The Empirical Evaluation of Semantic Alignment Quality Metrics for Vehicle Domain Component Frameworks Interface Ontologies

Semantic alignment of application software components’ ontologies represents a great interest in vehicle application domains that manipulate heterogeneous overlapping knowledge application frameworks. In the past few years, with the growth in the novel vehicle service requirements such as autonomous driving, V2X (Vehicle-to-Vehicle communication) and many others, automotive application software component models are becoming increasingly collaborative with other qualified cross-enterprise industrial partners to accomplish these complex service requirements. The most daunting impediment to this cross-enterprise collaboration is semantic interoperability. For efficient services collaboration through cross-enterprise semantic interoperability between the vehicle application frameworks’ software components, aligning the interface ontologies of these components by identifying the depth of semantic alignment relationships between the concepts of the interface ontologies is the major focus of this paper. In contrast to several existing ontology structural metrics, this work defines, evaluates and validates ontology metrics to measure the depth of semantic alignment between the vehicle domain software component frameworks’ interface ontological models. To emphasize the substantial role of semantic alignment of software component frameworks’ interface ontologies in semantic interoperability, a typical vehicle domain case study involving vehicle applications is considered for demonstration.

Single-component frameworks for heterogeneous catalytic hydrolysis of organophosphorous compounds in pure water

Amine linkers incorporated in Zr-MOFs aid in organophosphorous chemical warfare agent hydrolysis under solely aqueous conditions

Generating Knowledge-Based System Generators

This paper investigates software engineering techniques for designing and reengineering knowledge-based system generators, focusing on inference engines and domain specific languages. Indeed, software development of knowledge-based systems is a difficult task. We choose a software engineering approach to favor code reuse, evolution, and maintenance. We propose a software platform named Lama to design the different elements necessary to produce a knowledge-based system. This platform offers software toolkits (mainly component frameworks) to build interfaces, inference engines, and expert languages. We have used the platform to build several KBS generators for various tasks (planning, classification, model calibration) in different domains. The approach appears well fitted to knowledge-based system generators; it allows developers a significant gain in time, as well as it improves software readability and safeness.

Generating Knowledge-Based System Generators

This article investigates software engineering techniques for designing and reengineering knowledge-based system generators, focusing on inference engines and domain specific languages. Indeed, software development of knowledge-based systems is a difficult task. We choose a software engineering approach to favor code reuse, evolution, and maintenance. We propose a software platform named Lama to design the different elements necessary to produce a knowledge-based system. This platform offers software toolkits (mainly component frameworks) to build interfaces, inference engines, and expert languages. We have used the platform to build several KBS generators for various tasks (planning, classification, model calibration) in different domains. The approach appears well fitted to knowledge-based system generators; it allows developers a significant gain in time, as well as it improves software readability and safeness.