=Paper=
{{Paper
|id=Vol-3795/icaiw_wkmit_4
|storemode=property
|title=Knowledge Management: Innovation, Technology and Ontologies
|pdfUrl=https://ceur-ws.org/Vol-3795/icaiw_wkmit_4.pdf
|volume=Vol-3795
|authors=Luciano Straccia,María F. Pollo-Cattaneo
|dblpUrl=https://dblp.org/rec/conf/icai2/StracciaC24
}}
==Knowledge Management: Innovation, Technology and Ontologies==
https://ceur-ws.org/Vol-3795/icaiw_wkmit_4.pdf
Knowledge Management: Innovation, Technology and
Ontologies
Luciano Straccia* , María F. Pollo-Cattaneo
Universidad Tecnológica Nacional, Buenos Aires, Argentina
Abstract
Knowledge is an essential resource to create value for organizations and requires adequate Knowledge Man-
agement to generate, retain, and apply it and obtain competitive advantages. Knowledge management implies
considering the people who are part of the organization as a key factor, proposing organizational cultures and
aspects that favor it, and applying processes and technologies. This research used a literature review of articles
published in the DBLP Computer Science Bibliography in first and second-quartile journals based on the Scimago
Ranking Journal to analyze the presence of Knowledge Management, which views are considered, and which
aspects of technology are most relevant. The results of this research provide an overview of relevant topics
about knowledge management and allow shows the relevance relationship between knowledge management
and innovation, and artificial intelligence and ontologies as a fundamental part of the application of knowledge
management.
Keywords
Knowledge, Knowledge Management, Innovation, Ontology, Technology.
1. Introduction
Knowledge is an essential resource to create value for organizations that includes experiences, values,
and non-contextual information allowing transformation of the organizational culture and process and
increasing the economic value of companies. It required adequate management to generate, retain and
apply it and obtain competitive advantages. Knowledge Management is a multi-disciplinary approach
that seeks structured and systematic ways and allows management the knowledge. This paper presents
in section 2 the theoretical basis associated with knowledge, knowledge management, and some terms
and concepts related whit its: the views and categories for KM technologies analysis. The section 3
presents the background of the research group, the objectives of this article to increase knowledge
on the topic, the proposed research questions, and the method and scope of this article. The section 4
shows the search execution in 5 steps: search execution on DBLP, the Scimago Database download,
match between DBLP and Scimago and paper selection by quartile distribution, paper selection by
access and paper selection by topic. Section 5 presents the results associated with the research questions.
Finally, the conclusions are presented in Section 6.
2. Theorical basis
This section presents the theoretical basis for this article describing the concept of Knowledge Man-
agement 2.1, its views 2.2, and the categories of analysis of knowledge management technologies
2.3.
2.1. Knowledge Management
Knowledge Management is a multi-disciplinary approach that seeks structured and systematic ways
and allows management the knowledge. Perez and Urbáez [1] defines knowledge management as “a
ICAIW 2024: Workshops at the 7th International Conference on Applied Informatics 2024, October 24–26, 2024, Viña del Mar, Chile
*
Corresponding author.
$ lstraccia@frba.utn.edu.ar (L. Straccia); flo.pollo@gmail.com (M. F. Pollo-Cattaneo)
0000-0002-1183-7944 (L. Straccia); 0000-0003-4197-3880 (M. F. Pollo-Cattaneo)
© 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
264
Luciano Straccia et al. CEUR Workshop Proceedings 264–279
managerial approach or emerging discipline that seeks in a structured and systematic way to take
advantage of the knowledge generated to achieve the objectives of the organization and optimize the
decision-making process”. Knowledge is the most important strategic resource [2] and the companies
need to generate processes to manage this resource and take advantage of the experience and skills
of talents and leaders in the face of environmental contingencies, or the risks and threats of the
context [3, 4, 5]. Knowledge resides in people and therefore its transmission implies a voluntary act of
people, understanding knowledge as a “mixture of structured experiences, values, and non-contextual
information that provides a framework for evaluating new experiences and information” [6]. It should
be noted that the boundary for establishing whether a job involves knowledge or just routine operational
actions without putting one’s knowledge into action is still under debate.
2.2. Knowledge Management Views
A system view is a representation of the system from the perspective of a specific set of related concerns,
which suppresses details to provide a simplified model that has only the elements related to the viewpoint
concerns [7, 8, 9] and allows a particular element to be examined from a specific scope. A knowledge
management view describes the concepts, elements, and characteristics of an integrated knowledge
management system. Several authors use alternative terms to refer to the views: dimensions [10, 11],
factors [12], components [13], drivers [14, 15] and critical success factors [16, 17].
Straccia et al. [18] shows an analysis of the views in different papers and the following views are
obtained: a) individuals or people, b) organizational aspects, c) activities and processes, d) measurement,
and e) technology; the technology view included the knowledge representation topics.
2.3. Categories for Technologies on Knowledge Management
There are different technologies for knowledge management, which can be categorized as follows [19]:
a) socialization techniques, b) techniques or models for knowledge explanation and representation, c)
field of study, d) logical and analytical process, e) organizational practices, and f) technological tools.
Socialization techniques are those that allow the exchange of experiences and the transfer and
acquisition of tacit knowledge. Socialization is a concept that arose in other disciplines (especially
sociology) but was promoted in knowledge management through the SECI model designed by Nonaka
and Takeuchi [20]; socialization technique includes e.g. Community of practices, Mentoring, or Expert
assistance.
The techniques or models for knowledge explanation and representation included the ways and
techniques to make knowledge explicit in some support or model, including those probable models, like
case studies, catalogs, directories, knowledge maps, ontologies, etc. Each technique or model is likely
to be supported by technological tools, but these tools are not included in this category. For Davis et
al. [21] knowledge representations “are also how we express things about the world, the medium of
expression and communication in which we tell the machine (and perhaps one another) about the world.
This role for representations is inevitable so long as we need to tell the machine (or other people) about
the world, and so long as we do so by creating and communicating representations”. For Avramenko y
Kraslawski [22] the knowledge representation “is the study of how knowledge about the world can be
represented and what kinds of reasoning can be done with that knowledge".
A field of study refers to a branch of knowledge or a set of branches of knowledge with interdisciplinary
action; each field can include processes, technologies, etc. Some fields of study can be considered
Artificial Intelligence, Big Data, Business Intelligence (BI), Cybersecurity, and the Internet of Things
(IoT).
