A Semantic Representation for Process-Oriented Knowledge Management Based on Functionblock Domain Models Supporting Distributed and Collaborative Production Planning

2015 ◽  
Author(s):  
Benjamin Gernhardt ◽  
Franz Miltner ◽  
Tobias Vogel ◽  
Holger Brocks ◽  
Matthias Hemmje ◽  
...  
Keyword(s):  
Semantic Web ◽  
Knowledge Representation ◽  
Production Planning ◽  
Semantic Knowledge ◽  
Semantic Integration ◽  
Product Knowledge ◽  
Simulation And Optimization ◽  
Knowledge Based ◽  
Process Oriented ◽  
Machine Readable

Semantic knowledge representation, management, sharing, access, and re-use approaches can support collaborative production planning in a flexible and efficient as well as an effective way. Therefore, semantic-technology based representations of Collaborative Production Process Planning (CAPP) knowledge integrated into a machine readable process formalization is a key enabling factor for sharing such knowledge in cloud-based semantic-enabled knowledge repositories supporting CAPP scenarios as required in the CAPP4SMES project [1]. Beyond that, Small and Medium Enterprises (SMEs) as represented in CAPP4SMES request for a standardized CAPP-oriented product-knowledge- and production-feature representation that can be achieved by applying function-block based knowledge representation models. Semantic Web- and at the same time Cloud-based technologies, tool suites and application solutions which are based on process-oriented semantic knowledge representation methodologies such as Process-oriented Knowledge-based Innovation Management (German: Wissens-basiertes Prozesess-orientiertes Innovationsmanagement, WPIM) [2] can satisfy these needs, supporting the semantic integration, management, access and re-use in a machine readable and integrated representation of distributed CAPP knowledge that is shared within a cloud-based centralized semantic-enabled knowledge repository. Furthermore semantic knowledge representation and querying add value to knowledge-based and computer-aided re-use of such knowledge within CAPP activities and, finally, pave the way towards further automating planning, simulation and optimization support in a semantic web for CAPP.

Implementation of a Knowledge-Based Production Planning Including a Direct Manipulative Process Editor and a Mediator Architecture

2017 ◽  
Author(s):  
Benjamin Gernhardt ◽  
Tobias Vogel ◽  
Lihui Wang ◽  
Matthias Hemmje
Keyword(s):  
Knowledge Representation ◽  
Change Management ◽  
Production Planning ◽  
Planning Process ◽  
Semantic Knowledge ◽  
Semantic Integration ◽  
Global Market ◽  
Knowledge Based ◽  
Process Oriented ◽  
Machine Readable

Today, in the era of modern Intelligent Production Environments (IPE) and Industry 4.0, the manufacturing of a product takes place in various partial steps and these mostly in different locations, potentially distributed all over the world. The producing companies must assert in the global market and always find new ways to cut costs by saving tax, changing to the best providers, and by using the most efficient and fastest production processes. Furthermore, they must be inevitably based on a cloud-based repository and distributed architectures to make data and information accessible everywhere as well as development processes and knowledge available for a worldwide cooperation. A so called Collaborative Adaptive (Production) Process Planning (CAPP) can be supported by semantic approaches for knowledge representation and management as well as knowledge sharing, access, and re-use in a flexible and efficient way. In this way, to support CAPP scenarios, semantic representations of such knowledge integrated into a machine-readable process formalization is a key enabling factor for sharing in cloud-based knowledge repositories. This is especially required for, e.g., Small and Medium Enterprises (SMEs). When SMEs work together on a production planning for a joint product, they exchange component production and manufacturing change information between different planning subsystems. These exchanges are mostly based on the already well-established Standard for the Exchange of Product model data (STEP), not least to obtain a computer-interpretable representation. Moreover, so-called Function Block (FB) Domain Models could support these planning process. FBs serve as a high-level planning-process knowledge-resource template and to the representation of knowledge. Furthermore, methodologies are required, which based on process-oriented semantic knowledge-representation, such as Process-oriented Knowledge-based Innovation Management (German: Wissens-basiertes Prozess-orientiertes Innovations Management, WPIM). WPIM is already a web- and cloud-based tool suites and can represent such planning processes and their knowledge resources and can therefore be used to support the integration and the management of distributed CAPP knowledge in Manufacturing Change Management (MCM), as well as its access and re-use. That is also valid for Assembly-, Logistics- and Layout Planning (ALLP). On the one hand, a collaborative planning in a machine-readable and integrated representation will be possible as well as an optimization for mass production. On the other hand, within a cloud-based semantic knowledge repository, that knowledge can be shared with all partners and contributors. To combine all these functionalities, in 2016 we have already introduced a method, called Knowledge-based Production Planning (KPP). We outlined the theoretical advantages of integrating CAPP with Collaborative Manufacturing Change Management (CMCM) in the last year at MSEC16. In this Paper, we will demonstrate our first implementations of the KPP application with an integrated visual direct manipulative process editor as well as a first prototype of our mediator architecture with a semantic integration including a query library based on the KPP ontology.

