Research on Key Issues of Functioning Domain Knowledge Representation Based on ICF

2015 ◽  
Vol 25 (4) ◽  
pp. 43
Author(s):  
Xiaohua LIN ◽  
Danhong LIN
Keyword(s):  
Knowledge Representation ◽  
Domain Knowledge ◽  
Key Issues

scholarly journals Semantic Representation of Domain Knowledge for Professional VR Training

2021 ◽  
pp. 139-150
Author(s):  
Jakub Flotyński ◽  
Paweł Sobociński ◽  
Sergiusz Strykowski ◽  
Dominik Strugała ◽  
Paweł Buń ◽  
...  
Keyword(s):  
Knowledge Representation ◽  
Domain Knowledge ◽  
Professional Training ◽  
Semantic Representation ◽  
Essential Element ◽  
Domain Experts ◽  
Training Systems ◽  
Web Standards ◽  
Domain Specific Knowledge ◽  
Vr Training

Domain-specific knowledge representation is an essential element of efficient management of professional training. Formal and powerful knowledge representation for training systems can be built upon the semantic web standards, which enable reasoning and complex queries against the content. Virtual reality training is currently used in multiple domains, in particular, if the activities are potentially dangerous for the trainees or require advanced skills or expensive equipment. However, the available methods and tools for creating VR training systems do not use knowledge representation. Therefore, creation, modification and management of training scenarios is problematic for domain experts without expertise in programming and computer graphics. In this paper, we propose an approach to creating semantic virtual training scenarios, in which users’ activities, mistakes as well as equipment and its possible errors are represented using domain knowledge understandable to domain experts. We have verified the approach by developing a user-friendly editor of VR training scenarios for electrical operators of high-voltage installations.

An eco asset ontology towards effective eco asset management

2017 ◽  
Vol 7 (4) ◽  
pp. 388-399 ◽  
Author(s):  
Jehan Zeb
Keyword(s):  
Knowledge Representation ◽  
Asset Management ◽  
Domain Knowledge ◽  
Ad Hoc ◽  
The Body ◽  
Content Type ◽  
Body Of Knowledge ◽  
Management Functions ◽  
Effective Integration ◽  
Common Understanding

Purpose The purpose of this paper is to develop an ontology of eco or natural assets to represent eco asset knowledge at two levels: eco asset metal model and eco asset ontology (EA_Onto). The three objectives of this paper are to: define eco assets explicitly to reach a common understanding of the terms; evaluate the ontology; and discuss a potential area of application. Design/methodology/approach A seven-step methodology was used to develop the proposed ontology: define the scope; develop the eco asset meta model (EA_MM), define taxonomy, code ontology, capture ontology, evaluate ontology and document ontology. Findings The EA_MM was developed to represent eco asset domain knowledge, which was further extended to develop the EA_Onto, explicitly defining the eco asset knowledge in asset management. As a part of evaluation, it was found that the knowledge representation is consistent, concise, clear, complete and correct. Practical implications Theoretically, the proposed ontology is a significant contribution to the body of knowledge in asset management. Practically, the knowledge representation provides a common understanding of eco assets for asset management experts. In addition, it will be used in applications for effective eco asset management. Originality/value The current literature lacks explicit declaration of eco assets, how they are related to built environment for effective integration and how asset management functions are to be applied to accomplish effective eco asset management. Presently, eco assets are managed on an ad hoc basis, which need to be explicitly defined through developing an EA_Onto for implementation in applications for effective eco asset management.

A Hypergraph-Based Knowledge Representation Model for Smart Product-Service System Development

2021 ◽  
Author(s):  
Wang Zuoxu ◽  
Li Xinyu ◽  
Chen Chun-Hsien ◽  
Zheng Pai
Keyword(s):  
Knowledge Representation ◽  
Domain Knowledge ◽  
Manufacturing Companies ◽  
Binary Relations ◽  
Fundamental Study ◽  
Essential Information ◽  
Knowledge Reasoning ◽  
Representation Model ◽  
Product Service ◽  
Smart Product

Abstract In the trend of digital servitization, manufacturing companies have been transforming their business paradigms to Smart product-service systems (Smart PSS) by integrating products and associated services as bundles. To support the knowledge-intensive process of Smart PSS development, massive domain knowledge should be well-organized and reused. However, due to the existence of non-binary relations caused by product-service bundles (PSB) and context-awareness concerns in the Smart PSS development activities, conventional graph-based approaches for knowledge representation may lose essential information in transforming non-binary relations into binary ones, and hence cause incorrect results in the subsequent knowledge queries. To mitigate this problem, a hypergraph-based knowledge representation model for Smart PSS was proposed, which represents the non-binary relations among multiple entities with hyperedges. Technically, the knowledge source and the typical hyperedge schema in Smart PSS development are identified in this paper. A detailed case study in the scenarios of 3D printing troubleshooting and PSB recommendation was conducted to showcase the proposed hypergraph-based knowledge representation model and demonstrate its validity. The results show that the hypergraph-based knowledge model significantly relieves the sparsity in the ordinary KG by adding multiple hyperedges. It is anticipated that the proposed hypergraph knowledge representation model can serve as a fundamental study for further knowledge reasoning activities.

