Exploration of rule-based knowledge bases: A knowledge engineer’s support

Nowak-Brzezińska, Agnieszka; Wakulicz-Deja, Alicja

doi:10.1016/j.ins.2019.02.019

Cited by 23 publications

(10 citation statements)

References 14 publications

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“…The previous research work of the authors showed that, instead of searching whole

with all rules, we usually only search a small piece of it (the experiments showed that usually only a few percent of the total set was reviewed). More about this can be found in [ 5 , 6 ]. Rule clustering allows for the user to browse the

faster.…”

Section: Rule Clusteringmentioning

confidence: 99%

Exploration of Outliers in If-Then Rule-Based Knowledge Bases

Nowak-Brzezińska

Horyń

2020

Entropy

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The article presents both methods of clustering and outlier detection in complex data, such as rule-based knowledge bases. What distinguishes this work from others is, first, the application of clustering algorithms to rules in domain knowledge bases, and secondly, the use of outlier detection algorithms to detect unusual rules in knowledge bases. The aim of the paper is the analysis of using four algorithms for outlier detection in rule-based knowledge bases: Local Outlier Factor (LOF), Connectivity-based Outlier Factor (COF), K-MEANS, and SMALLCLUSTERS. The subject of outlier mining is very important nowadays. Outliers in rules If-Then mean unusual rules, which are rare in comparing to others and should be explored by the domain expert as soon as possible. In the research, the authors use the outlier detection methods to find a given number of outliers in rules (1%, 5%, 10%), while in small groups, the number of outliers covers no more than 5% of the rule cluster. Subsequently, the authors analyze which of seven various quality indices, which they use for all rules and after removing selected outliers, improve the quality of rule clusters. In the experimental stage, the authors use six different knowledge bases. The best results (the most often the clusters quality was improved) are achieved for two outlier detection algorithms LOF and COF.

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“…The previous research work of the authors showed that, instead of searching whole

faster.…”

Section: Rule Clusteringmentioning

confidence: 99%

Exploration of Outliers in If-Then Rule-Based Knowledge Bases

Nowak-Brzezińska

Horyń

2020

Entropy

Self Cite

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“…We want our solution to be universal and, therefore, to work effectively both for data with outliers and for typical data. We also want our solution to be effective for any data type, not just only numerical, which is easier to analyze [ 2 , 3 , 4 ].…”

Section: Introductionmentioning

confidence: 99%

Qualitative Data Clustering to Detect Outliers

Nowak-Brzezińska

Łazarz

2021

Entropy

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Detecting outliers is a widely studied problem in many disciplines, including statistics, data mining, and machine learning. All anomaly detection activities are aimed at identifying cases of unusual behavior compared to most observations. There are many methods to deal with this issue, which are applicable depending on the size of the data set, the way it is stored, and the type of attributes and their values. Most of them focus on traditional datasets with a large number of quantitative attributes. The multitude of solutions related to detecting outliers in quantitative sets, a large and still has a small number of research solutions is a problem detecting outliers in data containing only qualitative variables. This article was designed to compare three different categorical data clustering algorithms: K-modes algorithm taken from MacQueen’s K-means algorithm and the STIRR and ROCK algorithms. The comparison concerned the method of dividing the set into clusters and, in particular, the outliers detected by algorithms. During the research, the authors analyzed the clusters detected by the indicated algorithms, using several datasets that differ in terms of the number of objects and variables. They have conducted experiments on the parameters of the algorithms. The presented study made it possible to check whether the algorithms similarly detect outliers in the data and how much they depend on individual parameters and parameters of the set, such as the number of variables, tuples, and categories of a qualitative variable.

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“…This research contributes to the development of a more robust and knowledge-intensive system, aiming to aid design practice and enhance creative behaviour on design teams. By emulating heuristic CIT selection with a KBS, knowledge management on creativity becomes more integrated, permanent and accessible (Giarratano and Riley, 2005;Duan et al, 2019;Nowak-Brzezi nska and Wakulicz-Deja, 2019). Naturally, several AI methods can contribute to improving creativity in design.…”

Section: Introductionmentioning

confidence: 99%

“…In this work, the development of a KBS based on rules using object orientation paradigms aimed to explicitly systematize experts' knowledge, managing how information flows along the inferencing process and making such knowledge-intensive expertise accessible to users (Blume et al, 2018;Mutawa and Alzuwawi, 2019). Rule-based approach, contrasting other knowledge management methods such as trees, semantic networks and schemas, are also scalable for complex empirical knowledge, straightforward for computational implementation and of simple information retrieval (Yin, 2016;Nowak-Brzezi nska and Wakulicz-Deja, 2019). Other implementation paradigms such as machine learning algorithms, though able to work with higher volumes of information, would incur in keeping parts of the process as black boxes (Molnar, 2019;Stewart, 2019).…”

Section: Introductionmentioning

confidence: 99%

An artificial intelligence approach to support knowledge management on the selection of creativity and innovation techniques

Botega

Silva

2020

JKM

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Purpose Creativity is an important skill for design teams to reach new and useful solutions. Designers often use one or more of creativity and innovation techniques (CITs) to achieve the desired creative potential during new product development (NPD). The selection of adequate CITs requires considerable expertise, given the multiple application contexts and the extensive number of techniques available. The purpose of this study is to present a creativity support system able to manage this amount of information and provide valuable knowledge to improve NPD. Design/methodology/approach This study presents a knowledge-based system prototype using artificial intelligence (AI) to support knowledge management on the selection of CITs for design. CITs assertion is modelled through a double inference process using five categories, correlating over 500 different entry scenarios to 24 implemented CITs. The techniques are classified according to: design stage, innovation focus, team relationship, execution method and difficult of use. Prototype outputs explanations on the inference process and chosen techniques information. Findings To demonstrate the system scope, two opposite design cases are presented. The system was validated by experts in knowledge management and mechanical engineering design. The validation process demonstrates relevance of the approach and improvement directions for future developments. Originality/value Though literature contains toolkits and taxonomy for CITs, no work applies AI to identify design scenarios, select best CITs and instruct about their use. Validators reported to know less than half of the available techniques, showing a clear knowledge gap among design experts.

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Exploration of rule-based knowledge bases: A knowledge engineer’s support

Cited by 23 publications

References 14 publications

Exploration of Outliers in If-Then Rule-Based Knowledge Bases

Exploration of Outliers in If-Then Rule-Based Knowledge Bases

Qualitative Data Clustering to Detect Outliers

An artificial intelligence approach to support knowledge management on the selection of creativity and innovation techniques

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