The logical and analytical process category corresponds to data and information processing and their
treatment activities. It may include technological tools but especially involves analysis and exploitation
processes. Some examples of logical and analytical processes are workflow, data mining, and text
mining.
265
Luciano Straccia et al. CEUR Workshop Proceedings 264–279
Organizational practice is a category that represents a mechanism to communicate its values, norms,
and goals to its employees and is instrumental in corporate education, virtual learning environment,
and virtual reality and simulation [23].
Finally, the last category is technological tools: artifacts that able to be deployed in a technological
infrastructure environment, including the software, part of the software, or similar. In general, these
tools can be modeled as components in a sequence diagram or deploy diagram of Unified Modeling
Language. The different technological tools are grouped according to the following subcategories:
type of systems, repositories and storage media, bidirectional communication tools, tools for content
presentation, and others.
3. Research Objectives, Method and Scope
The technology for knowledge management has been a concern for the research group in recent years
[18, 19, 24]: from the preliminary identification of some technologies to the identification and proposals
of architecture. It has also carried out several research related to knowledge management models,
processes, and measurement.
The objective of this paper is to analyze the aspects related with knowledge management in the most
recognized academic literature, both on knowledge management issues in general and on technologies
for the application of knowledge management in particular.
The following research questions proposed are:
1. Does the academic literature perform specific analyses for some of the views of knowledge
management or does it deal with more general issues?
2. What are the views of knowledge management present in the academic literature?
3. What general issues do the literature present?
4. What information do you provide about the technology for knowledge management?
Systematic Mapping literature on DBLP Computer Science Bibliography is carried out in this paper.
Systematic Mapping Studies (also known as Scoping Studies) are designed to provide a wide overview
of a research area [25] and used to structure a research area, while systematic reviews are focused
on gathering and synthesizing evidence [26]; this type of study allows summarize and disseminate
research findings and to identify research gaps in the existing literature [27].
The mapping was executed on DBLP; this portal aims to “cover all areas of computing (from algo-
rithms, artificial intelligence, compilers, data mining, bioinformatics, networking, robotics, security,
virtualization...)” and has more than 6 million indexed papers [28] being an online reference of the main
computer science publications [29]. These correspond to Step 1 and are detailed in 4.1.
To identify the journals with the highest academic level, the journals present in Quartile 1 (Q1) and
Quartile 2 (Q2) were selected using the Scimago Journal Rank, a ranking with an international scope,
and whose data source based on Scopus [30] and a match between BDLP and Scimago Journal Rank is
executed for identify the quartile of the publication to which corresponds each paper. These activities
are step 2 (shown in 4.2) and step 3 (in 4.3).
For Q1 and Q2 papers found in the previous step, a search is carried out to obtain access considering
those that have free access, are accessible through a portal or with University credentials. The results
are presented as step 4 in section 4.4.
Finally, in step 5 (section 4.5) a selection of documents is presented, eliminating those that meet any of
the following criteria: 1. keyword only in the bibliographic references, in index terms (or keywords) or
the authors’ biography; 2. non-relevant use of the term; or 3. belonging or not to the topic “knowledge
management” and in compliance with the theoretical framework and the KM concept proposed in this
work. The selected papers after step 5 are used to analyze the results presented in section 5.
266
Luciano Straccia et al. CEUR Workshop Proceedings 264–279
4. Search Execution
This section presents the execution of the review and the selection of the works to be analyzed in the
following section.
4.1. Step 1: Search execution on DBLP
A search on DBLP Computer Science Bibliography is executed to obtain articles associated with
knowledge management with the criteria presented in Table 1. A total of 376 articles were found: 126
corresponding to 2021, 126 corresponding to 2022, and 124 corresponding to 2023.
Table 1
Search Criteria
Criterial Value
Keyword “knowledge management”
Publication Type Journal Article
Years 2021,2022,2023
4.2. Step 2: Scimago Database download
The Scimago database is downloaded with 28740 sources identified with their SJR Best Quartile: 8702
belong to Q1; 7295 to Q2; 6674 to Q3 and 6069 to Q4.
4.3. Step 3: Match between DBLP and Scimago and paper selection by quartile
distribution
On the Scimago database, a search is made for the quartile to which each of the journals where each
of the articles obtained in DBLP were published belongs, to associate each paper with a quartile. The
journal and quartile are identified for 329 papers and a database of publications is generated with the
following fields: title of the publication, year, journal, and quartile. Regarding the papers found in DBLP
corresponding to each year, their quartile distribution is obtained and presented in Table 2. Considering
the scope of the present work, the 301 papers corresponding to Q1 and Q2 are selected.
Table 2
Articles for year and quartile distribution
Year Q1 Q2 Q3 Q4 Not Found Total
2021 74 21 11 1 19 126
2022 78 23 6 3 16 126
2023 84 21 6 1 12 124
Total 236 65 23 5 47 376
4.4. Step 4: Paper selection by access
For Q1 and Q2 papers found in the previous step, a search is carried out to obtain access considering
those that have free access, are accessible through a portal or with University credentials. The results
are presented in Table 3. A total of 121 papers were found for which access is available.
267
Luciano Straccia et al. CEUR Workshop Proceedings 264–279
Table 3
Articles Q1 y Q2 with access and without access
Year Q1 with access Q2 with access Q1 without access Q2 without access Total
2021 49 10 25 11 95
2022 28 12 50 11 101
2023 19 3 65 18 105
Total 96 25 140 40 301
4.5. Step 5: Paper selection by topic
35 five papers were found to be discarded for the first criterion (keyword only in the bibliographic
references, in index terms (or keywords) or the authors’ biography) and 9 for the second (non-relevant
use of the term) resulting in 77 papers to be analyzed in detail in section 5.
5. Results
Each of the subsections presented in this section corresponds to research questions. The first section
5.1 corresponds to RQ1 (first research question) and RQ2; the second section 5.2 corresponds to RQ3
and finally the third section 5.3 corresponds to RQ4.
5.1. Knowledge Management and Knowledge Management Views (RQ1 and RQ2)
This section seeks to answer the first two questions: Does the academic literature perform specific
analyses for some of the views of knowledge management or does it deal with more general issues?
and What are the views of knowledge management present in the academic literature?
