Semantic Retrieval Based on SPARQL and Fuzzy Ontology for Electronic Commerce

Zhai, Jun; Li, Jianfeng; Lin, Yan

doi:10.4304/jcp.6.10.2127-2134

Cited by 7 publications

(3 citation statements)

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“…The various works proposed in the area of "Fuzzy Logic" is reported in this section. Semantic retrieval [6] [7] from the ontology is done on semantic web by exploring the concept of "fuzzy linguistic variable" for classification and applied successfully in both areas of "electronic commerce" and "traffic information service". The concept of fuzzy linguistic variables is applied to Resource Description Framework (RDF) data model and SPARQL query language is constructed between fuzzy concepts for semantic query expansion.…”

Section: T Sheeba Reshmy Krishnanmentioning

confidence: 99%

“…Fuzzy linguistic variable is used to make distinction on the vague knowledge representation of existing concepts of learner profile ontology. The definition of fuzzy linguistic variable [6] is given as a 4-tuple (X, T, M, U), where: X is the name of fuzzy linguistic variable; T is the set of terms which is the value of fuzzy linguistic variable; M is the mapping rules which map every term of T to fuzzy set at U; U is the universe of discourse. Initially the crisp values of learner profile concepts are used to create a fuzzy set which includes linguistic variables of each concept.…”

Section: Phase 4-semantic Retrievalmentioning

confidence: 99%

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Fuzzy Integrated Ontology Model of Dynamic Learner Profiling

Sheeba*¹,

Krishnan²

2019

IJITEE

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In the context of lifelong learning, learner profile has emerged as a feasible model that support and promote the provision of lifelong learning opportunities. Learner profile describes the attributes and outcomes of education in a learning system. It includes information on learner’s gender, skills, education, interest, learning preferences, learning style, etc. This paper proposes an approach to construct a fuzzy based semantic learner profile in the promising technology of semantic web by using the concept of ontology and use it for the reasoning of learner preferences. The approach starts with the collection of learner’s static and dynamic data. The dynamic data of learner particularly learner interest and learning style are extracted by weblog analysis and using algorithms such as semantic based representation using WordNet and modified decision tree classifier with strong rules based on Felder-Silverman learning style model. The retrieved data is then used to construct learner profile using ontology in which automatic learner profile updating is obtained using ontology based semantic similarity algorithm. Finally to achieve semantic retrieval from learner profile ontology, fuzzy concepts such as fuzzy linguistic variable and fuzzy IF THEN rules are applied. Fuzzy linguistic variable facilitate semantic retrieval and more specific classification from learner profile ontology and fuzzy IF THEN rules predict the learning preference of new students based on the forward chaining reasoning process implemented in the existing ontology model. The final representation of semantic fuzzy ontology based learner profile improves the performance of tasks such as classification, semantic retrieval and prediction of learning preference to the new learners. The case study is conducted for the real-time learners involved in studying the courses registered in Moodle Learning Management System. The experiments were performed with NetBeans IDE, Jena framework and Protégé 4.2 beta editor. The experiments confirm that the proposed learner profile is a good representation of the learner's preferences.

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Section: T Sheeba Reshmy Krishnanmentioning

confidence: 99%

Section: Phase 4-semantic Retrievalmentioning

confidence: 99%

Fuzzy Integrated Ontology Model of Dynamic Learner Profiling

Sheeba*¹,

Krishnan²

2019

IJITEE

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“…The fuzzy sets theory was introduced into the concept and relationship in the construction of ontology since then. And researches on the fuzzy ontology are emerging gradually [3][4][5][6][7][8][9][10][11][12][13] .…”

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confidence: 99%

Scientific Ontology Construction Based on Interval Valued Fuzzy Theory Under Web 2.0

Xue¹,

Jia²,

Jiang³

et al. 2013

JSW

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Construction of the unified and shared domain ontology is significant for effective knowledge management. For the acquisition and sharing of scientific research knowledge under Web2.0, a novel approach of building Interval Valued Fuzzy Ontology (IVFO) in scientific research domain is presented. Through interval valued fuzzy theory, the definition and constructing framework of IVFO is proposed. Then IVFO is applied to semi-automatic extraction of information retrieval research domain. The preliminary constructing of research domain ontology is an essential base for the knowledge management system of scientific research. It can be effective methods for enhancing the efficiency and productivity of researching.

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An Ontological Framework of Semantic Learner Profile in an E-Learning System

Sheeba

Krishnan

2016

Advances in Brain Inspired Cognitive Systems

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Semantic Retrieval Based on SPARQL and Fuzzy Ontology for Electronic Commerce

Cited by 7 publications

References 0 publications

Fuzzy Integrated Ontology Model of Dynamic Learner Profiling

Fuzzy Integrated Ontology Model of Dynamic Learner Profiling

Scientific Ontology Construction Based on Interval Valued Fuzzy Theory Under Web 2.0

An Ontological Framework of Semantic Learner Profile in an E-Learning System

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