AI-Based Model for Knowledge Evaluation in Public Organizations

Version	Summary	Created by	Modification	Content Size	Created at	Operation
1		María Segunda García_González	--	1826	2023-10-31 12:11:12	\|
2	Reference format revised.	Lindsay Dong	+ 2 word(s)	1828	2023-11-01 02:25:20	\|

This entry is adapted from the peer-reviewed paper 10.3390/app132111796

In the construction of knowledge bases, it is very important to evaluate the quality of the knowledge entered into them. Artificial Intelligence (AI) development has led to the research of knowledge management tools for multi-user environments, among many other AI applications. In the knowledge management field, the construction of ontologies as knowledge repositories using various sources requires a means of evaluation of all: the input ontologies and the integration process on the output ontology. The results obtained from the evaluations serve as guides to measure the quality of the repository.

knowledge management artificial intelligence collective intelligence ontology

1. Introduction

Knowledge in organizations is a valuable organizational asset. Organizational Knowledge Management (KM) is the process of identifying, acquiring, evaluating, and disseminating knowledge into the organization aiming to make smart decisions. Knowledge has become the distinctive element for the competitiveness of organizations, so KM is one of the key factors in achieving organizational goals.

KM makes use of a wide repertory of procedures, techniques, and tools, including Artificial Intelligence (AI). Ontologies, which are the standard method of knowledge representation in AI, have been used consistently in KM for a variety of organizational endeavors and application domains, including education ^[1].

Various types of knowledge can be allocated in different functional units of an organizational structure. At the same time, ^[2] pointed out that knowledge integration at an organizational level enables the organization to carry out better innovation processes.

Knowledge integration implies collaboration in organizations. This requirement is hard to meet sometimes, especially when the size of the organization is small since the reduced organizational size usually conveys the limitation of resources at all human capital, material, technological, and temporal levels ^[3].

Knowledge integration is an important factor for innovation processes since collaboration makes it easier to carry out experiments ^[4], increase cohesion within collaborative teams ^[5], or generate new solutions to challenging issues ^[6].

Sustainable practices in agriculture and farming are gaining momentum worldwide. However, such practices usually involve all environmental, economic, and social parameters, which makes such practices complex. This also has an influence on the management of crops incorporating such practices so that often different crop management views can emerge that can be complementary to each other. In fact, there is a demand for integrating different views addressing crop management in farming organizations that can tackle the increasingly complex administrative processes necessary to meet the regulations to receive subventions from the EC. This has been exacerbated after the decision of the EU to align itself with the 2030 Agenda of the United Nations (UN) and, therefore, with the UN-defined Sustainable Development Goals.

2. AI-Based Model for Knowledge Evaluation in Public Organizations

Knowledge in organizations is a valuable organizational asset. Hence, it should be managed adequately. Organizational Knowledge Management (KM) can be defined as the process of identifying, acquiring, evaluating, and disseminating knowledge into the organization aiming to make smart decisions. In recent years, knowledge has become the distinctive element for the competitiveness of organizations, so KM is one of the key factors in achieving corporate goals. According to ^[7], the KM objectives in an organization are to promote growth, communication, and the preservation of knowledge.

Knowledge Management Systems (KMS) allow access, exchange, and update of organizational business knowledge. Organizations that recognize the relevance of KM usually make use of available capabilities in the organization or create new ones with the purpose of investing in new solutions demanded by the market. Overall, a good KM allows organizations to increase their effectiveness and efficiency ^[8].

KM makes use of an ample range of procedures, techniques, and tools, including Artificial Intelligence (AI). Ontologies, which can be said to be the standard method of knowledge representation in AI, have been used consistently in KM for a variety of organizational endeavors and application domains, including education ^[1].

According to some studies ^[9], different types of knowledge can be allocated in distinct functional units of an organizational structure. At the same time, in ^[2], it is pointed out that knowledge integration at the organizational level enables the organization to carry out better innovation processes. This has been claimed in recent research studies ^[10].

Knowledge integration within the context of problem-solving at the organizational level requires collaboration. This requirement is difficult to meet sometimes, especially when the size of the organization is small, since the reduced organizational size usually conveys a limitation of resources at all human capital, material, technological, and temporal levels ^[3]. However, at the same time, the need for collaboration in small corporations is high as they need to create and manage adequate knowledge for realizing innovations ^[11].

Based on another research line, it has been put forward that independently of the viability of collaboration within organizations, collaboration can be very positive for organizations ^[12]. Thus, for instance, it assists in overcoming sole restrictions related to (human) resource availability and in finding synergies amongst actors.