Each of the 77 articles analyzed, is investigated if it deals with some of the knowledge management
views proposed in the theoretical bases or if it deals with general issues. We found 41 papers presenting
general issues and 37 papers dealing with some views. The distribution of views found is presented in
table 4.
Table 4
Search Results for Knowledge Management View
View Value
Individual 5
Organizational Aspects 7
Activities and Process 4
Technologies 25
Measurement 0
For each paper found with general issues es performed a detailed analysis for response the third
research question: What general issues does the literature present? The method and results are presented
in the next section. The papers found on the Technology view are analyzed in 5.4.
5.2. General Issues (RQ3)
For the papers found with general issues (41 articles) the method of analysis 5.2.1 and the synthesis of the
analysis 5.2.2 are presented. Then some of the categories found in the analysis are treated individually
in the following subsections. This section finds a response to the third research question.
268
Luciano Straccia et al. CEUR Workshop Proceedings 264–279
5.2.1. Method of Analysis
For the analysis of the results obtained in the review presented in the previous section, an analysis
with open coding is carried out, based on the systemic design for the qualitative research [31] and the
procedures of Strauss and Corbin [32]. In the open coding, "all the segments of the material obtained
for analysis are reviewed and it generates -by constant comparison- initial categories of meaning. It
thus eliminates redundancy and develops evidence for the categories (raises the level of abstraction)"
[31]. The categories are created from an interpretation of the data [33].
5.2.2. Synthesis of the analysis
Table 5 shows the categories found for the different studies analyzed. In [34] the detail of the analyzed
articles and their categorization can be found here.
Table 5
Categories for General Issues
Category Results
Innovation 10
Framework 6
ICT 5
case study 5
views 2
relationship: KM - 4IR 2
relationship: KM - software development 1
relationship: KM – Agile 1
relationship: KM - risk management 1
relationship: KM - Smart City 1
relationship: KM - Business Process 1
Ambidexterity 1
Performance 1
Practices 1
Collaborative innovation 1
Portfolio 1
Domains 1
strategic foresight 1
emerging technologies 1
Terminology 1
morphological framework 1
perceptions 1
The most discussed topics in the works found are innovation with some specific reference to collabo-
rative innovation; these topics are detailed in 5.2.3 and 5.2.4, respectively.
The presence of frameworks or general models of knowledge management is common in the academic
literature; they address general aspects of the subject and the identification of the components (called
views in the theoretical bases of this article); similarly, the works are specifically linked to identifying
views of knowledge management.
The relationship with information and communication technology (ICT) is addressed in different
works already mentioned in this article that gave rise to the definition of technology as a view of
knowledge management. Its specific approach is found in the articles that are analyzed in 5.3.
Several papers present the relationship of knowledge management with other disciplines (software
development [35], agile [36], risk management [37], Smart City [38] and Business Process [39]) and
with aspects of the technological revolution [40, 41]. In addition, specific case studies in different
institutions are presented. Zanker and Bures [42] analyze the domains of knowledge management
use in a systematic review of the literature and identify three groups: the first group is composed
269
Luciano Straccia et al. CEUR Workshop Proceedings 264–279
of Business, Education, and Managerial Disciplines; the second group, decision-making, Managerial
Functions, Miscellaneous Knowledge Areas and Software Engineering; and the last group is a subset of
models characterized by the fact that they employ KM KMP expressions while encapsulating domain
knowledge. Linked to Software Engineering the authors propose references to Mishra and Mahanty
[43, 44] and Jafari [45].
Other topics are addressed by only 1 article: ambidextery, performance, practices, portfolio, strategic
foresight, emerging technologies, terminology, morphological framework, and perceptions.
5.2.3. Innovation
Organizational innovation is "the organization’s capability to convert its human resources knowledge
and integrate it to have new knowledge that produces a new product or a process" [46]. For [47],
innovation refers "to a wide range of actions, products, and processes such as the improvement of
administrative, planning, and programming systems, production processes, and the development of
new products or the improvement of existing ones" and says that dissemination of knowledge facilitates
innovation [47, 48].
The purpose of knowledge management is to offer innovation, so the relationship between these
is often studied [49]. The knowledge dissemination affects innovative ability [50], there is a positive
association between KM and innovation [51], and KM processes have a significant relationship with
competitive strategy and innovativeness of firms [52]. The knowledge shared and transferred from
multiple partners results in synergy for new knowledge creation and innovation performance [53, 54].
Zhang et al. [53] inquire about how knowledge to carry out technological innovation.
The KM literature considers innovation as a critical factor for companies in creating value and
maintaining a competitive advantage in today’s highly complex and dynamic environment [55, 56]; the
application of knowledge would lead to innovation [57], a topic that is studied for [58] with evidence
from academician workstations in China. [56, 59] investigate the impact of knowledge management
practices (KMPs) on innovation performance.
The paper of Zhao [60] discusses the concept of dual innovation and the relationship between
knowledge acquisition ability and dual innovation synergy.
[61] present a survey on a sample of 115 tourism lifestyle entrepreneurs, with the hypotheses tested
about the relationship between innovation and KM using structural equations. For the authors, local
knowledge plays an important role in TLEs’ innovation and competitiveness because it is tacit and
difficult to imitate [62, 63]. Hoarau [64] has recognized that generating and using (assimilating) new
knowledge from external sources is an important predictor of innovation capacity. The fact that local
knowledge acquisition affects its assimilation was also validated since it is through the acquisition of
new knowledge that it is possible to transform the existing knowledge into innovation to produce a
new concept within the organization.
Innovation is considered by some authors as an activity within the knowledge management process
[65] and the relationship between innovation and knowledge management is studied by Gloet in his
works [66, 67] and by Chaabane [68]. Gloet [66] presents the concept of Knowledge Innovation Man-
agement and the KIM Capability Model and defines the KIM as “the design, implementation, and review
of social and technological activities and processes to improve the creation, sharing, dissemination, and
use of knowledge to support innovation”.
Knowledge sharing has a huge impact on the success of distributed innovation [35] y Companies
that specialize in new product development (NPD) might profit from knowledge collecting by growing
and increasing the quality of their offerings.
Finally, [46, 49] makes a very important survey on the relationship between knowledge management
and innovation. It is the most specific work to trace the historical relationship between both aspects
including references to [69, 70, 71] and exploring the empirical relationship of the effects of knowledge
management on innovation following the contributions initiated by Darroch [72].
