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.

scholarly journals Visual aspect-oriented modeling of explorable extended reality environments

2021 ◽  
Author(s):  
Jakub Flotyński
Keyword(s):  
3D Modeling ◽  
Domain Knowledge ◽  
Semantic Representation ◽  
Training Design ◽  
Domain Experts ◽  
Visual Approach ◽  
Technical Details ◽  
High Level ◽  
Aspect Oriented Modeling ◽  
Level Analysis

AbstractThe availability of various extended reality (XR) systems for tracking users’ and objects’ behavior opens new opportunities for analyzing users’ and objects’ interactions and autonomous actions. Such analysis can be especially useful and attainable to domain experts when it is based on domain knowledge related to a particular application, liberating the analysts from going into technical details of 3D content. Analysis of XR users’ and objects’ behavior can provide knowledge about the users’ experience, interests and preferences, as well as objects’ features, which may be valuable in various domains, e.g., training, design and marketing. However, the available methods and tools for building XR focus on 3D modeling and programming rather than knowledge representation, making them unsuitable for domain-oriented analysis. In this paper, a new visual approach to modeling explorable XR environments is proposed. It is based on a semantic representation of aspects, which extend the primary code of XR environments to register their behavior in a form explorable with reasoning and queries, appropriate for high-level analysis in arbitrary domains. It permits domain experts to comprehend and analyze what happened in an XR environment regarding users’ and objects’ actions and interactions. The approach has been implemented as an extension to MS Visual Studio and demonstrated in an explorable immersive service guide for household appliances. The evaluation results show that the approach enables efficient development of explorable XR and may be useful for people with limited technical skills.

QuenchML: A semantics-preserving markup language for knowledge representation in quenching

2013 ◽  
Vol 27 (1) ◽  
pp. 65-82
Author(s):  
Aparna S. Varde ◽  
Mohammed Maniruzzaman ◽  
Richard D. Sisson
Keyword(s):  
Knowledge Representation ◽  
Domain Knowledge ◽  
Materials Science ◽  
Heat Treating ◽  
Markup Language ◽  
Domain Experts ◽  
Domain Specific ◽  
Extensible Markup ◽  
The Given ◽  
Representation Of Knowledge

AbstractKnowledge representation (KR) is an important area in artificial intelligence (AI) and is often related to specific domains. The representation of knowledge in domain-specific contexts makes it desirable to capture semantics as domain experts would. This motivates the development of semantics-preserving standards for KR within the given domain. In addition to the storage and analysis of information using such standards, the effect of globalization today necessitates the publishing of information on the Web. Thus, it is advisable to use formats that make the information easily publishable and accessible while developing KR standards. In this article, we propose such a standard called Quenching Markup Language (QuenchML). This follows the syntax of the eXtensible Markup Language and captures the semantics of the quenching domain within the heat treating of materials. We describe the development of QuenchML, a multidisciplinary effort spanning the realms of AI, database management, and materials science, considering various aspects such as ontology, data modeling, and domain-specific constraints. We also explain the usefulness of QuenchML in semantics-preserving information retrieval and in text mining guided by domain knowledge. Furthermore, we outline the significance of this work in software tools within the field of AI.

Research-Agenda For Process Simulation Dashboards

2021 ◽  
Author(s):  
Carlo Simon ◽  
Stefan Haag ◽  
Lara Zakfeld
Keyword(s):  
Traffic Management ◽  
Research Agenda ◽  
Domain Knowledge ◽  
Holistic Approach ◽  
Special Focus ◽  
Domain Experts ◽  
Domain Specific ◽  
Domain Specific Knowledge ◽  
Important Field ◽  
Simulation Results

The European Conference on Modelling and Simulation is a prominent but not the only conference showing possibilities and relevance of simulation. Meanwhile, it is an important field of research worldwide and current discussions about the industry of the future and especially the idea of digital twins for the simulation of forecasts in parallel to an existing reality increase its importance. All these efforts led to highly elaborated simulation modeling methods and tools that can be applied to different fields from air traffic management to zoo building. However, based on conference participations, literature research, and conversations with other researchers and practitioners, we observe that simulations are by far not being used as often as possible in day-to-day business. And if they are used, typically individual software solutions are developed that can hardly be transferred to other applications. So, how can we reduce the barriers for using simulation? Any simulation comes along with a profound domain knowledge, a modeling method, a tool for the definition and simulation of models, and the visualization of the simulation results. Different roles conduct these tasks: Domain experts deliver the domain specific knowledge and – as is the case for further members of staff – must be able to interpret the simulation results. Modeling and visualization experts develop the simulations but also deliver a proper presentation for the domain experts, probably without having a deeper understanding of these results. A decision on whether a simulation is conducted at all is made by management, possibly together with the information systems department. The latter roles need information concerning the benefits both in advance as well as in retrospect. Since we mainly work in the field of process modeling and simulation with the aid of Petri nets for production and logistics, the above made considerations encouraged further studies on the usage of simulation with a special focus on dashboard visualization of the simulation results in this field. A holistic approach includes the process of simulation development and use. The research agenda for which a grant could be won is explained within this paper and may animate other researchers to participate.

scholarly journals Representation and Presentation of Culinary Tradition as Cultural Heritage