Organizational members may overcome collaborative impediments or obstacles by making use of knowledge sharing that allows them to benefit from complementary views on a certain issue of interest at the organizational level ^[11]. More recently, the authors in ^[13] determined that knowledge integration has a tremendous influence on companies’ performance.

In general, knowledge integration is also a key factor for innovation processes since collaboration makes it easier to carry out experiments ^[4], increase cohesion within collaborative teams ^[5], or generate new solutions to challenging issues ^[6]. However, in the case of small organizations, a few individuals must address unknown parts of the innovation process using their few resources ^[14]. Furthermore, this issue jeopardizes the finding of solutions to complex problems requiring collaboration ^[4].

From another perspective, other authors have researched possible solutions or factors to be considered to overcome such limitations. Thus, ^[15] considers that knowledge integration is affected by several factors related to psychological profiles and the possession of common resources, such as shared interests, meanings, or lexicons. Moreover, following the research results described in ^[16], experts’ collaboration in organizations minimizes the effects of their (human capital, cognitive, material) resource limitations considered at an individual scale. In other words, collaboration increases the organization’s capabilities to deal with complex issues.

To achieve adequate knowledge integration in organizations, Corporate Memory (CM) systems, which can be defined as knowledge repositories and know–how in a group of individuals who work in a firm, have played an essential role ^[17]. Within the CM system construction process, it is paramount to evaluate knowledge before including it in the CM system.

On the other hand, by placing the citizen at the center of the advancement of public administrations, it is necessary to bet on KM mechanisms that allow the design and evaluation of public policies in sectors still with low rates of digitalization, such as farming. In other words, it is necessary to analyze, design, and implement public policies by adopting a governmental–social co-production and co-authorship approach that allows “the Government of society to become Government with society” ^[18]. In this process, the function of the Public Administration, in collaboration with the Government, should become a “dynamic resource.” Institutional strength should not be confused with firmness and inability to introduce resources and processes that serve to adapt to changes. In turn, the involvement of other social agents in collaboration with public administrations would expand the capacity to develop the necessary institutional and professional skills within public services. Thus, “the State cannot be understood as a monolithic actor, but rather as an entity in which an endless number of mechanisms and interests operate in multiple logics” ^[19].

Elsewhere, it has been argued that the State is not the only institution capable of carrying out the task of proposing and implementing collective goals in an effective and efficient manner. In this sense, the State has the perfect mechanisms to carry out the decision-making process. So, it could be said that the State is the only locus available when it comes to carrying out a legitimate collective action. However, it must also be capable of delegating functions to other public structures for the development of mechanisms for citizen participation in the formulation of new goals; in other words: “in the contemporary world, responsibility for government actions can be a necessary substitute for other forms of democracy” ^[20].

According to ^[21], Collective Intelligence (CI) results from long processes and constitutes the social capacity within the framework of a territory. In this way, when speaking of an intelligent territory, it is not enough that the institutions dedicated to knowledge exist. It is necessary to generate relational dynamics that cause changes in the structures, processes, and collective rules ^[22]. The use of the CI is a two-way road. It is believed that public administrations should bet more and more on this type of method. The reason for this is that while public or private institutions incorporate the knowledge of the communities through crowdsourcing methodologies, these same institutions enrich their communities by facilitating access to the generated knowledge. This feedback has been carried out so far through different methods: in the form of open-access documents, with structured data sets for subsequent statistical exploitation by the user, or through improvements to the software on which this knowledge exchange between institutions and communities is based ^[23].

Management of knowledge on sustainable practices in agriculture and farming is gaining momentum worldwide. However, such practices usually involve all environmental, economic, and social parameters, which makes such practices complex. This also has an influence on the management of crops incorporating such practices so that often different crop management views can emerge that can be complementary to each other. In fact, from a knowledge management perspective, there is a demand for integrating different views addressing crop management in farming organizations that can tackle the increasing complexity of the administrative processes necessary to meet the regulations to receive subventions from the EC. This has been exacerbated after the decision of the EU to align itself with the 2030 Agenda of the United Nations (UN) and, therefore, with the UN-defined Sustainable Development Goals.