270
Luciano Straccia et al. CEUR Workshop Proceedings 264–279
5.2.4. Collaborative Innovation
Zhang [53] proposes a new technological innovation paradigm based on collaborative manufacturing and
open innovation; collaborative innovation refers "to a network innovation model based on interactions
of multiple parties, by taking universities, enterprises, and research institutions as core elements and
by considering government, financial institutions, intermediaries, innovation platforms, and nonprofit
organizations as auxiliary elements" [53, 73]. This strategy facilitates knowledge flow and promotes
learning from each other [53, 74, 75] through their participation in alliances.
5.3. Technology for Knowledge Management (RQ4)
For the papers found with technology view (25) the method of analysis 5.3.1 and the synthesis of the
analysis 5.3.2 are presented and some of the categories found in the analysis are treated individually in
the following subsections. This section finds a response to the fourth research question.
5.3.1. Method of Analysis
The different core technologies of each article are identified and categories are assigned according to
the theoretical bases presented in 2.3.
5.3.2. Synthesis of the analysis
Table 6 shows the technologies (and knowledge representation) found for the different studies analyzed
and their categorization. In [34] the detail of the analyzed articles and the technology for each paper.
Table 6
Technologies and their Categories
Technology Category Results
Ontology techniques or models for knowledge explanation and representation 5
Artificial Intelligence Fields of study 4
Knowledge Graph techniques or models for knowledge explanation and representation 3
Secure KM Fields of study 2
Natural Language Processing Fields of study 2
Knowledge Base Technological tool 2
Machine Learning Fields of study 1
Big Data Fields of study 1
Wiki techniques or models for knowledge explanation and representation 1
Decision Support System Technological tool 1
IT platform analytics Technological tool 1
KMS Technological tool 1
Simulation Organizational practices 1
learning management expert system Technological tool 1
Mobile Technologies Technological tool 1
The following sections deal specifically with those technologies that were found in more than 1
paper: Ontologies 5.3.3, Artificial Intelligence 5.3.4, Knowledge Base 5.3.5, Knowledge Graph 5.3.6,
Natural Language Processing 5.3.7, and Secure Knowledge Management 5.3.8. In addition, papers
were found related to Machine Learning [76], Big Data [77], Wiki [78], Decision Support System [79],
IT platform analytics [80], KMS[81], Simulation [82], Learning Management Expert System [83] and
Mobile Technologies [84].
The main categories for which results were found were the field of study, techniques or models for
knowledge explanation and representation, and technological tools. A reference to an organizational
practice was also found, while no results were found related to socialization techniques and logical and
analytical processes.
271
Luciano Straccia et al. CEUR Workshop Proceedings 264–279
5.3.3. Ontologies
An implementation of ontologies based on knowledge management is presented by Villamar Gomez
[85] who describes a system for service robots that combines ontological knowledge reasoning and
human–robot interaction to interpret natural language commands. The robot disambiguates uncertain
requests through spoken interaction with the human before completing a task using information
from ontological knowledge to create more precise questions. The paper presents various experiences
with ontologies and especially ontologies for describing robots and uses the KnowRob framework, a
framework designed to provide knowledge to totally autonomous robots that use the standardized
description language Web Ontology Language (OWL). Wang [86] analyzed 1275 ontologies on the
Web and found that most of them were in OWL; for [87] that OWL (Web Ontology Language) is the
most widely used for ontology implementation too. OWL consists of three languages with increasing
expressivity: OWL Lite, OWL DL, and OWL Full. All three of these languages allow you to describe
classes, properties, and instances.
Spyropoulos [88] propose an Integrated Data-Driven Forensic Ontological Approach to Crime Scene
Analysis defining an ontology as "a structured framework that defines the relationships between
various entities and concepts within a particular domain" allowing for the representation of complex
relationships that are machine-readable and intuitively understandable for human operators [89] y
also proposing the use of OWL. For [77] an ontology “defines a description of concepts in a concrete
domain (classes or concepts), properties of each concept describing various features and attributes of
the concept (properties) and restrictions on properties”, meanwhile for Sathiya [90] ontology is ideal for
semantically representing knowledge by integrating and organizing it into a conceptual hierarchy; [77]
proposes a BIGOWL ontology which is the result of ontology-driven approach to support knowledge
management in Big Data.
Another case of ontology implementation can be found at [91] which proposes a framework for
identifying and prioritizing Data Analytics (DA) opportunities. This framework has 3 components: a
team of experts, DA Opportunity Knowledge Base, and prioritization tools, and proposes a collaborative
knowledge management based on ontologies.
Finally, Gao et al. [92] is the main work associated with ontologies reviewing existing ontology-based
KM tools that can support knowledge-sharing activities to provide helpful information for future
research directions and presenting ontology-based knowledge systems [93, 94, 95, 96], repository [97]
and sharing portal [98] and propose the concept of an ideal ontology-based knowledge management
tools component and function.
5.3.4. Artificial Intelligence
Balaram [99] proposes a knowledge management architecture with artificial intelligence with 3 axes:
management, users, and application. The management axis contains the sources to capture or ac-
quire knowledge and the means of organization, the user’s axis proposes strategies of socialization
(share/learn) and knowledge transfer and finally, the application axis defines where to apply knowledge
concerning sales, manufacture, and education. It mentions the use of artificial intelligence to help the
knowledge management architecture, but it is not clear how it integrates it and what are the tools that
should be used.
Some works present the relationship between Artificial Intelligence and Knowledge Management.
Taherdoost et al. [100] examine the approaches in light of the literature that is currently accessible
on AI and KM, focusing on articles that address practical applications and the research background.
Liu and Li [101] show the Progress of Business Analytics and Knowledge Management for Enterprise
Performance Using Artificial Intelligence and Man-Machine Coordination; and Li et al. [102] explore the
application potential of HCI (human-computer interaction) technology under AI (artificial intelligence)
in enterprise performance evaluation and the influence of abusive management and self-efficacy on
enterprise performance.
272
Luciano Straccia et al. CEUR Workshop Proceedings 264–279
5.3.5. Knowledge Base
Related to Knowledge Base, Park et al. [91] propose a framework for identifying and prioritizing
Data Analytics (DA) opportunities including a component called DA Opportunity Knowledge Base,
meanwhile [103] proposes a complex knowledge base question answering (C-KBQA) framework for
intelligent bridge management based on multi-task learning (MTL) and cross-task constraints.
