METAMODEL ENGINEERING FOR SUPPORTING FUZZY KNOWLEDGE BASE SYNTHESIS

2020 ◽  
pp. 34-47
Author(s):  
N. O. Dorodnykh ◽  
O. A. Nikolaychuk ◽  
A. Yu. Yurin
Keyword(s):  
Knowledge Base ◽  
Code Generation ◽  
Conceptual Models ◽  
Conceptual Modeling ◽  
Fuzzy Rule ◽  
Engineering Problem ◽  
Model Transformations ◽  
Source Codes ◽  
Automated Generation ◽  
Fuzzy Knowledge

The paper is devoted to fuzzy knowledge base engineering problem. The effectiveness of this process can be improved by automated generation of source codes and analysis of data presented in different forms, in particular, in the form of conceptual models describing a certain subject domain. The knowledge base code generation is based on the transformation of conceptual models from the model-based approach and the use of metamodels. The metamodeling provides the description of the source and target formalisms of conceptual modeling and knowledge representation. We present an approach for fuzzy knowledge base engineering based on model transformations. In particular, metamodels for describing fuzzy rule-based models and fuzzy ontologies and method for automated metamodel generation are presented.

scholarly journals FUZZY KNOWLEDGE BASE ENGINEERING BASED ON TRANSFORMATION OF FUZZY CONCEPTUAL MODELS

2020 ◽  
Author(s):  
Н.О. Дородных ◽  
О.А. Николайчук ◽  
А.Ю. Юрин ◽  
С.А. Коршунов
Keyword(s):  
Knowledge Base ◽  
Conceptual Models ◽  
Fuzzy Knowledge

Разработка баз знаний остается важной областью научных исследований. Эффективность этого процесса может быть повышена за счет автоматизированного анализа существующих моделей предметной области в виде концептуальных диаграмм различных типов. В данной работе предлагается подход, который может быть использован для прототипирования нечетких продукционных баз знаний путем преобразования концептуальных моделей с нечеткими факторами. Предлагаемый подход включает в себя: расширенный предметно-ориентированный декларативный язык описания моделей трансформаций (TMRL); методику автоматизированного анализа и преобразования нечетких концептуальных моделей, реализованных в XML-подобных форматах; программные средства в форме модулей-конвертеров для системы разработки баз знаний (KBDS), поддерживающие предложенный подход. Апробация подхода осуществлена при создании баз знаний для решения задач автоматизации проведения экспертизы промышленной безопасности.

scholarly journals A Quality Model for Conceptual Models of MDD Environments

2010 ◽  
Vol 2010 ◽  
pp. 1-17 ◽  
Author(s):  
Beatriz Marín ◽  
Giovanni Giachetti ◽  
Oscar Pastor ◽  
Alain Abran
Keyword(s):  
Defect Detection ◽  
Code Generation ◽  
Conceptual Models ◽  
Model Transformations ◽  
Quality Model ◽  
Suitable Alternative ◽  
Model Driven ◽  
Software Products ◽  
Detection Of Defects

In Model-Driven Development (MDD) processes, models are key artifacts that are used as input for code generation. Therefore, it is very important to evaluate the quality of these input models in order to obtain high-quality software products. The detection of defects is a promising technique to evaluate software quality, which is emerging as a suitable alternative for MDD processes. The detection of defects in conceptual models is usually manually performed. However, since current MDD standards and technologies allow both the specification of metamodels to represent conceptual models and the implementation of model transformations to automate the generation of final software products, it is possible to automate defect detection from the defined conceptual models. This paper presents a quality model that not only encapsulates defect types that are related to conceptual models but also takes advantage of current standards in order to automate defect detection in MDD environments.

scholarly journals Short-Term Fuzzy Load Forecasting Model Using Genetic–Fuzzy and Ant Colony–Fuzzy Knowledge Base Optimization

2018 ◽  
Vol 8 (6) ◽  
pp. 864 ◽  
Author(s):  
Murat Luy ◽  
Volkan Ates ◽  
Necaattin Barisci ◽  
Huseyin Polat ◽  
Ertugrul Cam
Keyword(s):  
Knowledge Base ◽  
Load Forecasting ◽  
Ant Colony ◽  
Forecasting Model ◽  
Short Term ◽  
Fuzzy Knowledge

Towards evolutionary knowledge representation under the big data circumstance

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Xuhui Li ◽  
Liuyan Liu ◽  
Xiaoguang Wang ◽  
Yiwen Li ◽  
Qingfeng Wu ◽  
...  
Keyword(s):  
Big Data ◽  
Knowledge Base ◽  
Data Model ◽  
Directed Acyclic Graph ◽  
Conceptual Models ◽  
Structural Features ◽  
New Approach ◽  
Content Type ◽  
Semantic Data ◽  
Semantic Data Model

Purpose The purpose of this paper is to propose a graph-based representation approach for evolutionary knowledge under the big data circumstance, aiming to gradually build conceptual models from data. Design/methodology/approach A semantic data model named meaning graph (MGraph) is introduced to represent knowledge concepts to organize the knowledge instances in a graph-based knowledge base. MGraph uses directed acyclic graph–like types as concept schemas to specify the structural features of knowledge with intention variety. It also proposes several specialization mechanisms to enable knowledge evolution. Based on MGraph, a paradigm is introduced to model the evolutionary concept schemas, and a scenario on video semantics modeling is introduced in detail. Findings MGraph is fit for the evolution features of representing knowledge from big data and lays the foundation for building a knowledge base under the big data circumstance. Originality/value The representation approach based on MGraph can effectively and coherently address the major issues of evolutionary knowledge from big data. The new approach is promising in building a big knowledge base.

