|
on Knowledge Management and Knowledge Economy |
|
Issue of 2022‒10‒03
four papers chosen by Laura Nicola-Gavrila Centrul European de Studii Manageriale în Administrarea Afacerilor |
| By: | Suela Maxhelaku (University of Tirana); Elda Xhumari (University of Tirana); Endrit Xhina (University of Tirana) |
| Abstract: | The use of knowledge graphs in health care has increased the performance in which information is retrieved and patients' diagnoses are made. Because the healthcare domain is made up of complex concepts and many relationships between them, developing knowledge graphs presents numerous challenges. Another issue is the large number of medical standards, such as HL7, ICD-10, SNOMED CT, DICOM, LOINC, and so on. In this paper are analyzed the technologies used in the construction of knowledge graphs in healthcare. In addition, a model for generating knowledge graphs from the APHRO (Albanian Patient Healthcare Records) Ontology is proposed. |
| Keywords: | Knowledge graphs, Ontology, Healthcare |
| JEL: | C45 I19 |
| Date: | 2022–07 |
| URL: | http://d.repec.org/n?u=RePEc:sek:iefpro:13615633&r= |
| By: | Makoza, Frank |
| Abstract: | African countries are considering digital economy strategies to enhance their competitiveness, create economic value and improve the well-being of their citizens. This paper analysed the Malawi digital economy strategy (2021-2026) against the Digital Economy Ecosystem Framework. The findings showed consistency in elements for macro policy, digital foundations and digital adoption/Transformation; and core, services and solutions respectively. Nonetheless, some of the elements in the Malawi digital economy strategy were missing details and required attention. These include digital taxation, data sector, digital services laws and regulations and cyber security. Lack of details in these elements in the Malawi digital economy strategy can affect the planned activities and outcomes of the strategy. The study contributes towards an understanding of digital economy strategies in the context of developing countries. |
| Keywords: | Digital economy,Digital economy strategy,Digital Economy Kit,Digital technologies |
| Date: | 2022 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:esprep:264273&r= |
| By: | Elda Xhumari (University of Tirana, Faculty of Natural Sciences, Department of Informatics); Suela Maxhelaku (University of Tirana, Faculty of Natural Sciences, Department of Informatics); Endrit Xhina (University of Tirana, Faculty of Natural Sciences, Department of Informatics) |
| Abstract: | Many learning activities include working with graph data, which offers a wealth of relational information between parts. Modeling physical systems, learning molecular fingerprints, predicting protein interfaces, and diagnosing illnesses all need the use of a model that can learn from graph inputs. In other fields, such as learning from non-structural data such as texts and images, reasoning on extracted structures (such as phrase dependency trees and image scene graphs) is a major topic that requires graph reasoning models. Graph neural networks (GNNs) are neural models that use message transmission between graph nodes to represent graph dependency. Variants of GNNs have recently showed ground-breaking performance on a variety of deep learning tasks. This paper represents a review of the literature on Knowledge Graphs and Graph Neural Networks, with a particular focus on Graph Embeddings and Graph Neural Networks applications as a powerful tool for organizing structured data and making sense of unstructured data, which can be applied to a variety of real-world problems. |
| Keywords: | Knowledge Graph, Graph Neural Network, DeepWalk, Node2Vec, Structural Deep Network Embedding |
| JEL: | C45 |
| Date: | 2022–07 |
| URL: | http://d.repec.org/n?u=RePEc:sek:iefpro:13615626&r= |
| By: | Leogrande, Angelo; Costantiello, Alberto; Laureti, Lucio |
| Abstract: | In this article we investigate the determinants of “New Doctorate Graduates” in Europe. We use data from the EIS-European Innovation Scoreboard of the European Commission for 36 countries in the period 2010-2019 with Pooled OLS, Dynamic Panel, WLS, Panel Data with Fixed Effects and Panel Data with Random Effects. We found that “New Doctorate Graduates” is positively associated, among others, with “Human Resources” and “Government Procurement of Advanced Technology Products” and negatively, associated among others, with “Total Entrepreneurial Activity” and “Innovation Index”. We apply a clusterization with k-Means algorithm either with the Silhouette Coefficient either with the Elbow Method and we found that in both cases the optimal number of clusters is three. Furthermore, we use the Network Analysis with the Distance of Manhattan, and we find the presence of seven network structures. Finally, we propose a confrontation among ten machine learning algorithms to predict the value of “New Doctorate Graduates” either with Original Data-OD either with Augmented Data-AD. Results show that SGD-Stochastic Gradient Descendent is the best predictor for OD while Linear Regression performs better for AD. |
| Keywords: | Innovation, and Invention: Processes and Incentives; Management of Technological Innovation and R&D; Diffusion Processes; Open Innovation. |
| JEL: | O3 O30 O32 |
| Date: | 2022–09–06 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:114452&r= |