5.3.6. Knowledge Graph
Knowledge Graph is a concept proposed by Google in 2012. Knowledge graphs (KGs) organize data from
multiple sources, capture information about entities of interest in a given domain or task (like people,
places or events), and forge connections between them [104]. There are two types of knowledge graphs:
general and vertical domain [105]. A novel marine science domain-based knowledge graph framework
is presented in [106]. Ortiz Vivar et al. [107] propose a framework for academic knowledge management
and research networking, which introduces a new perspective of integration combining information
from multiple sources into a consolidated knowledge base and using knowledge graph-powered and
Deng et al. [108] propose use of knowledge graph in Supply Chain Management.
5.3.7. Natural Language Processing
As mentioned in the ontologies section, Khadir et al. [87] describe a system for service robots that
combines ontological knowledge reasoning and human-robot interaction to interpret natural language
commands and successfully perform household chores. Arnarsson et al. [109] demonstrate a method us-
ing Natural Language Processing and document clustering algorithms to find structurally or contextually
related documents from databases containing Engineering Change Request documents.
5.3.8. Secure Knowledge Management
Sahay [110] recognizes Secure Knowledge Management (SKM) as the science of security in the collection,
organizing, and dissemination of knowledge; although the work does not address details associated with
the theoretical bases of KM and the reference to IA prevails, it is relevant to consider this field of study
to consider in the implementation of technology for knowledge management. Samtani et al. [111] says
that "there are several key areas of cybersecurity and SKM that could be significantly enhanced through
the development of novel AI-enabled analytics techniques" and include Cyber Threat Intelligence,
Disinformation and Computational Propaganda, Security Operations Centers, and Adversarial Machine
Learning to Robustify Cyber-Defenses.
6. Conclusions
The results obtained allow observing the relevance of knowledge management for innovation in
organizations and artificial intelligence and ontologies as a fundamental part of the application of
knowledge management.
It is proposed for future work to investigate the relationship between artificial intelligence and
knowledge management and to propose points of integration between both fields of study; it is also
expected to revalue the use of ontologies and to propose some ideas for their implementation, as well
as to propose specific ontologies for the fields of action of the research group.
This work is relevant to the activities of the researchers as it allows the survey of the most important
current issues present in the bibliography of the highest academic level. It is also important to highlight
the absence of aspects of software architecture and software integration that account for the complex
integration between the different technological elements. In future works, it is proposed to analyze and
propose alternatives associated with the integration of components.
273
Luciano Straccia et al. CEUR Workshop Proceedings 264–279
References
[1] V. A. Pérez, M. F. Urbáez, Modelos teóricos de gestión del conocimiento: descriptores, con-
ceptualizaciones y enfoques, Entreciencias: diálogos en la Sociedad del Conocimiento 4 (2016)
201–227.
[2] G. Gelaf, Abordajes creativos en situaciones de crisis organizacionales, Contaduría General de la
Nación. Tucumán, Argentina (2010).
[3] A. Sánchez-Sánchez, O. Valés-Ambrosio, C. García-Lirios, M. Amemiya-Ramirez, Confiabilidad
y validez de un instrumento que mide la gestión del conocimiento, Espacios en blanco. Serie
indagaciones 30 (2020) 1–10.
[4] A. C. Santos, Modelo integrado de gestión humana y del conocimiento: una tecnología de
aplicación, Revista Venezolana de Gerencia 17 (2012) 86–98.
[5] P. Gómez, M. Sánchez, H. Florez, J. Villalobos, Co-creation of models and metamodels for
enterprise architecture projects, in: Proceedings of the 2012 Extreme Modeling Workshop, 2012,
pp. 21–26.
[6] T. H. Davenport, Working knowledge: How organizations manage what they know, NewYork
Harvard Business School (1998).
[7] IEEE, Ieee standard glossary of software engineering terminology, 1990.
[8] S. S. Alhir, Understanding the model driven architecture (mda), Methods & Tools 11 (2003) 17–24.
[9] H. Florez, M. Sánchez, J. Villalobos, iarchimate: a tool for managing imperfection in enterprise
models, in: 2014 IEEE 18th international enterprise distributed object computing conference
workshops and demonstrations, IEEE, 2014, pp. 201–210.
[10] G. Servin, C. De Brún, Abc of knowledge management, NHS National Library for Health:
Specialist Library 20 (2005) 1–68.
[11] F. Corrêa, F. Ziviani, D. B. F. Carvalho, A gestão do conhecimento holística: análise de aderência
do modelo de rojas, bermudez e morales (2013), Revista Cubana de Información en Ciencias de la
Salud 30 (2019) 2.
[12] D. Rubier Valdés, La incidencia de la gestión del conocimiento en el éxito de las organizaciones,
Cooperativismo y desarrollo 7 (2019) 392–405.
[13] M. Gómez-Vargas, M. García Alsina, Factores influyentes de la gestión del conocimiento en el
contexto de la investigación universitaria, Información, cultura y sociedad (2015) 29–46.
[14] N. Milton, The 4 legs on the knowledge management table, Recuperado: http://www. nickmilton.
com/2014/10/the-4-legs-on-knowledge-management-table. html (2014).
[15] H. Florez, , M. Sánchez, J. Villalobos, Embracing imperfection in enterprise architecture models.,
CEUR Workshop Proceedings 1023 (2013) 8–17.
[16] L. E. Zapata Cantú, Los determinantes de la generación y la transferencia del conocimiento en
pequeñas y medianas empresas del sector de las tecnologías de la información de Barcelona,
Universitat Autònoma de Barcelona„ 2005.
[17] I. Cahyadi, Factors influencing knowledge transfer in ERP system implementation within indone-
sian small and medium enterprises, Ph.D. thesis, Victoria University, 2016.
[18] L. Straccia, M. F. Pollo-Cattaneo, A. Maulini, Knowledge management model: A process view,
in: International Conference on Computational Science and Its Applications, Springer, 2023, pp.
599–616.
[19] L. Straccia, A. Maulini, M. G. Bongiorno, M. Giorda, M. F. P. Cattaneo, Knowledge representation
and technologies in the latin american academic literature., in: ICAI Workshops, 2022, pp.
271–287.
[20] I. Nonaka, H. Takeuchi, The knowledge-creating company, Harvard business review 85 (2007)
162.