Knowledge-Based Production Planning Within the Reference Planning Process Supporting Manufacturing Change Management

2016 ◽  
Author(s):  
Benjamin Gernhardt ◽  
Tobias Vogel ◽  
Mohammad Givehchi ◽  
Lihui Wang ◽  
Matthias Hemmje
Keyword(s):  
Knowledge Representation ◽  
Change Management ◽  
Production Planning ◽  
Planning Process ◽  
Semantic Knowledge ◽  
Collaborative Manufacturing ◽  
Knowledge Based ◽  
Process Oriented ◽  
Machine Readable ◽  
Component Production

The manufacturing of a product takes place in several partial steps and these mostly in different locations to save tax or to use the best providers. Therefore, in the era of Internet of Things (IoT) and modern Intelligent Production Environments (IPE) are going to be inevitably based on a cloud-based repository and distributed architecture to make data and information accessible everywhere as well as development processes and knowledge available for worldwide cooperation. Semantic approaches for knowledge representation and management as well as knowledge sharing, access, and re-use can support Collaborative Adaptive Production Process Planning (CAPP) in a flexible, efficient, and effective way. Thus, semantic representations of such CAPP knowledge integrated into a machine readable process formalization is a key enabling factor for sharing such knowledge in cloud-based knowledge repositories supporting CAPP scenarios as required for e.g., Small and Medium Enterprises (SMEs). When such contributors work together on a product component production planning, they exchange component production and manufacturing change information between different planning subsystems which require, e.g., a standardized product-feature- and production-machine feature representation. These data exchanges are mostly based on applying the already established Standard for the Exchange of Product model data (STEP) for the computer-interpretable representation and exchange of product manufacturing information. Furthermore, the planning process can be supported by so-called Function Block (FB) based knowledge representation models, serving as a high-level planning-process knowledge-resource template. Web-based and at the same time Cloud-based tool suites, which are based on process-oriented semantic knowledge-representation methodologies, such as Process-oriented Knowledge-based Innovation Management (German: Wissens-basiertes Prozess-orientiertes Innovations Management, WPIM) can satisfy the needs of representing such planning processes and their knowledge resources. In this way, WPIM can be used to support the integration and management of distributed CAPP knowledge, as well as its access and re-use in Manufacturing Change Management (MCM) including Assembly-, Logistics and Layout Planning (ALLP). Therefore, also a collaborative planning and optimization for mass production in a machine readable and integrated representation is possible. On the other hand, that knowledge can be shared within a cloud-based semantic knowledge repository. To integrate all these functionalities, this paper introduces a new method, called Knowledge-based Production Planning (KPP) and outlines the advantages of integrating CAPP with Collaborative Manufacturing Change Management (CMCM). In this way, an enabling basis for achieving ALLP interoperability in Distributed Collaborative Manufacturing and Logistics will be demonstrated.

