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

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.

Ordered models for concept representation

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.

Automated Rule Base Completion as Bayesian Concept Induction

Considerable attention has recently been devoted to the problem of automatically extending knowledge bases by applying some form of inductive reasoning. While the vast majority of existing work is centred around so-called knowledge graphs, in this paper we consider a setting where the input consists of a set of (existential) rules. To this end, we exploit a vector space representation of the considered concepts, which is partly induced from the rule base itself and partly from a pre-trained word embedding. Inspired by recent approaches to concept induction, we then model rule templates in this vector space embedding using Gaussian distributions. Unlike many existing approaches, we learn rules by directly exploiting regularities in the given rule base, and do not require that a database with concept and relation instances is given. As a result, our method can be applied to a wide variety of ontologies. We present experimental results that demonstrate the effectiveness of our method.

Efficient Concept Induction for Description Logics

Concept Induction refers to the problem of creating complex Description Logic class descriptions (i.e., TBox axioms) from instance examples (i.e., ABox data). In this paper we look particularly at the case where both a set of positive and a set of negative instances are given, and complex class expressions are sought under which the positive but not the negative examples fall. Concept induction has found applications in ontology engineering, but existing algorithms have fundamental performance issues in some scenarios, mainly because a high number of invokations of an external Description Logic reasoner is usually required. In this paper we present a new algorithm for this problem which drastically reduces the number of reasoner invokations needed. While this comes at the expense of a more limited traversal of the search space, we show that our approach improves execution times by up to several orders of magnitude, while output correctness, measured in the amount of correct coverage of the input instances, remains reasonably high in many cases. Our approach thus should provide a strong alternative to existing systems, in particular in settings where other systems are prohibitively slow.

Concept Induction in Description Logics Using Information-Theoretic Heuristics

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

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.