scholarly journals Embedding Learning Through Multilingual Concept Induction

2018 ◽  
Author(s):  
Philipp Dufter ◽  
Mengjie Zhao ◽  
Martin Schmitt ◽  
Alexander Fraser ◽  
Hinrich Schütze
Keyword(s):  
Concept Induction

scholarly journals Sensitivity of the Nonsubsidized Consumption Promotion Mechanisms of New Energy Vehicles to Potential Consumers’ Purchase Intention

2021 ◽  
Vol 13 (8) ◽  
pp. 4293
Author(s):  
Yuqing Lin ◽  
Jingjing Wu ◽  
Yongqing Xiong
Keyword(s):  
Purchase Intention ◽  
Urban Traffic ◽  
Practical Significance ◽  
Local Conditions ◽  
Concept Induction ◽  
New Energy ◽  
Education Levels ◽  
The Right ◽  
Policy Incentives ◽  
New Energy Vehicles

With the background of China’s new energy vehicles (NEVs) subsidies declining, there is an important practical significance to effectively play the role of the nonsubsidized consumption promotion mechanisms. The nonsubsidized mechanisms for NEVs are classified into two types—concept induction and policy incentives, and differences in the sensitivity of the two types of mechanisms on potential consumer purchase intentions due to differences in urban traffic patterns and consumer education levels are analyzed. The results show that consumers in cities with medium to high traffic pressure are more sensitive to the right-of-way privileges component of the policy incentives, and consumers in cities with low traffic pressure are more sensitive to the charging guarantee component of the policy incentives. Consumers with medium to high education are more sensitive to the pro-environmental component in concept induction, and consumers with low education are more sensitive to the charging guarantee policy component of the policy incentives. Therefore, the implementation of the nonsubsidized mechanisms for NEVs in China should adopt differentiated strategies based on local conditions and vary with each individual.

Concept Induction in Description Logics Using Information-Theoretic Heuristics

2011 ◽  
Vol 7 (2) ◽  
pp. 23-44 ◽  
Author(s):  
Nicola Fanizzi
Keyword(s):  
Semantic Web ◽  
Experimental Evaluation ◽  
Learning Algorithm ◽  
Description Logics ◽  
Learning Problem ◽  
Information Theoretic ◽  
Concept Induction ◽  
Formal Ontologies ◽  
Refinement Operators ◽  
Theoretical Foundations

This paper presents an approach to ontology construction pursued through the induction of concept descriptions expressed in Description Logics. The author surveys the theoretical foundations of the standard representations for formal ontologies in the Semantic Web. After stating the learning problem in this peculiar context, a FOIL-like algorithm is presented that can be applied to learn DL concept descriptions. The algorithm performs a search through a space of candidate concept definitions by means of refinement operators. This process is guided by heuristics that are based on the available examples. The author discusses related theoretical aspects of learning with the inherent incompleteness underlying the semantics of this representation. The experimental evaluation of the system DL-Foil, which implements the learning algorithm, was carried out in two series of sessions on real ontologies from standard repositories for different domains expressed in diverse description logics.

Concept Induction in Description Logics Using Information-Theoretic Heuristics

Semantic Web ◽  
2013 ◽  
pp. 97-118
Author(s):  
Nicola Fanizzi
Keyword(s):  
Semantic Web ◽  
Experimental Evaluation ◽  
Learning Algorithm ◽  
Description Logics ◽  
Learning Problem ◽  
Information Theoretic ◽  
Concept Induction ◽  
Formal Ontologies ◽  
Refinement Operators ◽  
Theoretical Foundations

This paper presents an approach to ontology construction pursued through the induction of concept descriptions expressed in Description Logics. The author surveys the theoretical foundations of the standard representations for formal ontologies in the Semantic Web. After stating the learning problem in this peculiar context, a FOIL-like algorithm is presented that can be applied to learn DL concept descriptions. The algorithm performs a search through a space of candidate concept definitions by means of refinement operators. This process is guided by heuristics that are based on the available examples. The author discusses related theoretical aspects of learning with the inherent incompleteness underlying the semantics of this representation. The experimental evaluation of the system DL-Foil, which implements the learning algorithm, was carried out in two series of sessions on real ontologies from standard repositories for different domains expressed in diverse description logics.

scholarly journals Ordered models for concept representation

2020 ◽  
Vol 30 (6) ◽  
pp. 1143-1181
Author(s):  
Michael Freund
Keyword(s):  
Cognitive Psychology ◽  
Partial Order ◽  
Interesting Consequence ◽  
Concept Representation ◽  
Order Relations ◽  
Concept Induction ◽  
Rigorous Definition ◽  
Definition Of ◽  
Concept Theory

Abstract Basic notions linked with concept theory can be accounted for by partial order relations. These orders translate the fact that, for an agent, an object may be seen as a better or a more typical exemplar of a concept than anyother. They adequately model notions linked with categorial membership, typicality and resemblance, without any of the drawbacks that are classically encountered in conjunction theory. An interesting consequence of such a concept representation is the possibility of using the tools of non-monotonic logic to address some well-known problems of cognitive psychology. Thus, conceptual entailment and concept induction can be reexamined in the framework of preferential inference relations. This leads to a rigorous definition of the basic notions used in the study of category-based induction.

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