LiFR: A Lightweight Fuzzy DL Reasoner

Tsatsou, Dorothea; Dasiopoulou, Stamatia; Kompatsiaris, Ioannis; Mezaris, Vasileios

doi:10.1007/978-3-319-11955-7_32

Cited by 14 publications

(13 citation statements)

References 9 publications

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“…Some popular fuzzy ontology reasoners are fuzzyDL [18], Fire [25], FPLGERDS [26], YADLR [27], DeLorean [28], GURDL [19], FRESG [29], LiFR [30], and SMT-based solver [31]. To the best of our knowledge, fuzzyDL, FRESG and YADLR are the only ones supporting instance retrieval, while FRESG, and YADLR are the only ones supporting realization.…”

Section: Reasoning Tasksmentioning

confidence: 99%

Algorithms for Instance Retrieval and Realization in Fuzzy Ontologies

2020

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Fuzzy description logics, the formalism behind fuzzy ontologies, are an important mathematical method with applications in many artificial intelligence scenarios. This paper proposes the first specific algorithms to solve two reasoning tasks with respect to a fuzzy ontology: the instance retrieval and the realization problem. Our algorithms are based on a reduction of the number of optimization problems to solve by merging some of them. Our experimental evaluation shows that the novel algorithm to solve the instance retrieval outperforms the previous algorithm, and that in practice it is common to be able to solve a single optimization problem.

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Section: Reasoning Tasksmentioning

confidence: 99%

Algorithms for Instance Retrieval and Realization in Fuzzy Ontologies

2020

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“…The first Fuzzy Description Logics (FDLs) were developed based on Zadeh's fuzzy semantics [95,106,110], and classical DL algorithms were extended to deal with the additional expressivity provided by the truth degrees. Since then, a multitude of combinations of description logics with fuzzy semantics have been investigated, they have been subject of several surveys and monographies [10,24,47,74,102], and many FDL reasoners have been implemented [1,14,23,86,107]. Returning to the example axiom (1), FDLs enable us to grade a flu diagnosis as mild or severe, based on the severity of its symptoms.…”

Section: Historymentioning

confidence: 99%

Fuzzy Description Logics – A Survey

Borgwardt

Peñaloza

2017

Lecture Notes in Computer Science

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“…ensuring that the ontology implements its definitions and requirements correctly [26], was based on its compliance on the questionnaires fulfilled and the requirements that the technical experts have posed. Furthermore, consistency checking of the ontology was performed via the LiFR reasoner [27], on an ABox 2 instantiating all concepts and relations of the ontology.…”

Section: B Knowledge-based Modelling Of Learning Activitiesmentioning

confidence: 99%

Modelling learning experiences in adaptive multi-agent learning environments

Tsatsou¹,

Vretos²,

Daras³

2017

2017 9th International Conference on Virtual Worlds and Games for Serious Applications (VS-Games)

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In next generation technology-enhanced learning environments, intelligent educational systems can benefit from tapping into multi-agent, adaptive, gamified learning experiences, which transform the traditional instructional paradigm from classroom-based learning to personalised learning in any setting, whether collective or individual. Such settings enable learning targeted to each individual's learning styles and needs, through the use of autonomous technological agents as actuators of the learning process. Learning components which will respond to the needs of such an educational framework should provide capabilities for adaptive, affective and interactive learning, automatic feedback and automatic assessment of the learners' behavioural state. A novel methodology is proposed to model such components, which focuses on the representation and management of learning objects (LOs) for any educational domain, any type of learner and learning style and any learning methodology, while fostering non-linearity in the educational process. This methodology is supported by a strategy for modelling and adapting re-usable learning objectives, coupled with an ontology that enables scalable and personalized decision-making over learning activities on autonomous devices, enabling dynamic modularisation of learning material during the learning process.

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LiFR: A Lightweight Fuzzy DL Reasoner

Cited by 14 publications

References 9 publications

Algorithms for Instance Retrieval and Realization in Fuzzy Ontologies

Algorithms for Instance Retrieval and Realization in Fuzzy Ontologies

Fuzzy Description Logics – A Survey

Modelling learning experiences in adaptive multi-agent learning environments

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