Artificial Intelligence for Knowledge Management with BPMN and Rules

Ligęza, Antoni; Potempa, Tomasz

doi:10.1007/978-3-642-54897-0_2

Cited by 9 publications

(6 citation statements)

References 20 publications

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“…The work aimed to facilitate the COBIT 5 and ISO 27001 simultaneous assessment, with the objective to evaluate Enterprise Governance of Information Technologies. Finally, the work by [51] explores BPMN diagrams and Business Rules (BR) to be used for Knowledge Management. The framework presented in the work is based on the combination of BPMN and BR as a tool for Knowledge Management and it considers specifically the formal knowledge and not the internal knowledge from the individuals of an organisation.…”

Section: Related Workmentioning

confidence: 99%

A framework for assessing capability in organisations using enterprise models

Romero

Guédria

Panetto

et al. 2022

Journal of Industrial Information Integration

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Organisational assessments provide a view of the state of different aspects of an enterprise so as to understand its strengths, weaknesses and possible improvement paths. A relevant element used to perform assessments is the assessment framework, which contains the requirements that the entity must fulfil as well as other aspects. On the other hand, another essential element during an assessment is the Evidence, which is any type of data reflecting the state of the assessed entity. Among several possible assessment evidences, such as documents, e-mails, interview extracts, and questionnaires, Enterprise Models represented through a specific Modelling Language can serve as such, since they allow to describe diverse organisational aspects. Thus, they can provide useful information to be considered when performing an assessment. Notwithstanding, the analysis of the models is demanding in terms of time and resources, and reducing the effort to perform such analysis could improve the assessment process. Thus, a possible manner of improving the analysis process is to understand beforehand which type of information can be found within an enterprise model taking into consideration the modelling language that has been used to represent it and the list of requirements defined by the assessment framework used to perform the assessment. In this sense, this work introduces a method to identify the requirements, devised by an assessment framework, that can be answered through the analysis of modelling elements defined using a specific modelling language. We ground our approach on a Requirement Decomposition task following the Pseudo-Requirement Graph method, and the matching between requirements and modelling language elements following the Goal Question Metric paradigm. To validate our method, we perform separate evaluations of the BPMN and ArchiMate languages, to identify the set of requirements from the ISO/IEC 33020 international standard that can be answered through models that are defined using each language. We also introduce a case study based on the analysis of a concrete business process, following the proposed approach.

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Section: Related Workmentioning

confidence: 99%

A framework for assessing capability in organisations using enterprise models

Romero

Guédria

Panetto

et al. 2022

Journal of Industrial Information Integration

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“…Banyak kajian telah membuktikan faedah manajemen pengetahuan dengan teknologi untuk meningkatkan efisiensi dan juga secara langsung meningkatkan daya saing dan prestasi perusahaan [11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27] atau dengan menggunakan objek perantara [18] [19] [20] [21] [22]. Beberapa penelitian juga memberikan pernyataan tentang aspek apa saja yang harus diperhatikan untuk memastikan pengelolaan manajemen sebuah perusahaan dapat meningkatkan prestasi perusahaan [11] [12] [13] [14] [20] [21].…”

Section: Peran Teknologi Informasi Dalam Manajemen Pengetahuanunclassified

Penerapan Kecerdasan Buatan Dan Teknologi Informasi Pada Efisiensi Manajemen Pengetahuan

Alief

Nurmiati

2022

JMASIF

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Manajemen pengetahuan telah dipelajari dan dibahas sejak lama oleh beberapa peneliti dari akademisi dan sektor bisnis karena vitalitasnya untuk keberhasilan perusahaan. Selain itu, banyak perusahaan terkemuka di seluruh dunia telah mengadopsi beberapa praktik manajemen pengetahuan untuk memastikan bahwa mereka tetap unggul dari pesaing mereka di dunia bisnis yang kompetitif. Oleh karena itu, perusahaan terus mencari cara untuk meningkatkan praktik manajemen pengetahuan. Penelitian ini menggunakan metode literature review terhadap 12 jurnal. Tujuan penelitian ini untuk memiliki pemahaman yang lebih mendalam tentang tren penelitian terbaru dari proses manajemen pengetahuan dan praktik terbaiknya di perusahaan. Namun, subjek ini membutuhkan penyelidikan lebih lanjut dari perspektif lain. Dari hasil penelitian ini tehnik AI yang paling sering di gunakan adalah metode Artificial Neural Network dimana negara yang meneliti subjek terbanyak yaitu Arab Saudi dan UK, dengan studi manajemen pengetahuan yang terkait dengan AI meningkat dalam tiga tahun terakhir dari 2016 hingga 2019. Penelitian ini secara sistematis menerapkan praktik manajemen pengetahuan saat ini yang mengandalkan mekanisme TI dan AI dan dampaknya terhadap perusahaan beserta tantangan dan keterbatasannya.

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“…For the construction of knowledge management models and reasoning models, it involves generalized representation of knowledge and meta-model abstraction based on business activities. Based on ontology, Petri net [7], BPMN model [8], etc., the main research object is the definition of the rules and concept attributes in the model layer and the relationship between the established features in the business. The knowledge base and the feature library are continuously enriched based on existing models in the running environment.…”

Section: Related Workmentioning

confidence: 99%

A Method of Ontology Evolution and Concept Evaluation Based on Knowledge Discovery in the Heavy Haul Railway Risk System

Cao

Montarnal

et al. 2019

Collaborative Networks and Digital Transformation

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The risk pre-control of heavy haul railways is a collaborative scenario with multi-department linkage and the risk analysis model relies on multiple data sources. As a tool for knowledge formal modeling, Ontology and knowledge graph can achieve knowledge discovery, reasoning and decision support based on multi-dimensional heterogeneous data. This paper restores unusual context with participant behavior data as the core, establishes a basic Scenario-Risk-Accident Chain (SRAC) ontology framework. Under collaborative relationships formed by reasoning rules between context and risk, this paper establishes evolution mechanism of SRAC to introduce new knowledge, such as knowledge extracted from device detection data. New entities are added to the risk concept tree through semantic similarity algorithms. In addition, researchers added weight attribute to the risk ontology. With quantitative representation of risk concepts, this paper uses risk relevance mining to establish associatedsubgraphs, establishes a new method for potential accident level assessment through maximum flow search mechanism.

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Artificial Intelligence for Knowledge Management with BPMN and Rules

Cited by 9 publications

References 20 publications

A framework for assessing capability in organisations using enterprise models

A framework for assessing capability in organisations using enterprise models

Penerapan Kecerdasan Buatan Dan Teknologi Informasi Pada Efisiensi Manajemen Pengetahuan

A Method of Ontology Evolution and Concept Evaluation Based on Knowledge Discovery in the Heavy Haul Railway Risk System

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