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Electronics
Special Issues
AI-Based Knowledge Management
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AI-Based Knowledge Management

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: closed (30 November 2021) | Viewed by 6437

Special Issue Editors

Dr. Dimitris Apostolou

E-Mail Website
Guest Editor
Department of Informatics, University of Piraeus, 18534 Piraeus, Greece
Interests: decision support systems; artificial intelligence; information systems
Special Issues, Collections and Topics in MDPI journals
Dr. Dionisios Sotiropoulos

E-Mail Website
Guest Editor
Department of Informatics, University of Piraeus, Karaoli & Dimitriou 80, 18534 Piraeus, Greece
Interests: machine learning; data mining; evolutionary computing; signal processing; digital social networks
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Technology is replacing human tasks that require great human effort. The integration of knowledge management with advanced artificial intelligence technology has the potential to significantly reduce the amount of effort required to manage traditional knowledge management systems. The transfer of several human-driven tasks to artificial intelligence can reduce administrative overhead, increase the effectiveness of a small number of people to improve the flow of knowledge, to keep knowledge accurate, up-to-date, and trusted, and enhance transparency. Artificial intelligence can enable computer systems to collect knowledge and codify it by using a set of rules and standards without any human interventions. Similarly, artificial intelligence can obtain knowledge and perform knowledge classification, categorization, and indexing. Analytics can provide insights into generated knowledge, codification, and sharing patterns and system performance that were previously unavailable. Artificial intelligence can turn a previously unused and passive system into a dynamic, constantly improving knowledge engine. Artificial intelligence can also leverage the development of effective knowledge management strategic plans.

Although artificial intelligence can shift some of the burden from people to automated, intelligent technology, knowledge-intensive tasks pose several challenges to intelligent technologies. Effective knowledge retrieval over multiple dispersed sources of explicit and codified tacit knowledge, as well as multiple systems with heterogeneous interfaces that are not designed to optimize search, compound the problem. Trust in artificial intelligence methods and algorithms is another major challenge. Further, knowledge management managers need to know exactly how to use the new intelligent methods, what they can optimize with them, and how to demonstrate their impact on management. Finally, the lack of metrics and ROI of artificial intelligence initiative makes it difficult to oversee the value for knowledge management and prove its value over time. These challenges can be a cause of frustration and undermine the application of artificial intelligence.

This Special Issue is seeking high-quality submissions that highlight emerging applications and address recent breakthroughs in the applications of artificial intelligence to knowledge management. The topics of interest include, but are not limited to the following:     

  • Methods for developing effective knowledge management strategies
  • Artificial intelligence for enhanced knowledge sharing
  • Intelligent methods for knowledge codification and storage
  • Knowledge representation and reasoning
  • Intelligent knowledge extraction methods and tools for knowledge management
  • Machine learning methods for knowledge management
  • Data analytics for knowledge management
  • Intelligent knowledge visualization techniques
  • Artificial Intelligence for knowledge-based decision making
  • Applications of AI-enabled knowledge management
  • Methodologies for enabling trust in AI-enabled knowledge management
  • Methodologies for deploying, using, and assessing AI-enabled knowledge management

Dr. Dimitris Apostolou
Dr. Dionisios Sotiropoulos
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (2 papers)

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Research

35 pages, 2568 KiB  
Article
An Attack Simulation and Evidence Chains Generation Model for Critical Information Infrastructures
by Eleni-Maria Kalogeraki, Spyridon Papastergiou and Themis Panayiotopoulos
Electronics 2022, 11(3), 404; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics11030404 - 28 Jan 2022
Cited by 3 | Viewed by 3822
Abstract
Recently, the rapid growth of technology and the increased teleworking due to the COVID-19 outbreak have motivated cyber attackers to advance their skills and develop new sophisticated methods, e.g., Advanced Persistent Threat (APT) attacks, to leverage their cybercriminal capabilities. They compromise interconnected Critical [...] Read more.
Recently, the rapid growth of technology and the increased teleworking due to the COVID-19 outbreak have motivated cyber attackers to advance their skills and develop new sophisticated methods, e.g., Advanced Persistent Threat (APT) attacks, to leverage their cybercriminal capabilities. They compromise interconnected Critical Information Infrastructures (CIIs) (e.g., Supervisory Control and Data Acquisition (SCADA) systems) by exploiting a series of vulnerabilities and launching multiple attacks. In this context, industry players need to increase their knowledge on the security of the CIs they operate and further explore the technical aspects of cyber-attacks, e.g., attack’s course, vulnerabilities exploitability, attacker’s behavior, and location. Several research papers address vulnerability chain discovery techniques. Nevertheless, most of them do not focus on developing attack graphs based on incident analysis. This paper proposes an attack simulation and evidence chains generation model which computes all possible attack paths associated with specific, confirmed security events. The model considers various attack patterns through simulation experiments to estimate how an attacker has moved inside an organization to perform an intrusion. It analyzes artifacts, e.g., Indicators of Compomise (IoCs), and any other incident-related information from various sources, e.g., log files, which are evidence of cyber-attacks on a system or network. Full article
(This article belongs to the Special Issue AI-Based Knowledge Management)
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21 pages, 342 KiB  
Article
Optimization of Decision Trees with Hypotheses for Knowledge Representation
by Mohammad Azad, Igor Chikalov, Shahid Hussain and Mikhail Moshkov
Electronics 2021, 10(13), 1580; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics10131580 - 30 Jun 2021
Cited by 5 | Viewed by 1751
Abstract
In this paper, we consider decision trees that use two types of queries: queries based on one attribute each and queries based on hypotheses about values of all attributes. Such decision trees are similar to the ones studied in exact learning, where membership [...] Read more.
In this paper, we consider decision trees that use two types of queries: queries based on one attribute each and queries based on hypotheses about values of all attributes. Such decision trees are similar to the ones studied in exact learning, where membership and equivalence queries are allowed. We present dynamic programming algorithms for minimization of the depth and number of nodes of above decision trees and discuss results of computer experiments on various data sets and randomly generated Boolean functions. Decision trees with hypotheses generally have less complexity, i.e., they are more understandable and more suitable as a means for knowledge representation. Full article
(This article belongs to the Special Issue AI-Based Knowledge Management)
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