related studies have used the mapping strategy based on ontology structure (Magnani, Rizopoulos, & Brien, 2005;Matteo & Danilo, 2010), which measures the mapping between two ontologies through the structure of elements (Olivia, 2014).…”

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confidence: 99%

Variable weight semantic graph‐based ontology mapping method

Yang

2018

Expert Systems

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The mapping method that is based on the name and structure of the ontology elements is the strategy used in most mapping methods. Methods using the name often only use the similarity between the individual elements in the ontology to predict the semantic relations between two ontologies, while the latter measure the mapping between two ontologies by means of the structural relations between the elements. The effects of these two kinds of mapping strategies are not ideal. Addressing this issue, the work presented in this paper proposes an ontology mapping approach, in which the ontology element name and structure are combined. It uses the approaches based on linguistics and distance to generate a variable weight semantic graph. On this graph, the similarity of element names and structure are calculated through iterative computation. In the process of iteration, similarity result values are constantly adjusted. The approach avoids the problem of single methods that cannot use the entire amount of ontology information; therefore, it provides a more ideal mapping result. For making full use of the message of ontology, our implementation and experimental results are provided to demonstrate the effectiveness of the mapping approach.

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Using concept similarity in cross ontology for adaptive e-Learning systems

Cited by 29 publications

References 6 publications

Using Fuzzy Ontology to Improve Similarity Assessment: Method and Evaluation

Using Fuzzy Ontology to Improve Similarity Assessment: Method and Evaluation

Context aware ontology‐based hybrid intelligent framework for vehicle driver categorization

Variable weight semantic graph‐based ontology mapping method

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