Ontologies play a fundamental role in CM construction and management processes. In the CI context, the conceptualization of the domain forms a conceptually coherent basis on which future extensions can be made or mapped to ontologies in other domains to enable interoperability ^[24]^[25]^[26]^[27]. To achieve this, it is necessary to carry out a fusion process involving different ontologies proposed by human experts. Such fusion must be conducted using manually created ontologies. Another approach to knowledge capture is learning ontologies ^[28]^[29]^[30] by automating their construction from texts. This often requires carrying out several processes, such as the identification of relevant terms ^[31]^[32]^[33]^[34], determination of concepts ^[35], obtaining of taxonomic relations through semantic similarity ^[36], or obtaining of partonomic relations. These processes frequently rely on natural language processing and the support of lexical resources.

AI development in recent years has led to the research of knowledge management tools for multi-user environments, among many other AI applications. In the knowledge management field, the construction of ontologies as knowledge repositories using various sources requires a means of evaluation of all: the input ontologies and the integration process on the output ontology. The results obtained from the evaluations serve as guides to measure the quality of the repository.

References

Muñoz García, A.; Lamolle, M.; Martinez-Béjar, R.; Espinal Santana, A. Learning Ecosystem Ontology with Knowledge Management as a Service. In Computational Collective Intelligence. ICCCI 2019; Nguyen, N., Chbeir, R., Exposito, E., Aniorté, P., Trawiński, B., Eds.; Lecture Notes in Computer Science; Springer: Berlin/Heidelberg, Germany, 2019; p. 11684.
Carlile, P.R. Transferring, translating, and transforming: An integrative framework for managing knowledge across boundaries. Organ. Sci. 2004, 15, 555–568.
McDowell, W.C.; Peake, W.O.; Coder, L.; Harris, M.L. Building small firm performance through intellectual capital development: Exploring innovation as the “black box”. J. Bus. Res. 2018, 88, 321–327.
Acharya, C.; Ojha, D.; Gokhale, R.; Patel, P.C. Managing information for innovation using knowledge integration capability: The role of boundary spanning objects. Int. J. Inf. Manag. 2022, 62, 102438.
Nguyen, N.P.; Ngo, L.V.; Bucic, T.; Phong, N.D. Cross-functional knowledge sharing, coordination and firm performance: The role of cross-functional competition. Ind. Mark. Manag. 2018, 71, 123–134.
Nerkar, A.; Roberts, P.W. Technological and product-market experience and the success of new product introductions in the pharmaceutical industry. Strateg. Manag. J. 2004, 25, 779–799.
Steels, L. Corporate knowledge management. In Proceedings of the International Symposium on the Management of Industrial and Corporate Knowledge ’93, Compiegne, France, 27–28 October 1993; pp. 223–235.
Anshari, M.; Syafrudin, M.; Tan, A.; Fitriyani, N.L.; Alas, Y. Optimisation of Knowledge Management (KM) with Machine Learning (ML) Enabled. Information 2023, 14, 35.
Grant, R.M. Toward a knowledge-based theory of the firm. Strateg. Manag. J. 1996, 17, 109–122.
Nicolini, D.; Mengis, J.; Swan, J. Understanding the role of objects in cross-disciplinary collaboration. Organ. Sci. 2012, 23, 612–629.
Dyer, J.H.; Singh, H. The relational view: Cooperative strategy and sources of interorganizational competitive advantage. Acad. Manag. Rev. 1998, 23, 660–679.
Carlile, P.R.; Rebentisch, E.S. Into the black box: The knowledge transformation cycle. Manag. Sci. 2003, 49, 1180–1195.
Singh, P.J.; Power, D. Innovative knowledge sharing, supply chain integration and firm performance of Australian manufacturing firms. Int. J. Prod. Res. 2014, 52, 6416–6433.
Glover, J.; Champion, D.; Daniels, K.; Boocock, G. Using capital theory to explore problem solving and innovation in small firms. J. Small Bus. Enterp. Dev. 2016, 23, 25–43.
Kogut, B. The network as knowledge: Generative rules and the emergence of structure. Strateg. Manag. J. 2000, 21, 405–425.
Carlile, P.R. A pragmatic view of knowledge and boundaries: Boundary objects in new product development. Organ. Sci. 2002, 13, 442–455.
