The Experts below are selected from a list of 4983 Experts worldwide ranked by ideXlab platform
Sreenivasa P Kumar - One of the best experts on this subject based on the ideXlab platform.
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a novel approach to generate mcqs from domain ontology
2015Co-Authors: E Vinu, Sreenivasa P KumarAbstract:Ontologies are structures, used for knowledge representation, which model domain knowledge in the form of concepts, roles, instances and their relationships. This knowledge can be exploited by an assessment system in the form of multiple choice questions (MCQs). The existing approaches, which use ontologies expressed in the Web Ontology Language (OWL) for MCQ generation, are limited to simple concept related questions - "What is C?" or "Which of the following is an example of C?" (where C is a concept symbol) - or analogy type questions involving roles. There are no efforts in the literature which make use of the terminological axioms in the ontology such as existential, universal and cardinality restrictions on concepts and roles for MCQ generation. Also, there are no systematic methods for generating incorrect answers (distractors) from ontologies. Distractor generation process has to be given much importance, since the generated distractors determine the quality and hardness of an MCQ. We propose two new MCQ generation approaches, which generate MCQs that are very useful and realistic in conducting assessment tests, and the corresponding distractor generating techniques. Our distractor generation techniques, unlike other methods, consider the Open-World Assumption, so that the generated MCQs will always be valid (falsity of distractors is ensured). Furthermore, we present a measure to determine the difficulty level (a value between 0 and 1) of the generated MCQs. The proposed system is implemented, and experiments on specific ontologies have shown the effectiveness of the approaches. We also did an empirical study by generating question items from a real-World ontology and validated our results with the help of domain experts.
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a novel approach to generate mcqs from domain ontology considering dl semantics and Open World Assumption
2015Co-Authors: E Vinu, Sreenivasa P KumarAbstract:Ontologies are structures, used for knowledge representation, which model domain knowledge in the form of concepts, roles, instances and their relationships. This knowledge can be exploited by an assessment system in the form of multiple choice questions (MCQs). The existing approaches which use ontologies expressed in the Web Ontology Language (OWL) for MCQ generation, are limited to simple concept related questions — “What is C?” or “Which of the following is an example of C?” (where C is a concept symbol) — or analogy type questions involving roles. There are no efforts in the literature which make use of the terminological axioms in the ontology such as existential, universal and cardinality restrictions on concepts and roles for MCQ generation. Also, there are no systematic methods for generating incorrect answers (distractors) from ontologies. Distractor generation process has to be given much importance, since the generated distractors determine the quality and hardness of an MCQ. We propose two new MCQ generation approaches, which generate MCQs that are very useful and realistic in conducting assessment tests, and the corresponding distractor generating techniques. Our distractor generation techniques, unlike other methods, consider the Open-World Assumption, so that the generated MCQs will always be valid (falsity of distractors is ensured). Furthermore, we present a measure to determine the difficulty level (a value between 0 and 1) of the generated MCQs. The proposed system is implemented, and experiments on specific ontologies have shown the effectiveness of the approaches. We also did an empirical study by generating question items from a real-World ontology and validated our results with the help of domain experts.
Felix T. S. Chan - One of the best experts on this subject based on the ideXlab platform.
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an extension to deng s entropy in the Open World Assumption with an application in sensor data fusion
2018Co-Authors: Yongchuan Tang, Deyun Zhou, Felix T. S. ChanAbstract:Quantification of uncertain degree in the Dempster-Shafer evidence theory (DST) framework with belief entropy is still an Open issue, even a blank field for the Open World Assumption. Currently, the existed uncertainty measures in the DST framework are limited to the closed World where the frame of discernment (FOD) is assumed to be complete. To address this issue, this paper focuses on extending a belief entropy to the Open World by considering the uncertain information represented as the FOD and the nonzero mass function of the empty set simultaneously. An extension to Deng’s entropy in the Open World Assumption (EDEOW) is proposed as a generalization of the Deng’s entropy and it can be degenerated to the Deng entropy in the closed World wherever necessary. In order to test the reasonability and effectiveness of the extended belief entropy, an EDEOW-based information fusion approach is proposed and applied to sensor data fusion under uncertainty circumstance. The experimental results verify the usefulness and applicability of the extended measure as well as the modified sensor data fusion method. In addition, a few Open issues still exist in the current work: the necessary properties for a belief entropy in the Open World Assumption, whether there exists a belief entropy that satisfies all the existed properties, and what is the most proper fusion frame for sensor data fusion under uncertainty.
