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Title:Evolucija prevladujočih paradigem in terminologije na področju raziskav sladkorne bolezni skozi prizmo teorije kompleksnih mrež : magistrsko delo
Authors:ID Benkovič, Luka (Author)
ID Markovič, Rene (Mentor) More about this mentor... New window
ID Gosak, Marko (Co-mentor)
Files:.pdf MAG_Benkovic_Luka_2022.pdf (2,63 MB)
MD5: 4B4771D5FBD0EE83BCC0F15B404D7D65
PID: 20.500.12556/dkum/efa5d513-5a18-4731-b0dd-9dde23846248
 
Language:Slovenian
Work type:Master's thesis/paper
Typology:2.09 - Master's Thesis
Organization:FNM - Faculty of Natural Sciences and Mathematics
Abstract:Znanstveno-raziskovalna dejavnost v zadnjih desetletjih doživlja velik razmah, kar se med drugim odraža v naraščajočem številu znanstvenih in strokovnih del. Tovrstne objave so ključne za prenos in razvoj znanja, a črpanje uporabnega znanja iz vedno večjih zbirk znanstvenih del postaja vse težje. Vsak posameznik je namreč sposoben obdelati le omejeno količino informacij. Kaže se potreba po tem, da se razvijejo orodja, ki nam bodo iz velike količine podatkov pomagala pri luščenju in agregaciji uporabnih informacij. V magistrski nalogi se te problematike lotimo s podatkovnim rudarjenjem po podatkovni bazi PubMed in z orodij s področja teorije kompleksnih mrež. Kot primer obravnavamo raziskovalno področje sladkorne bolezni, ki zaradi svoje razširjenosti predstavlja pereč problem za sodobno družbo. V prvem delu naloge predstavimo način pridobivanja in obdelave podatkov ter njihove osnovne značilnosti. V nadaljevanju se osredinimo na vsebino raziskovalnih del, ki jo proučujemo na podlagi ključnih besed. Tukaj delujemo ob predpostavki, da so ključne besede dober odraz vsebine pripadajočega dela in lahko iz njih izpeljemo prevladujoče paradigme na področju. Najprej pogledamo pogostost pojavljanja oziroma frekvenco posameznih besed, iz česar sklepamo o njihovi pomembnosti. Ugotovimo, da nam zgolj preprosta frekvenca ne omogoča preučevanja časovnega razvoja pomembnosti, zato uvedemo način rangiranja besed v razrede. Besede z najvišjimi rangi predstavljajo temeljne pojme raziskovalnega področja. Srednje veliki rangi predstavljajo posamezne niše področja, medtem ko najnižji rangi odražajo specifiko posamezne raziskave. Na koncu se lotimo še povezovanja ključnih besed v kompleksne mreže, iz katerih vrednotimo relacije. Iz naših rezultatov je razvidno, da je mreža ključnih besed modularna z jasno izraženimi skupnostmi, na podlagi česar lahko razberemo interdisciplinarno povezovanje. Posebno pozornost namenimo tudi časovnemu razvoju mreže. Le-ta nenehno raste, medtem ko povprečna povezanost ne narašča monotono, temveč ima krivulja jasne vrhe v nekih prelomnih točkah raziskav. To implicira nova odrkitja, ki imajo za posledico umeščanje novih ključnih besed, terminologije oziroma paradigem v obstoječo strukturo.
Keywords:sladkorna bolezen, podatkovno rudarjenje, kompleksne mreže, razvoj terminologije
Place of publishing:Maribor
Place of performance:Maribor
Publisher:[L. Benkovič]
Year of publishing:2022
Number of pages:41 f.
PID:20.500.12556/DKUM-81136 New window
UDC:616.379-008.64:53(043.2)
COBISS.SI-ID:104183299 New window
Publication date in DKUM:08.04.2022
Views:799
Downloads:80
Metadata:XML RDF-CHPDL DC-XML DC-RDF
Categories:FNM
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Licences

License:CC BY-NC-SA 4.0, Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International
Link:http://creativecommons.org/licenses/by-nc-sa/4.0/
Description:A Creative Commons license that bans commercial use and requires the user to release any modified works under this license.
Licensing start date:14.01.2022

Secondary language

Language:English
Title:Evolution of the main paradigms and terminology in diabetes research through the lens of the complex networks theory
Abstract:Over the last few decades scientific research has experienced immense growth. This phenomenon can be observed in a growing number of scientific papers published each year. Such papers play a key role in transfer and development of knowledge. However, the ability of an individual to process information is limited. So an ever expanding base of literature presents a difficulty when we are trying to find knowledge. To maintain control over useful information we need to develop tools which will allow us to aggregate scientific data. In this master's thesis we tackle the mentioned issue using data mining techniques on the PubMed database and tools provided by the complex networks theory. We limit ourselves to Diabetes research field since Diabetes is another trending issue facing modern society. At first we present our approach to data gathering, processing and basic analysis. Then we focus on the written content of processed research papers by analysing keywords. Here our assumption is that keywords are a proper representation of a research paper and offer enough insight to derive the main paradigms of a research field. We use word frequency to infer importance of each keyword. However, we have to introduce a word ranking system based on frequency to get a proper glimpse into development of word importance through time. Keywords that are fundamental to the entire research field are attributed the highest ranks. Middle rated ranks correspond to keywords typical for a particular niche of the entire research field and lowest ranked keywords are specific to that particular research paper. Finally we use keyword co-occurrence to build complex networks and evaluate keyword relationships. Results show that the network is modular and that we are able to detect interdisciplinarity through keyword communities. Another important aspect is time evolution of the keyword network. The network expands in size through time. Despite that we observe clear peaks in the average connectivity metric at certain milestones of the Diabetes research field. These observations imply new breaktoughs in research resulting in integration of new keywords, terminology or entire paradigms into the existing network structure.
Keywords:diabetes, data mining, complex networks, terminology development


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