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Title:Empirična primerjava znanstvenih objav s semantičnimi vektorji : magistrsko delo
Authors:ID Javornik, Bojan Jan (Author)
ID Karakatič, Sašo (Mentor) More about this mentor... New window
Files:.pdf MAG_Javornik_Bojan_Jan_2025.pdf (39,90 MB)
MD5: 5389392145C660BC0FE64EC05D5ACE41
 
Language:Slovenian
Work type:Master's thesis/paper
Typology:2.09 - Master's Thesis
Organization:FERI - Faculty of Electrical Engineering and Computer Science
Abstract:V magistrski nalogi obravnavamo možnost uporabe semantičnih vektorskih reprezentacij kot orodja za primerjavo vsebine znanstvenih objav in spremljanje sprememb v raziskovalnih tematikah skozi čas. Menimo, da lahko z vektorizacijo povzetkov in merami kvantitativne razdalje zanesljivo sledimo podobnostim in razlikam v znanstveni produkciji. Posebej smo vključili tudi obdobje COVID-19, saj smo predvidevali, da bodo takratne vsebinske spremembe dober preizkus učinkovitosti metode. V raziskavi smo iz baze Scopus pridobili objave izbranih znanstvenih revij za obdobje 2017–2022, povzetke pretvorili v vektorje s prednaučenim SBERT modelom “all-MiniLM-L6-v2” ter zgradili predstavitve revij po letih. Za merjenje razdalj med vektorji smo uporabili kosinusno razdaljo, hipoteze pa preverili z Wilcoxonovim testom ter podprli rezultate z vizualizacijami in modeliranjem tematik z BERTopic. Rezultati potrjujejo, da obstajajo izrazite razlike med revijami, saj se vektorski centri različnih revij v prostoru jasno ločijo, medtem ko so objave znotraj posamezne revije bolj povezane. Prav tako smo zaznali tematske premike med zaporednimi leti iste revije, kar kaže na postopno, a zaznavno evolucijo raziskovalnih vsebin. Analiza obdobja pred in po letu 2020 je pokazala večje spremembe, vendar teh razlik ni mogoče neposredno pripisati COVID-19. Raziskava tako potrjuje, da je semantična vektorizacija skupaj s kosinusno razdaljo učinkovito orodje za empirično primerjavo vsebine znanstvenih objav.
Keywords:semantična vektorizacija, znanstvene objave, podobnost vsebine, SBERT, kosinusna razdalja
Place of publishing:Maribor
Place of performance:Maribor
Publisher:[B. J. Javornik]
Year of publishing:2025
Number of pages:1 spletni vir (1 datoteka PDF (XII, 58 str.))
PID:20.500.12556/DKUM-95743 New window
UDC:004.4'414(043.2)
COBISS.SI-ID:267099395 New window
Publication date in DKUM:22.12.2025
Views:184
Downloads:9
Metadata:XML DC-XML DC-RDF
Categories:KTFMB - FERI
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Licences

License:CC BY-NC-ND 4.0, Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
Link:http://creativecommons.org/licenses/by-nc-nd/4.0/
Description:The most restrictive Creative Commons license. This only allows people to download and share the work for no commercial gain and for no other purposes.
Licensing start date:17.10.2025

Secondary language

Language:English
Title:Empirical comparison of scientific publications with semantic vectors
Abstract:This master’s thesis explores the use of semantic vector representations to compare scientific publications and track shifts in research topics over time. We argue that vectorizing abstracts and applying quantitative distance measures can reliably capture similarities and differences in scientific production. The COVID-19 period was included as a potential test case for the method’s effectiveness. Publications from selected journals indexed in Scopus between 2017 and 2022 were collected. Abstracts were transformed into vectors using the pre-trained SBERT model “all-MiniLM-L6-v2”, and yearly journal representations were constructed. Cosine distance was applied to measure differences between vectors, while hypotheses were tested with the Wilcoxon signed-rank test. Results were further supported with visualizations and topic modeling using BERTopic. The findings confirm clear distinctions between journals, as vector centers of different journals are clearly separated, while publications within the same journal cluster more closely. Thematic shifts were also observed across consecutive years of the same journal, reflecting gradual but noticeable evolution of content. Analysis of the period before and after 2020 showed larger changes, though not directly attributable to COVID-19. Overall, the study demonstrates that semantic vectorization with cosine distance is an effective tool for empirical comparison of scientific publications.
Keywords:semantic vectorization, scientific publications, content similarity, SBERT, cosine distance


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