[21] R. Davis, H. Shrobe, P. Szolovits, What is a knowledge representation?, AI magazine 14 (1993)
17–17.
[22] Y. Avramenko, A. Kraslawski, Case based design: Applications in process engineering, volume 87,
Springer Science & Business Media, 2008.
274
Luciano Straccia et al. CEUR Workshop Proceedings 264–279
[23] J. M. T. Tayabas, F. A. Galicia, Prácticas organizacionales y el compromiso de los trabajadores
hacia la organización, Enseñanza e investigación en Psicología 10 (2005) 295–309.
[24] L. Straccia, M. F. Pollo-Cattáneo, M. Giorda, M. G. Bongiorno, A. Maulini, Architecture on
knowledge management systems: Its presence in the academic literature, in: International
Conference on Applied Informatics, Springer, 2022, pp. 411–423.
[25] J. Bailey, D. Budgen, M. Turner, B. Kitchenham, P. Brereton, S. Linkman, Evidence relating
to object-oriented software design: A survey, in: First International Symposium on Empirical
Software Engineering and Measurement (ESEM 2007), IEEE, 2007, pp. 482–484.
[26] K. Petersen, S. Vakkalanka, L. Kuzniarz, Guidelines for conducting systematic mapping studies
in software engineering: An update, Information and software technology 64 (2015) 1–18.
[27] N. Mays, E. Roberts, J. Popay, Synthesising research evidence, in: Studying the organisation and
delivery of health services, Routledge, 2004, pp. 200–232.
[28] Universidade da Coruña, Dblp, 2024. https://www.udc.es/es/biblioteca.fic/recursos_informacion/
bases_de_datos-00001/dblp.
[29] Universidad de Cuenca, Biblioguías, 2024. https://biblioguias.ucuenca.edu.ec/sp/subjects/
databases.php?letter=bysub&subject_id=11.
[30] Universidad de Buenos Aires, Facultad de filosofía y letras, 2024. http://iice.institutos.filo.uba.ar/
scimago.
[31] H. Sampieri, et al., R. hernández sampieri, c. fernández collado, p. baptista lucio, Metodología de
la investigación 6 (2014).
[32] A. Strauss, J. Corbin, Basics of qualitative research techniques, 1998.
[33] P. R. Martínez, La teoría fundamentada: un plan metodológico para respetar la naturaleza del
mundo empírico, Praxis Sociológica 12 (2008) 137–172.
[34] L. Straccia, M. Pollo-Cattáneo, Open data, 2024. https://docs.google.com/spreadsheets/d/
1QU5rPKgER-1RcklZd76zpJkflJn2BWV9.
[35] C. Gupta, J. M. Fernandez-Crehuet, V. Gupta, Measuring impact of cloud computing and knowl-
edge management in software development and innovation, Systems 10 (2022) 151.
[36] B. M. Napoleão, É. F. de Souza, G. A. Ruiz, K. R. Felizardo, G. V. Meinerz, N. L. Vijaykumar,
Synthesizing researches on knowledge management and agile software development using the
meta-ethnography method, Journal of Systems and Software 178 (2021) 110973.
[37] O. Okudan, C. Budayan, I. Dikmen, A knowledge-based risk management tool for construction
projects using case-based reasoning, Expert Systems with Applications 173 (2021) 114776.
[38] J. Israilidis, K. Odusanya, M. U. Mazhar, Exploring knowledge management perspectives in
smart city research: A review and future research agenda, International Journal of Information
Management 56 (2021) 101989.
[39] J. Wang, Y. Wang, O. Liu, W. K. Chong, Unleashing continuous improvement and competitive
advantage through bp-driven knowledge management processes, Journal of Global Information
Management (JGIM) 31 (2023) 1–19.
[40] M. Anshari, M. Syafrudin, N. L. Fitriyani, Fourth industrial revolution between knowledge
management and digital humanities, Information 13 (2022) 292.
[41] M. F. Manesh, M. M. Pellegrini, G. Marzi, M. Dabic, Knowledge management in the fourth
industrial revolution: Mapping the literature and scoping future avenues, IEEE Transactions on
Engineering Management 68 (2020) 289–300.
[42] M. Zanker, V. Bureš, Knowledge management as a domain, system dynamics as a methodology,
Systems 10 (2022) 82.
[43] D. Mishra, B. Mahanty, A study of software development project cost, schedule and quality by
outsourcing to low cost destination, Journal of Enterprise Information Management 29 (2016)
454–478.
[44] D. Mishra, B. Mahanty, Study of maintenance project manpower dynamics in indian software
outsourcing industry, Journal of Global Operations and Strategic Sourcing 12 (2019) 62–81.
[45] M. Jafari, R. Hesamamiri, J. Sadjadi, A. Bourouni, Assessing the dynamic behavior of online q&a
knowledge markets: A system dynamics approach, Program 46 (2012) 341–360.
275
Luciano Straccia et al. CEUR Workshop Proceedings 264–279
[46] J.-Y. Lai, J. Wang, K. R. Ulhas, C.-H. Chang, Aligning strategy with knowledge management
system for improving innovation and business performance, Technology Analysis & Strategic
Management 34 (2022) 474–487.
[47] J. A. Romero-Hidalgo, P. C. Isiordia-Lachica, A. Valenzuela, R. A. Rodríguez-Carvajal, Knowledge
and innovation management model in the organizational environment, Information 12 (2021)
225.
[48] J. Darroch, R. McNaughton, Examining the link between knowledge management practices and
types of innovation, Journal of intellectual capital 3 (2002) 210–222.
[49] L. An, A. Chua, M. Islamm, Knowledge management and innovation, 2022.
[50] Z. Bao, C. Wang, A multi-agent knowledge integration process for enterprise management
innovation from the perspective of neural network, Information Processing & Management 59
(2022) 102873.
[51] O. T. Erena, M. M. Kalko, S. A. Debele, Organizational factors, knowledge management and
innovation: empirical evidence from medium-and large-scale manufacturing firms in ethiopia,
Journal of Knowledge Management 27 (2022) 1165–1207.
[52] K. Trivedi, K. B. Srivastava, The role of knowledge management processes in leveraging competi-
tive strategies to achieve firm innovativeness, The Bottom Line 35 (2022) 53–72.