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An Extension to Deng’s Entropy in the Open World Assumption with an Application in Sensor Data Fusion
2018Co-Authors: Yongchuan Tang, Deyun Zhou, Felix T. S. ChanAbstract:Quantification of uncertain degree in the Dempster-Shafer evidence theory (DST) framework with belief entropy is still an Open issue, even a blank field for the Open World Assumption. Currently, the existed uncertainty measures in the DST framework are limited to the closed World where the frame of discernment (FOD) is assumed to be complete. To address this issue, this paper focuses on extending a belief entropy to the Open World by considering the uncertain information represented as the FOD and the nonzero mass function of the empty set simultaneously. An extension to Deng’s entropy in the Open World Assumption (EDEOW) is proposed as a generalization of the Deng’s entropy and it can be degenerated to the Deng entropy in the closed World wherever necessary. In order to test the reasonability and effectiveness of the extended belief entropy, an EDEOW-based information fusion approach is proposed and applied to sensor data fusion under uncertainty circumstance. The experimental results verify the usefulness and applicability of the extended measure as well as the modified sensor data fusion method. In addition, a few Open issues still exist in the current work: the necessary properties for a belief entropy in the Open World Assumption, whether there exists a belief entropy that satisfies all the existed properties, and what is the most proper fusion frame for sensor data fusion under uncertainty
E Vinu - One of the best experts on this subject based on the ideXlab platform.
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a novel approach to generate mcqs from domain ontology
2015Co-Authors: E Vinu, Sreenivasa P KumarAbstract:Ontologies are structures, used for knowledge representation, which model domain knowledge in the form of concepts, roles, instances and their relationships. This knowledge can be exploited by an assessment system in the form of multiple choice questions (MCQs). The existing approaches, which use ontologies expressed in the Web Ontology Language (OWL) for MCQ generation, are limited to simple concept related questions - "What is C?" or "Which of the following is an example of C?" (where C is a concept symbol) - or analogy type questions involving roles. There are no efforts in the literature which make use of the terminological axioms in the ontology such as existential, universal and cardinality restrictions on concepts and roles for MCQ generation. Also, there are no systematic methods for generating incorrect answers (distractors) from ontologies. Distractor generation process has to be given much importance, since the generated distractors determine the quality and hardness of an MCQ. We propose two new MCQ generation approaches, which generate MCQs that are very useful and realistic in conducting assessment tests, and the corresponding distractor generating techniques. Our distractor generation techniques, unlike other methods, consider the Open-World Assumption, so that the generated MCQs will always be valid (falsity of distractors is ensured). Furthermore, we present a measure to determine the difficulty level (a value between 0 and 1) of the generated MCQs. The proposed system is implemented, and experiments on specific ontologies have shown the effectiveness of the approaches. We also did an empirical study by generating question items from a real-World ontology and validated our results with the help of domain experts.
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a novel approach to generate mcqs from domain ontology considering dl semantics and Open World Assumption
2015Co-Authors: E Vinu, Sreenivasa P KumarAbstract:Ontologies are structures, used for knowledge representation, which model domain knowledge in the form of concepts, roles, instances and their relationships. This knowledge can be exploited by an assessment system in the form of multiple choice questions (MCQs). The existing approaches which use ontologies expressed in the Web Ontology Language (OWL) for MCQ generation, are limited to simple concept related questions — “What is C?” or “Which of the following is an example of C?” (where C is a concept symbol) — or analogy type questions involving roles. There are no efforts in the literature which make use of the terminological axioms in the ontology such as existential, universal and cardinality restrictions on concepts and roles for MCQ generation. Also, there are no systematic methods for generating incorrect answers (distractors) from ontologies. Distractor generation process has to be given much importance, since the generated distractors determine the quality and hardness of an MCQ. We propose two new MCQ generation approaches, which generate MCQs that are very useful and realistic in conducting assessment tests, and the corresponding distractor generating techniques. Our distractor generation techniques, unlike other methods, consider the Open-World Assumption, so that the generated MCQs will always be valid (falsity of distractors is ensured). Furthermore, we present a measure to determine the difficulty level (a value between 0 and 1) of the generated MCQs. The proposed system is implemented, and experiments on specific ontologies have shown the effectiveness of the approaches. We also did an empirical study by generating question items from a real-World ontology and validated our results with the help of domain experts.
Yongchuan Tang - One of the best experts on this subject based on the ideXlab platform.