Heritage ◽  
2021 ◽  
Vol 4 (2) ◽  
pp. 612-640
Author(s):  
Nikolaos Partarakis ◽  
Danai Kaplanidi ◽  
Paraskevi Doulgeraki ◽  
Effie Karuzaki ◽  
Argyro Petraki ◽  
...  
Keyword(s):  
Social Sciences ◽  
Knowledge Representation ◽  
Cultural Heritage ◽  
Systematic Approach ◽  
Semantic Representation ◽  
Ethnographic Research ◽  
Abstract Form ◽  
Web Based ◽  
Social Sciences And Humanities ◽  
Authoring Environment

This paper presents a knowledge representation framework and provides tools to allow the representation and presentation of the tangible and intangible dimensions of culinary tradition as cultural heritage including the socio-historic context of its evolution. The representation framework adheres to and extends the knowledge representation standards for the Cultural Heritage (CH) domain while providing a widely accessible web-based authoring environment to facilitate the representation activities. In strong collaboration with social sciences and humanities, this work allows the exploitation of ethnographic research outcomes by providing a systematic approach for the representation of culinary tradition in the form of recipes, both in an abstract form for their preservation and in a semantic representation of their execution captured on-site during ethnographic research.

A Topic Modeling Approach to Study Design Requirements

2021 ◽  
Author(s):  
Cheng Chen ◽  
Jesse Mullis ◽  
Beshoy Morkos
Keyword(s):  
Domain Knowledge ◽  
Latent Dirichlet Allocation ◽  
High Volume ◽  
Management Tools ◽  
Human Errors ◽  
Domain Experts ◽  
Product Evolution ◽  
Engineering Changes ◽  
Collapsed Gibbs Sampling ◽  
Requirements Documents

Abstract Risk management is vital to a product’s lifecycle. The current practice of reducing risks relies on domain experts or management tools to identify unexpected engineering changes, where such approaches are prone to human errors and laborious operations. However, this study presents a framework to contribute to requirements management by implementing a generative probabilistic model, the supervised latent Dirichlet allocation (LDA) with collapsed Gibbs sampling (CGS), to study the topic composition within three unlabeled and unstructured industrial requirements documents. As finding the preferred number of topics remains an open-ended question, a case study estimates an appropriate number of topics to represent each requirements document based on both perplexity and coherence values. Using human evaluations and interpretable visualizations, the result demonstrates the different level of design details by varying the number of topics. Further, a relevance measurement provides the flexibility to improve the quality of topics. Designers can increase design efficiency by understanding, organizing, and analyzing high-volume requirements documents in confirmation management based on topics across different domains. With domain knowledge and purposeful interpretation of topics, designers can make informed decisions on product evolution and mitigate the risks of unexpected engineering changes.

scholarly journals Cross-Fertilizing Deep Web Analysis and Ontology Enrichment

2017 ◽  
Author(s):  
Marilena Oita ◽  
Antoine Amarilli ◽  
Pierre Senellart
Keyword(s):  
Domain Knowledge ◽  
Deep Web ◽  
Web Pages ◽  
Complete Understanding ◽  
Specific Knowledge ◽  
Domain Specific ◽  
Domain Specific Knowledge ◽  
Web Crawlers ◽  
New Perspective ◽  
The Impact

Deep Web databases, whose content is presented as dynamically-generated Web pages hidden behind forms, have mostly been left unindexed by search engine crawlers. In order to automatically explore this mass of information, many current techniques assume the existence of domain knowledge, which is costly to create and maintain. In this article, we present a new perspective on form understanding and deep Web data acquisition that does not require any domain-specific knowledge. Unlike previous approaches, we do not perform the various steps in the process (e.g., form understanding, record identification, attribute labeling) independently but integrate them to achieve a more complete understanding of deep Web sources. Through information extraction techniques and using the form itself for validation, we reconcile input and output schemas in a labeled graph which is further aligned with a generic ontology. The impact of this alignment is threefold: first, the resulting semantic infrastructure associated with the form can assist Web crawlers when probing the form for content indexing; second, attributes of response pages are labeled by matching known ontology instances, and relations between attributes are uncovered; and third, we enrich the generic ontology with facts from the deep Web.

scholarly journals A Survey of Domain Knowledge Elicitation in Applied Machine Learning

2021 ◽  
Vol 5 (12) ◽  
pp. 73
Author(s):  
Daniel Kerrigan ◽  
Jessica Hullman ◽  
Enrico Bertini
Keyword(s):  
Machine Learning ◽  
Domain Knowledge ◽  
Development Process ◽  
Model Development ◽  
Knowledge Elicitation ◽  
Future Directions ◽  
Domain Experts ◽  
Applied Machine Learning ◽  
Elicitation Process ◽  
Model Development Process

Eliciting knowledge from domain experts can play an important role throughout the machine learning process, from correctly specifying the task to evaluating model results. However, knowledge elicitation is also fraught with challenges. In this work, we consider why and how machine learning researchers elicit knowledge from experts in the model development process. We develop a taxonomy to characterize elicitation approaches according to the elicitation goal, elicitation target, elicitation process, and use of elicited knowledge. We analyze the elicitation trends observed in 28 papers with this taxonomy and identify opportunities for adding rigor to these elicitation approaches. We suggest future directions for research in elicitation for machine learning by highlighting avenues for further exploration and drawing on what we can learn from elicitation research in other fields.

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.

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