Euzenat, J. Corporate memory through cooperative creation of knowledge bases and hyper-documents. In Proceedings of the 10th Workshop on Knowledge Acquisition (KAW), Banff, AB, Canada, 9–14 November 1996; Gaines, B., Musen, M., Eds.; pp. 1–18.
Villanueva, L.F.A. Current issues in the of public policy discipline. Opera 2019, 25, 11–25. (In Spanish)
Pardo, M. Public administration and governance. In El Instituto Nacional de Administración Pública en la Reunión Nacional de Administración; González, C.F.M., Guzmán, M.G., Jáuregui, C.M.S., Eds.; INAP: Cuajimalpa, México, 2018; pp. 29–42. (In Spanish)
Peters, B.G. Globalization, governance and the State: Some propositions about the process of governing. CLAD Reforma Y Democr. 2007, 39, 33–50. (In Spanish)
PÉREZ, M.C. A Quality Management Model for Territorial Intelligence Projects (MGCPIT): Application to the Buenavista de Apasco Case, Macuspana, Tabasco, México. Ph.D. Thesis, University of Huelva, Huelva, Spain, 2017. (In Spanish).
Innerarity, D. The governance of smart territories. Ekon. Rev. Vasca De Econ. 2010, 74, 50–65.
García-González, M.S. Collective intelligence: A new form of participation and transparency to improve the technological operation of Public Administrations? In Scenarios of Citizen Participation: A multilevel Vision; Manuel Giménez Abad Foundation for Parliamentary Studies and the Autonomous State: Zaragoza, Spain, 2022; pp. 1–36. (In Spanish)
Van Heijst, G.; Schreiber, A.T.; Wielinga, B.J. Using explicit ontologies in KBS development. Int. J. Hum. Comput. Stud. 1997, 46, 183–292.
Kalfoglou, Y.; Schorlemmer, M. Ontology Mapping: The State of the Art. Knowl. Eng. Rev. 2003, 18, 1–31.
Barbosa, L.; De Carvalho, R.B.; Choo, C.W.; Versiani, A.F. Corporate memory processes in project-based organizations: A framework for engineering design firms. In Proceedings of the International Conference on Intellectual Capital, Knowledge Man-Agement and Organisational Learning; ICICKM: Toronto, ON, Canada, 2020; pp. 45–54.
Haase, P.; Herzig, D.M.; Kozlov, A.; Nikolov, A.; Trame, J. Metaphactory: A platform for knowledge graph management. Semant. Web 2019, 10, 1109–1125.
Asim, M.N.; Wasim, M.; Khan, M.U.G.; Mahmood, W.; Abbasi, H.M. A survey of ontology learning techniques and applications. Database 2018, 2018, bay101.
Shvaiko, P.; Euzenat, J. Ontology matching: State of the art and future challenges. IEEE Trans. Knowl. Data Eng. 2011, 25, 158–176.
Ding, G.; Yi, P.; Feng, X. Constructing a Knowledge Graph for the Chinese Subject Based on Collective Intelligence. Int. J. Semant. Web Inf. Syst. 2023, 19, 1–19.
Kageura, K.; Umino, B. Methods of automatic term recognition: A review. Terminology. Int. J. Theor. Appl. Issues Spec. Commun. 1996, 3, 259–289.
Sumathi, S.; Rajappa, S.; Kumar, L.A.; Paneerselvam, S. Machine Learning for Decision Sciences with Case Studies in Python; CRC Press: Boca Raton, FL, USA.
Hassanien, A.; Bhattacharyya, S.; Chakrabati, S.; Bhattacharya, A.; Dutta, S. (Eds.) Emerging Technologies in Data Mining and Information Security: Proceedings of IEMIS 2020, Volume 1; Springer: Singapore, 2021.
Jeng, Y.L.; Huang, Y.M. Dynamic learning paths framework based on collective intelligence from learners. Comput. Hum. Behav. 2019, 100, 242–251.
Navigli, R.; Velardi, P. Learning domain ontologies from document warehouses and dedicated web sites. Comput. Linguist. 2004, 30, 151–179.
Rodriguez, M.A.; Egenhofer, M.J. Determining semantic similarity among entity classes from different ontologies. IEEE Trans. Knowl. Data Eng. 2003, 15, 442–456.

© Text is available under the terms and conditions of the Creative Commons Attribution (CC BY) license; additional terms may apply. By using this site, you agree to the Terms and Conditions and Privacy Policy.

Upload a video for this entry

Information

Subjects: Computer Science, Artificial Intelligence; Computer Science, Information Systems; Public Administration

Contributors MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to https://encyclopedia.pub/register :

María S. García-González

Enrique Paniagua-Arís

Rodrigo Martínez-Béjar

Juan A. López-Caballero

Alessandro Gasparetto

View Times: 128

Update Date: 01 Nov 2023

Table of Contents

Video Upload Options

Confirm

1. Introduction

2. AI-Based Model for Knowledge Evaluation in Public Organizations

References