Towards Knowledge Based Process Planning Support for CAPP-4-SMEs: Problem Description, Relevant State of the Art and Proposed Approach

2014 ◽  
Author(s):  
Franz Miltner ◽  
Tobias Vogel ◽  
Matthias Hemmje
Keyword(s):  
Knowledge Representation ◽  
Process Planning ◽  
Semantic Knowledge ◽  
Feature Representation ◽  
Enabling Factors ◽  
Computer Aided Process Planning ◽  
Simulation And Optimization ◽  
Knowledge Based ◽  
Production Environments ◽  
Problem Description

Knowledge sharing approaches enable collaborative process-oriented planning in adaptive and highly dynamic, i.e., innovation and therefore change-driven, cross-organizational production environments. Externalization of knowledge as well as knowledge and process formalization is the key enabling factors for supporting collaborative production process planning. Beyond that SMEs request for a standardized production-planning oriented knowledge and feature representation. Semantic Web- and at the same time Cloud-based application s solutions which are based on process-driven knowledge representation methodologies such as WPIM can satisfy these needs, support intelligent collaboration, being able to cope with high change dynamics and therefore support boosting overall adaptiveness of production processes. Furthermore semantic knowledge representation and semantic querying add value to knowledge-based Computer-Aided Process Planning and, finally, pave the way towards automating planning, simulation and optimization.

Knowledge Representation Method of Product Principle Solution Based on Semantic Network Model

2010 ◽  
Vol 34-35 ◽  
pp. 1865-1869
Author(s):  
Xiao Ying Chen ◽  
Bin He
Keyword(s):  
Knowledge Representation ◽  
Network Model ◽  
Semantic Network ◽  
Design System ◽  
Product Knowledge ◽  
Knowledge Based ◽  
Principle Solution ◽  
Representation Method ◽  
Semantic Object ◽  
Knowledge Based Design

Product design is a problem-solving activity based on knowledge. This paper is devoted to presenting a systematic knowledge representation method of principle solution based on semantic network model. For the expression of product knowledge, the semantic object, constraints and their relationships among the expression of the semantic object network model are proposed step by step. Then the principle solution representation model based on semantic network model is put forwards. The knowledge representation of a car is given as an example, which demonstrates that this method is obviously helpful for knowledge-based design system and product innovation.

scholarly journals Ontology-Based Knowledge Representation in Robotic Systems: A Survey Oriented toward Applications

2021 ◽  
Vol 11 (10) ◽  
pp. 4324
Author(s):  
Sumaira Manzoor ◽  
Yuri Goncalves Rocha ◽  
Sung-Hyeon Joo ◽  
Sang-Hyeon Bae ◽  
Eun-Jin Kim ◽  
...  
Keyword(s):  
Knowledge Representation ◽  
Real World ◽  
Autonomous Robots ◽  
Semantic Knowledge ◽  
Lessons Learned ◽  
Knowledge Representation And Reasoning ◽  
Knowledge Based Systems ◽  
Knowledge Based ◽  
Representation Systems ◽  
Knowledge Representation Systems

Knowledge representation in autonomous robots with social roles has steadily gained importance through their supportive task assistance in domestic, hospital, and industrial activities. For active assistance, these robots must process semantic knowledge to perform the task more efficiently. In this context, ontology-based knowledge representation and reasoning (KR & R) techniques appear as a powerful tool and provide sophisticated domain knowledge for processing complex robotic tasks in a real-world environment. In this article, we surveyed ontology-based semantic representation unified into the current state of robotic knowledge base systems, with our aim being three-fold: (i) to present the recent developments in ontology-based knowledge representation systems that have led to the effective solutions of real-world robotic applications; (ii) to review the selected knowledge-based systems in seven dimensions: application, idea, development tools, architecture, ontology scope, reasoning scope, and limitations; (iii) to pin-down lessons learned from the review of existing knowledge-based systems for designing better solutions and delineating research limitations that might be addressed in future studies. This survey article concludes with a discussion of future research challenges that can serve as a guide to those who are interested in working on the ontology-based semantic knowledge representation systems for autonomous robots.