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An Improved Approach of Incomplete Information Fusion and Its Application in Sensor Data-Based Fault Diagnosis
2021Co-Authors: Yutong Chen, Yongchuan TangAbstract:The Dempster–Shafer evidence theory has been widely used in the field of data fusion. However, with further research, incomplete information under the Open World Assumption has been discovered as a new type of uncertain information. The classical Dempster’s combination rules are difficult to solve the related problems of incomplete information under the Open World Assumption. At the same time, partial information entropy, such as the Deng entropy is also not applicable to deal with problems under the Open World Assumption. Therefore, this paper proposes a new method framework to process uncertain information and fuse incomplete data. This method is based on an extension to the Deng entropy in the Open World Assumption, negation of basic probability assignment (BPA), and the generalized combination rule. The proposed method can solve the problem of incomplete information under the Open World Assumption, and obtain more uncertain information through the negative processing of BPA, which improves the accuracy of the results. The results of applying this method to fault diagnosis of electronic rotor examples show that, compared with the other uncertain information processing and fusion methods, the proposed method has wider adaptability and higher accuracy, and is more conducive to practical engineering applications
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an extension to deng s entropy in the Open World Assumption with an application in sensor data fusion
2018Co-Authors: Yongchuan Tang, Deyun Zhou, Felix T. S. ChanAbstract:Quantification of uncertain degree in the Dempster-Shafer evidence theory (DST) framework with belief entropy is still an Open issue, even a blank field for the Open World Assumption. Currently, the existed uncertainty measures in the DST framework are limited to the closed World where the frame of discernment (FOD) is assumed to be complete. To address this issue, this paper focuses on extending a belief entropy to the Open World by considering the uncertain information represented as the FOD and the nonzero mass function of the empty set simultaneously. An extension to Deng’s entropy in the Open World Assumption (EDEOW) is proposed as a generalization of the Deng’s entropy and it can be degenerated to the Deng entropy in the closed World wherever necessary. In order to test the reasonability and effectiveness of the extended belief entropy, an EDEOW-based information fusion approach is proposed and applied to sensor data fusion under uncertainty circumstance. The experimental results verify the usefulness and applicability of the extended measure as well as the modified sensor data fusion method. In addition, a few Open issues still exist in the current work: the necessary properties for a belief entropy in the Open World Assumption, whether there exists a belief entropy that satisfies all the existed properties, and what is the most proper fusion frame for sensor data fusion under uncertainty.
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An Extension to Deng’s Entropy in the Open World Assumption with an Application in Sensor Data Fusion
2018Co-Authors: Yongchuan Tang, Deyun Zhou, Felix T. S. ChanAbstract:Quantification of uncertain degree in the Dempster-Shafer evidence theory (DST) framework with belief entropy is still an Open issue, even a blank field for the Open World Assumption. Currently, the existed uncertainty measures in the DST framework are limited to the closed World where the frame of discernment (FOD) is assumed to be complete. To address this issue, this paper focuses on extending a belief entropy to the Open World by considering the uncertain information represented as the FOD and the nonzero mass function of the empty set simultaneously. An extension to Deng’s entropy in the Open World Assumption (EDEOW) is proposed as a generalization of the Deng’s entropy and it can be degenerated to the Deng entropy in the closed World wherever necessary. In order to test the reasonability and effectiveness of the extended belief entropy, an EDEOW-based information fusion approach is proposed and applied to sensor data fusion under uncertainty circumstance. The experimental results verify the usefulness and applicability of the extended measure as well as the modified sensor data fusion method. In addition, a few Open issues still exist in the current work: the necessary properties for a belief entropy in the Open World Assumption, whether there exists a belief entropy that satisfies all the existed properties, and what is the most proper fusion frame for sensor data fusion under uncertainty
Martin Steffen - One of the best experts on this subject based on the ideXlab platform.
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incremental reasoning with lazy behavioral subtyping for multiple inheritance
2011Co-Authors: Johan Dovland, Einar Broch Johnsen, Olaf Owe, Martin SteffenAbstract:Object-orientation supports code reuse and incremental programming. Multiple inheritance increases the possibilities for code reuse, but complicates the binding of method calls and thereby program analysis. Behavioral subtyping allows program analysis under an Open World Assumption; i.e., under the Assumption that class hierarchies are extensible. However, method redefinition is severely restricted by behavioral subtyping, and multiple inheritance may lead to conflicting restrictions from independently designed superclasses. This paper presents a more liberal approach to incremental reasoning for multiple inheritance under an Open World Assumption. The approach, based on lazy behavioral subtyping, is well-suited for multiple inheritance, as it incrementally imposes context-dependent behavioral constraints on new subclasses. We first present the approach for a simple language and show how incremental reasoning can be combined with flexible code reuse. Then this language is extended with a hierarchy of interface types which is independent of the class hierarchy. In this setting, flexible code reuse can be combined with modular reasoning about external calls in the sense that each class is analyzed only once. We formalize the approach as a calculus and show soundness for both languages.
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incremental reasoning for multiple inheritance
2009Co-Authors: Johan Dovland, Einar Broch Johnsen, Olaf Owe, Martin SteffenAbstract:Object-orientation supports code reuse and incremental programming. Multiple inheritance increases the power of code reuse, but complicates the binding of method calls and thereby program analysis. Behavioral subtyping allows program analysis under an Open World Assumption ; i.e., under the Assumption that class hierarchies are extensible. However, method redefinition is severely restricted by behavioral subtyping, and multiple inheritance often leads to conflicting restrictions from independently designed superclasses. This paper presents an approach to incremental reasoning for multiple inheritance under an Open World Assumption. The approach, based on a notion of lazy behavioral subtyping , is less restrictive than behavioral subtyping and fits well with multiple inheritance, as it incrementally imposes context-dependent behavioral constraints on new subclasses. We formalize the approach as a calculus, for which we show soundness.