The REA Pattern, Knowledge Structures, and Conceptual Modeling Performance

2005 ◽  
Vol 19 (2) ◽  
pp. 57-77 ◽  
Author(s):  
Gregory J. Gerard
Keyword(s):  
Domain Knowledge ◽  
Conceptual Models ◽  
Conceptual Modeling ◽  
Knowledge Structure ◽  
Specific Pattern ◽  
Accounting Information Systems ◽  
Domain Specific ◽  
Domain Specific Knowledge ◽  
Business Setting ◽  
Structured Knowledge

Most database textbooks on conceptual modeling do not cover domainspecific patterns. The texts emphasize notation, apparently assuming that notation enables individuals to correctly model domain-specific knowledge acquired from experience. However, the domain knowledge acquired may not aid in the construction of conceptual models if it is not structured to support conceptual modeling. This study uses the Resources Events Agents (REA) pattern as an example of a domain-specific pattern that can be encoded as a knowledge structure for conceptual modeling of accounting information systems (AIS), and tests its effects on the accuracy of conceptual modeling in a familiar business setting. Fifty-three undergraduate and forty-six graduate students completed recall tasks designed to measure REA knowledge structure. The accuracy of participants' conceptual models was positively related to REA knowledge structure. Results suggest it is insufficient to know only conceptual modeling notation because structured knowledge of domain-specific patterns reduces design errors.

Web Intelligence

Big Data ◽  
2016 ◽  
pp. 711-733 ◽  
Author(s):  
Jafreezal Jaafar ◽  
Kamaluddeen Usman Danyaro ◽  
M. S. Liew
Keyword(s):  
Big Data ◽  
Knowledge Base ◽  
Data Model ◽  
Performance Comparison ◽  
Web Ontology Language ◽  
Web Intelligence ◽  
Knowledge Based ◽  
Ontology Language ◽  
And Performance ◽  
Fuzzy Knowledge

This chapter discusses about the veracity of data. The veracity issue is the challenge of imprecision in big data due to influx of data from diverse sources. To overcome this problem, this chapter proposes a fuzzy knowledge-based framework that will enhance the accessibility of Web data and solve the inconsistency in data model. D2RQ, protégé, and fuzzy Web Ontology Language applications were used for configuration and performance. The chapter also provides the completeness fuzzy knowledge-based algorithm, which was used to determine the robustness and adaptability of the knowledge base. The result shows that the D2RQ is more scalable with respect to performance comparison. Finally, the conclusion and future lines of the research were provided.

Supporting Electronic Collaboration in Conceptual Modeling

2010 ◽  
pp. 199-211
Author(s):  
Peter Rittgen
Keyword(s):  
Conceptual Models ◽  
Conceptual Modeling ◽  
Negotiation Process ◽  
Distributed Development ◽  
Collaborative Modeling ◽  
Electronic Collaboration ◽  
The Social

The authors study collaborative modeling by analyzing conversations and loud thinking during modeling sessions and the resulting models themselves. They identify the basic activities of the modeling teams on the social, pragmatic, semantic and syntactic levels and derive a schema for the pragmatic level. The authors’ main conclusion is that team-based modeling is largely a negotiation process. Drawing on these results the authors derive an architecture of a system that supports the distributed development of conceptual models.

Using Ontology Languages for Conceptual Modeling

2012 ◽  
pp. 1-28
Author(s):  
Palash Bera ◽  
Anna Krasnoperova ◽  
Yair Wand
Keyword(s):  
Conceptual Models ◽  
Conceptual Modeling ◽  
Systems Development ◽  
Information Systems Development ◽  
Semantic Relationships ◽  
Support Domain ◽  
Domain Semantics ◽  
Ontology Languages ◽  
Conceptual Modeling Grammar ◽  
Precise Representation

Conceptual models are used to support understanding of and communication about application domains in information systems development. Such models are created using modeling grammars (usually employing graphic representation). To be effective, a grammar should support precise representation of domain concepts and their relationships. Ontology languages such as OWL emerged to define terminologies to support information sharing on the Web. These languages have features that enable representation of semantic relationships among domain concepts and of domain rules, not readily possible with extant conceptual modeling techniques. However, the emphasis in ontology languages has been on formalization and being computer-readable, not on how they can be used to convey domain semantics. Hence, it is unclear how they can be used as conceptual modeling grammars. We suggest using philosophically based ontological principles to guide the use of OWL as a conceptual modeling grammar. The paper presents specific guidelines for creating conceptual models in OWL and demonstrates, via example, the application of the guidelines to creating representations of domain phenomena. To test the effectiveness of the guidelines we conducted an empirical study comparing how well diagrams created with the guidelines support domain understanding in comparison to diagrams created without the guidelines. The results indicate that diagrams created with the guidelines led to better domain understanding of participants.

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