[53] W. Zhang, Y. Jiang, W. Zhang, Capabilities for collaborative innovation of technological alliance:
A knowledge-based view, IEEE Transactions on Engineering Management 68 (2019) 1734–1744.
[54] Z. Yao, Z. Yang, G. J. Fisher, C. Ma, E. E. Fang, Knowledge complementarity, knowledge absorption
effectiveness, and new product performance: The exploration of international joint ventures in
china, International Business Review 22 (2013) 216–227.
[55] G. M.-d. Castro, M. Delgado-Verde, J. Amores-Salvadó, J. E. Navas-López, Linking human,
technological, and relational assets to technological innovation: exploring a new approach,
Knowledge Management Research & Practice 11 (2013) 123–132.
[56] J. Sofiyabadi, C. Valmohammadi, et al., Impact of knowledge management practices on innovation
performance, IEEE Transactions on Engineering Management 69 (2020) 3225–3239.
[57] R. B. Kline, Principles and practice of structural equation modeling, Guilford publications, 2023.
[58] X. Quan, H. Xiao, Q. Ji, J. Zhang, Can innovative knowledge management platforms lead to
corporate innovation? evidence from academician workstations in china, Journal of Knowledge
Management 25 (2021) 117–135.
[59] K. M. Law, A. K. Lau, A. W. Ip, The impacts of knowledge management practices on innovation
activities in high-and low-tech firms, Journal of Global Information Management (JGIM) 29
(2021) 1–25.
[60] J. Zhao, Knowledge management capability and technology uncertainty: driving factors of dual
innovation, Technology Analysis & Strategic Management 33 (2021) 783–796.
[61] A. L. Dias, M. F. M. Gomes, L. Pereira, R. L. Costa, Local knowledge management and innovation
spillover: exploring tourism entrepreneurship potential, International Journal of Service Science,
Management, Engineering, and Technology (IJSSMET) 13 (2022) 1–14.
[62] P. Shrivastava, J. J. Kennelly, Sustainability and place-based enterprise, Organization & environ-
ment 26 (2013) 83–101.
[63] Á. Dias, G. M. Silva, M. Patuleia, M. R. González-Rodríguez, Transforming local knowledge into
lifestyle entrepreneur’s innovativeness: Exploring the linear and quadratic relationships, Current
Issues in Tourism 24 (2021) 3222–3238.
[64] H. Hoarau, Knowledge acquisition and assimilation in tourism-innovation processes, Scandina-
vian Journal of Hospitality and Tourism 14 (2014) 135–151.
[65] M. Gloet, D. Samson, Knowledge management to support supply chain sustainability and collab-
oration practices, 2019.
[66] M. Gloet, D. Samson, Knowledge and innovation management: Developing dynamic capabilities
to capture value from innovation, in: 2016 49th Hawaii International Conference on System
Sciences (HICSS), IEEE, 2016, pp. 4282–4291.
[67] M. Gloet, D. Samson, Knowledge and innovation management to support supply chain innovation
276
Luciano Straccia et al. CEUR Workshop Proceedings 264–279
and sustainability practices, Information Systems Management 39 (2022) 3–18.
[68] A. Chaabane, A. Ramudhin, M. Paquet, Designing supply chains with sustainability considerations,
Production Planning & Control 22 (2011) 727–741.
[69] T. Cui, H. J. Ye, H. H. Teo, J. Li, Information technology and open innovation: A strategic
alignment perspective, Information & Management 52 (2015) 348–358.
[70] E. Durmuş-Özdemir, K. Abdukhoshimov, Exploring the mediating role of innovation in the
effect of the knowledge management process on performance, Technology Analysis & Strategic
Management 30 (2018) 596–608.
[71] Y. Sun, J. Liu, Y. Ding, Analysis of the relationship between open innovation, knowledge
management capability and dual innovation, Technology Analysis & Strategic Management 32
(2020) 15–28.
[72] J. Darroch, Knowledge management, innovation and firm performance, Journal of knowledge
management 9 (2005) 101–115.
[73] J. Hagedoorn, J. Schakenraad, The effect of strategic technology alliances on company perfor-
mance, Strategic management journal 15 (1994) 291–309.
[74] L. Argote, P. Ingram, Knowledge transfer: A basis for competitive advantage in firms, Organiza-
tional behavior and human decision processes 82 (2000) 150–169.
[75] M. Kotabe, X. Martin, H. Domoto, Gaining from vertical partnerships: knowledge transfer,
relationship duration, and supplier performance improvement in the us and japanese automotive
industries, Strategic management journal 24 (2003) 293–316.
[76] M. Anshari, M. Syafrudin, A. Tan, N. L. Fitriyani, Y. Alas, Optimisation of knowledge management
(km) with machine learning (ml) enabled, Information 14 (2023) 35.
[77] A. Benítez-Hidalgo, C. Barba-González, J. García-Nieto, P. Gutiérrez-Moncayo, M. Paneque, A. J.
Nebro, M. del Mar Roldán-García, J. F. Aldana-Montes, I. Navas-Delgado, Titan: A knowledge-
based platform for big data workflow management, Knowledge-Based Systems 232 (2021) 107489.
[78] N. Roxburgh, L. C. Stringer, A. J. Evans, T. G. Williams, B. Müller, Wikis as collaborative knowledge
management tools in socio-environmental modelling studies, Environmental Modelling &
Software 158 (2022) 105538.
[79] O. Meski, F. Belkadi, F. Laroche, M. Ritou, B. Furet, A generic knowledge management approach
towards the development of a decision support system, International journal of production
research 59 (2021) 6659–6676.
[80] F. A. Rabhi, M. Bandara, K. Lu, S. Dewan, Design of an innovative it platform for analytics
knowledge management, Future Generation Computer Systems 116 (2021) 209–219.
[81] L. Willman, M. E. Jennex, E. G. Frost, Using knowledge management to improve the effectiveness
of data fusion centers, International Journal of Knowledge Management (IJKM) 18 (2022) 1–16.
[82] T. F. Pereira, J. A. B. Montevechi, F. Leal, R. d. C. Miranda, A. P. G. Scheidegger, Application of a
management and storage system for knowledge generated from simulation projects as a teaching
and assessment tool, Simulation 97 (2021) 795–808.
[83] S. Sridharan, D. Saravanan, A. K. Srinivasan, B. Murugan, Adaptive learning management expert
system with evolving knowledge base and enhanced learnability, Education and Information
Technologies 26 (2021) 5895–5916.
