Ontology-based Information Model Research Articles

Paving the way for automated factory planning – applying rule-based expert systems to capacity planning

Today, numerous different software systems aid factory planners in their tasks. Nevertheless, due to their lacking interoperability and project-specific approaches, generic support for automated decision-making is still missing. Investigating the state of the art, we conclude that knowledge-based information modeling is needed for decision-making support. However, as the identified approaches of previous works propose no general automation concepts. Therefore, we define the guiding research question as how to model processual domain knowledge for automating factory planning processes. In this paper, we propose a planning assistance on rule-based expert systems. The planning assistance is composed of an ontology-based information model, a planning model consisting of individual planning functions, and a domain-specific inference engine. We implement the planning assistance with Semantic Web technologies and validate the solution using an application example from capacity planning. Thereby, we demonstrate the applicability of rule-based expert systems for automated factory planning. Finally, implications for future research are drawn for exploring further application areas and developing anticipated hybrid solution concepts.

A semantic structuring of educational research using ontologies

This article is devoted to the presentation of the semantic interoperability of research and scientific results through an ontological taxonomy. To achieve this, the principles of systematization and structuration of the scientific/research results in scientometrics databases have been analysed. We use the existing cognitive IT platform Polyhedron and extend it with an ontology-based information model as main contribution. As a proof-of-concept we have modelled two ontological graphs, “Development of a rational way for utilization of methane tank waste at LLC Vasylkivska poultry farm” and “Development a method for utilization of methane tank effluent”. Also, for a demonstration of the perspective of ontological systems for a systematization of research and scientific results, the “Hypothesis test system” ontological graph has created.

A semantic-based discovery service for the Internet of Things

With the Internet of Things (IoT), applications should interact with a huge number of devices and retrieve context data produced by those objects, which have to be discovered and selected a priori. Due to the number, heterogeneity, and dynamicity of resources, discovery services are required to consider many selection criteria, e.g., device capabilities, location, context data type, contextual situations, and quality. In this paper, we describe QoDisco, a semantic-based discovery service that addresses this requirement in IoT. QoDisco is composed of a set of repositories storing resource descriptions according to an ontology-based information model and it provides multi-attribute and range querying capabilities. We have evaluated different approaches to reduce the inherent cost of semantic search, namely parallel interactions with multiple repositories and publish-subscribe interactions. This paper also reports the results of some performance experiments on QoDisco with respect to these approaches to handle resource discovery requests in IoT.

Ontology-based disassembly information system for enhancing disassembly planning and design

Disassembly, as one of the core steps in the end of life (EOL) activities, has been a popular topic both in industrial and academic areas. Unlike assembly, any practical disassembly process planning, which is very important for material recycling or component reuse, is still a topic of active research. One major obstacle in developing an optimal disassembly process planning system is the uncertain characteristics of the EOL products (uncertain information), such as the quality and quantity of the components and parts, which normally make a deterministic disassembly process planning impossible. How to represent and further use such uncertain information in reasoning about a realistic optimal disassembly process sequence are the most important research issues. This paper addresses these issues by developing a disassembly information framework which consists of both an ontology-based information model (domain semantics and rules) and a computational model (process planning generation methodology). The explicit domain concepts, relationships, or rules developed in the ontology-based information enhance the process planning generation methodology (computational model) and thus make the planning process efficient and more realistic. Examples are used in the end to illustrate the use of the proposed framework and methodology.

Multi-level Self-organization in Cyber-Physical-Social Systems: Smart Home Cleaning Scenario

Cyber-Physical-Social Systems integrate various resources from physical network, cyberspace, and social world.These systems rely on communication, computation and control infrastructures commonly consisting of several levels for the three worlds with various resources as sensors, actuators, computational resources, services, humans, etc. Efficient interaction of these resources is essential for cyber-physical-social systems operation. The paper presents multi-level self-organization approach for cyber-physical-social systems that distinguishes three main level for the system resource interaction: physical level, planning level, and strategic level. Smart home cleaning scenariois proposedas an example of multi-level self-organization approach. Robot vacuum cleaner creates room map using the light-sensitive sensors and clean room using the map. For the efficient cleaning, it requires the lights switch on in the cleaning room. For this purposes the vacuum cleaner has to interact with the adaptive illumination control system.Manipulating robot move light furniture (e.g. chairs, or coffee table) or decorative elements (pots with flowers, vases, etc.). It interacts with the vacuum cleaner for synchronization of moving light furniture.For the strategic level, a special strategic planning service has been identified that determines general strategy of resources behavior in the scenario based on interaction with personal mobile device and current situation in smart home. Example of general strategies can be for example reduction in energy consumption, excellent cleaning. For the planning level, a special room management services have been proposed that provide general behavior the cyber-physical-social systems resources in physical level. Example of these strategies can be clean room 1 completely, after that clean room 2 or vice versa. For the physical level a vacuum cleaner, manipulating, and light control services have been proposed. The paper present ontology-based information model for smart home devices interaction. It allows the services of physical level linked to the appropriate smart room devices interact in cyber space while physical devices interact in physical space. Every resource in physical level of cyber-physical-social system is described by the ontology. Appropriate ontologies of vacuumcleanerrobot, adaptive illumination control system, and manipulating robothave been proposed in the paper.

Ontology-Based Information Model for the Exchange of Uncertainty in Load Carrying Structures

Knowledge about future process properties is crucial for the development of safe and economic products with load carrying structures. Real processes are influenced by uncertainty what causes scattering and deviation from assumed values. As a consequence, products are often oversized or even product failures can occur. To control uncertainty, extensive knowledge about future processes is necessary in the development process. This paper shows an approach for the representation of uncertainty in production-and usage-processes, according to scattering properties and their cause and effect relations. This approach is used as a common platform for storing, locating, comparing and reuse of knowledge about uncertain properties and their relations. The core of the proposed approach is an ontology-based information model with the ability to represent different levels of trusted information in relation to process parameters and cause and effect relations.