The Next Generation of Personalization Techniques

2011 ◽  
pp. 72-92
Author(s):  
Gulden Uchyigit
Keyword(s):  
Knowledge Representation ◽  
Traditional Method ◽  
Domain Knowledge ◽  
Information Overload ◽  
State Of The Art ◽  
User Preferences ◽  
Future Trends ◽  
User Profiles ◽  
User Interests ◽  
Key Concepts

Coping with today’s unprecedented information overload problem necessitates the deployment of personalization services. Typical personalization approaches model user preferences and store them in user profiles, used to deliver personalized content. A traditional method for profile representation is the so called keyword-based representation, where the user interests are modelled using keywords which are selected from the contents of the items which the user has rated. Although, keyword based approaches are simple and are extensively used for profile representation they fail to represent semantic-based information, this information is lost during the pre-processing phase. Future trends in personalization systems necessitate more innovative personalization techniques that are able to capture rich semanticbased information during the representation, modelling and learning phases. In recent years ontologies (key concepts and along with their interrelationships) to express semantic-based information have been very popular in domain knowledge representation. The primary goal of this chapter is to present an overview of the state-of-the art techniques and methodologies which aim to integrate personalization technologies with semantic-based information.

Research of Maize Field Knowledge Representation Building Based on Ontology

2012 ◽  
Vol 546-547 ◽  
pp. 441-445
Author(s):  
Ying Zhang ◽  
Gui Fen Chen
Keyword(s):  
Artificial Intelligence ◽  
Knowledge Representation ◽  
Intelligent Systems ◽  
Domain Knowledge ◽  
Semantic Level ◽  
Maize Field ◽  
Complex Knowledge ◽  
The Relationship ◽  
Knowledge Ontology ◽  
Knowledge Hierarchy

The knowledge representation of the traditional artificial intelligence used different modeling methods and the different development tools, it led to the lack of interoperability between all kinds of knowledge, ontology solved the problem. Ontology, which is a model in semantic and knowledge hierarchy describing the concept and the relationship between the concepts, has been the focus of the field of artificial intelligence since it was proposed. This paper explored the knowledge representation based on ontology in the field of artificial intelligence, built the maize domain knowledge ontology, the result shows: ontology can effectively solve the heterogeneous problem of expression of complex knowledge, makes the computer to understand information for the semantic level, and benefit to develop the intelligent systems of maize.

Study on Formalizable Aircraft Assembly Process Planning Knowledge

2006 ◽  
Vol 532-533 ◽  
pp. 640-643 ◽  
Author(s):  
Hong Jun Qiu ◽  
Hua Tao ◽  
Bin Tang Yang ◽  
Xiao Bin Gao
Keyword(s):  
Knowledge Representation ◽  
Process Planning ◽  
Domain Knowledge ◽  
Formal Description ◽  
Assembly Process ◽  
Assembly Process Planning ◽  
Aircraft Assembly ◽  
Special Domain ◽  
Planning Knowledge

Domain knowledge representation is various and domain-concerned. The aircraft assembly process planning (A2P2) is a special domain, a lot of things should be taken into account, and the knowledge representation of A2P2 is complicated. It is focused on the knowledge representation of A2P2 in this paper. Based-on case, the framework of A2P2 knowledge is presented. The main considerations of A2P2 are analyzed, the transformation and reassembly of native A2P2 knowledge is studied, the features of A2P2 is acquired, and the formalizable framework of A2P2 is proposed. With BNF, a formal description of A2P2 knowledge is given.

An agent-based model for domain knowledge representation

1999 ◽  
Vol 29 (2) ◽  
pp. 147-161 ◽  
Author(s):  
Florence Le Ber ◽  
Marie-Pierre Chouvet
Keyword(s):  
Knowledge Representation ◽  
Domain Knowledge ◽  
Agent Based Model ◽  
Agent Based

scholarly journals Heterogeneous Data Clustering Considering Multiple User-provided Constraints

2019 ◽  
Vol 14 (2) ◽  
pp. 170-182 ◽  
Author(s):  
Yue Huang
Keyword(s):  
Heterogeneous Networks ◽  
Data Clustering ◽  
Domain Knowledge ◽  
General Framework ◽  
Heterogeneous Data ◽  
Similarity Measurement ◽  
Clustering Methods ◽  
Multiple User ◽  
Key Issues ◽  
Internal Information

Clustering on heterogeneous networks which consist of multi-typed objects and links has proved to be a useful technique in many scenarios. Although numerous clustering methods have achieved remarkable success, current clustering methods for heterogeneous networks tend to consider only internal information of the dataset. In order to utilize background domain knowledge, we propose a general framework for clustering heterogeneous data considering multiple user-provided constrains. Specifically, we summarize that three types of manual constraints on the object can be used to guide the clustering process. Then we propose the User- HeteClus algorithm to solve the key issues in the case of star-structure heterogeneous data, which incorporating the user constraint into similarity measurement between central objects. Experiments on a real-world dataset show the effectiveness of the proposed algorithm.

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