[84] J. Fletcher-Brown, D. Carter, V. Pereira, R. Chandwani, Mobile technology to give a resource-
based knowledge management advantage to community health nurses in an emerging economies
context, Journal of Knowledge Management 25 (2021) 525–544.
[85] L. V. Gómez, J. Miura, Ontology-based knowledge management with verbal interaction for
command interpretation and execution by home service robots, Robotics and Autonomous
Systems 140 (2021) 103763.
[86] T. D. Wang, B. Parsia, J. Hendler, A survey of the web ontology landscape, in: International
Semantic Web Conference, Springer, 2006, pp. 682–694.
[87] A. C. Khadir, H. Aliane, A. Guessoum, Ontology learning: Grand tour and challenges, Computer
Science Review 39 (2021) 100339.
[88] A. Z. Spyropoulos, C. Bratsas, G. C. Makris, E. Garoufallou, V. Tsiantos, Interoperability-enhanced
277
Luciano Straccia et al. CEUR Workshop Proceedings 264–279
knowledge management in law enforcement: An integrated data-driven forensic ontological
approach to crime scene analysis, Information 14 (2023) 607.
[89] A. Spyropoulos, N. Kissoudi, A. Samalis, G. Makris, Representation in the semantic web of the
structure and functions of a police department in greece, 2020.
[90] B. Sathiya, T. Geetha, Automatic ontology learning from multiple knowledge sources of text,
International Journal of Intelligent Information Technologies (IJIIT) 14 (2018) 1–21.
[91] H. Park, H. Ko, Y.-t. T. Lee, S. Feng, P. Witherell, H. Cho, Collaborative knowledge manage-
ment to identify data analytics opportunities in additive manufacturing, Journal of Intelligent
Manufacturing (2023) 1–24.
[92] T. Gao, Y. Chai, Y. Liu, A review of knowledge management about theoretical conception and
designing approaches, International Journal of Crowd Science 2 (2018) 42–51.
[93] M. Jelokhani-Niaraki, Knowledge sharing in web-based collaborative multicriteria spatial decision
analysis: An ontology-based multi-agent approach, Computers, Environment and Urban Systems
72 (2018) 104–123.
[94] G. Bai, Y. Guo, Activity theory ontology for knowledge sharing in e-health, in: 2010 International
Forum on Information Technology and Applications, volume 1, IEEE, 2010, pp. 39–43.
[95] G. Zhao, Z. Luo, H. He, Y. Li, J. Xia, H. Zan, Research and realization of ontology-based tujia
brocade knowledge base system, in: 2017 3rd IEEE International Conference on Computer and
Communications (ICCC), IEEE, 2017, pp. 2569–2573.
[96] D. Yoo, S. No, Ontology-based economics knowledge sharing system, Expert Systems with
Applications 41 (2014) 1331–1341.
[97] R. Costa, C. Lima, J. Sarraipa, R. Jardim-Gonçalves, Facilitating knowledge sharing and reuse in
building and construction domain: an ontology-based approach, Journal of Intelligent Manufac-
turing 27 (2016) 263–282.
[98] S. Vasanthapriyan, J. Tian, D. Zhao, S. Xiong, J. Xiang, An ontology-based knowledge sharing
portal for software testing, in: 2017 IEEE international conference on software quality, reliability
and security companion (QRS-C), IEEE, 2017, pp. 472–479.
[99] A. Balaram, K. N. Kannan, L. Čepová, K. Kumar M, S. Rani B, V. Schindlerova, Artificial intelligence
for media ecological integration and knowledge management, Systems 11 (2023) 222.
[100] H. Taherdoost, M. Madanchian, Artificial intelligence and knowledge management: Impacts,
benefits, and implementation, Computers 12 (2023) 72.
[101] Q. Liu, J. Li, The progress of business analytics and knowledge management for enterprise
performance using artificial intelligence and man-machine coordination, Journal of Global
Information Management (JGIM) 30 (2022) 1–21.
[102] M. Li, X. Shang, N. Liu, X. Pan, F. Han, Knowledge management in relationship among abusive
management, self-efficacy, and corporate performance under artificial intelligence, Journal of
Global Information Management (JGIM) 30 (2022) 1–26.
[103] X. Yang, J. Yang, R. Li, H. Li, H. Zhang, Y. Zhang, Complex knowledge base question answering for
intelligent bridge management based on multi-task learning and cross-task constraints, Entropy
24 (2022) 1805.
[104] The Alan Turing Institute, Knowledge graph, 2024. https://www.turing.ac.uk/research/
interest-groups/knowledge-graphs.
[105] J. Pujara, H. Miao, L. Getoor, W. Cohen, Knowledge graph identification, in: The Semantic
Web–ISWC 2013: 12th International Semantic Web Conference, Sydney, NSW, Australia, October
21-25, 2013, Proceedings, Part I 12, Springer, 2013, pp. 542–557.
[106] J. Wu, Z. Wei, D. Jia, X. Dou, H. Tang, N. Li, Constructing marine expert management knowledge
graph based on trellisnet-crf, PeerJ Computer Science 8 (2022) e1083.
[107] J. Ortiz Vivar, J. Segarra, B. Villazón-Terrazas, V. Saquicela, Redi: Towards knowledge graph-
powered scholarly information management and research networking, Journal of Information
Science 48 (2022) 167–181.
[108] J. Deng, C. Chen, X. Huang, W. Chen, L. Cheng, Research on the construction of event logic
knowledge graph of supply chain management, Advanced Engineering Informatics 56 (2023)
278
Luciano Straccia et al. CEUR Workshop Proceedings 264–279
101921.
[109] I. Ö. Arnarsson, O. Frost, E. Gustavsson, M. Jirstrand, J. Malmqvist, Natural language processing
methods for knowledge management—applying document clustering for fast search and grouping
of engineering documents, Concurrent Engineering 29 (2021) 142–152.
[110] S. K. Sahay, N. Goel, M. Jadliwala, S. Upadhyaya, Advances in secure knowledge management in
the artificial intelligence era, Information Systems Frontiers 23 (2021) 807–810.
[111] S. Samtani, Z. Zhao, R. Krishnan, Secure knowledge management and cybersecurity in the era of
artificial intelligence, Information Systems Frontiers 25 (2023) 425–429.
279