Interval Algebra and Region Connection Calculus for Ontological Spatiotemporal Assembly Product Motion Knowledge Representation

2021 ◽  
pp. 1-18
Author(s):  
Md Tarique Hasan Khan ◽  
Frédéric Demoly ◽  
Kyoung Yun Kim
Keyword(s):  
Knowledge Representation ◽  
Semantic Knowledge ◽  
Added Value ◽  
Topological Spaces ◽  
Product Knowledge ◽  
Interval Algebra ◽  
Assembly Design ◽  
Knowledge Models ◽  
Region Connection Calculus

Over the last decades, noticeable efforts have been made to construct design knowledge during the detailed geometric definition phase systematically. However, physical products exhibit functional behaviors, which explain that they evolve over space and time. Hence, there is a need to extend assembly product knowledge towards the spatiotemporal dimension to provide more realistic knowledge models in assembly design. Systematic semantic knowledge representation via ontology enables designers to understand the anticipated product’s behavior in advance. In this article, Interval Algebra (IA) and Region Connection Calculus (RCC) are investigated to formalize and construct ontological spatiotemporal assembly product motion knowledge. IA is commonly used to represent the temporality between two entities, while RCC is more appropriate to represent the ‘part-to-part’ relationships of two topological spaces. This paper discusses the roles of IA and RCC and presents a case study of a nutcracker assembly model’s behavior. The assembly product motion ontology with the aid of IA and RCC is evaluated using a task-based approach. The evaluation shows the added value of the developed ontology compared to others published in the literature.

scholarly journals Semantic knowledge based reasoning framework for human robot collaboration

Procedia CIRP ◽  
2021 ◽  
Vol 97 ◽  
pp. 373-378
Author(s):  
Sharath Chandra Akkaladevi ◽  
Matthias Plasch ◽  
Michael Hofmann ◽  
Andreas Pichler
Keyword(s):  
Semantic Knowledge ◽  
Knowledge Based ◽  
Human Robot Collaboration

scholarly journals MMoOn Core – the Multilingual Morpheme Ontology

Semantic Web ◽  
2020 ◽  
pp. 1-29
Author(s):  
Bettina Klimek ◽  
Markus Ackermann ◽  
Martin Brümmer ◽  
Sebastian Hellmann
Keyword(s):  
Semantic Web ◽  
Linked Data ◽  
Semantic Model ◽  
Lexical Resources ◽  
Domain Specific ◽  
Word Forms ◽  
Language Data ◽  
Core Ontology ◽  
Machine Readable ◽  
Rapid Emergence

In the last years a rapid emergence of lexical resources has evolved in the Semantic Web. Whereas most of the linguistic information is already machine-readable, we found that morphological information is mostly absent or only contained in semi-structured strings. An integration of morphemic data has not yet been undertaken due to the lack of existing domain-specific ontologies and explicit morphemic data. In this paper, we present the Multilingual Morpheme Ontology called MMoOn Core which can be regarded as the first comprehensive ontology for the linguistic domain of morphological language data. It will be described how crucial concepts like morphs, morphemes, word forms and meanings are represented and interrelated and how language-specific morpheme inventories can be created as a new possibility of morphological datasets. The aim of the MMoOn Core ontology is to serve as a shared semantic model for linguists and NLP researchers alike to enable the creation, conversion, exchange, reuse and enrichment of morphological language data across different data-dependent language sciences. Therefore, various use cases are illustrated to draw attention to the cross-disciplinary potential which can be realized with the MMoOn Core ontology in the context of the existing Linguistic Linked Data research landscape.

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