Stefanos Ougiaroglou

Prof. Dr. Stefanos Ougiaroglou

Department of Digital Systems, University of the Peloponnese, 23100 Kladas, Sparta, Greece
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Research Focus & Metrics

Academic Career Length

20Years (2007-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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8 August 2014 in Pattern Analysis and Applications
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Journal Article
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Stefanos OugiaroglouStefanos Ougiaroglou
Stefanos Ougiaroglou
Georgios EvangelidisGeorgios Evangelidis
Georgios Evangelidis

1 March 2018 in Neurocomputing
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Journal Article
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Stefanos OugiaroglouStefanos Ougiaroglou
Stefanos Ougiaroglou
KDKonstantinos I. Diamantaras
Konstantinos I. Diamantaras
GEGeorgios Evangelidis
Georgios Evangelidis

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Journal Article
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Charalampos BratsasCharalampos Bratsas
Charalampos Bratsas
EAEfstathios Konstantinos Anastasiadis
Efstathios Konstantinos Anastasiadis
Alexandros K. AngelidisAlexandros K. Angelidis
Alexandros K. Angelidis
LILazaros Ioannidis
Lazaros Ioannidis
Rigas KotsakisRigas Kotsakis
Rigas Kotsakis
Stefanos OugiaroglouStefanos Ougiaroglou
Stefanos Ougiaroglou
The amount of data related to cyber threats and cyber attack incidents is rapidly increasing. The extracted information can provide security analysts with useful Cyber Threat Intelligence (CTI) to enhance their decision-making. However, because the data sources are heterogeneous, there is a lack of common representation of information, rendering the analysis of CTI complicated. With this work, we aim to review ongoing research on the use of semantic web tools such as ontologies and Knowledge Graphs (KGs) within the CTI domain. Ontologies and KGs can effectively represent information in a common and structured schema, enhancing interoperability among the Security Operation Centers (SOCs) and the stakeholders on the field of cybersecurity. When fused with Machine Learning (ML) and Deep Learning (DL) algorithms, the constructed ontologies and KGs can be augmented with new information and advanced inference capabilities, facilitating the discovery of previously unknown CTI. This systematic review highlights the advancements of this field over the past and ongoing decade and provides future research directions.

5 September 2007 in Lecture Notes in Computer Science
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Book Chapter
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Stefanos OugiaroglouStefanos Ougiaroglou
Stefanos Ougiaroglou
ANAlexandros Nanopoulos
Alexandros Nanopoulos
APApostolos N. Papadopoulos
Apostolos N. Papadopoulos
YMYannis Manolopoulos
Yannis Manolopoulos
TWTatjana Welzer-Druzovec
Tatjana Welzer-Druzovec

16 September 2012
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Conference Paper
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Stefanos OugiaroglouStefanos Ougiaroglou
Stefanos Ougiaroglou
Georgios EvangelidisGeorgios Evangelidis
Georgios Evangelidis

2 May 2024 in Electronics
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Journal Article
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Aristotelis KampatzisAristotelis Kampatzis
Aristotelis Kampatzis
Antonis SidiropoulosAntonis Sidiropoulos
Antonis Sidiropoulos
Konstantinos DiamantarasKonstantinos Diamantaras
Konstantinos Diamantaras
Stefanos OugiaroglouStefanos Ougiaroglou
Stefanos Ougiaroglou
Sentiment Analysis in text, especially text containing scientific citations, is an emerging research field with important applications in the research community. This review explores the field of sentiment analysis by focusing on the interpretation of citations, presenting a detailed description of techniques and methods ranging from lexicon-based approaches to Machine and Deep Learning models. The importance of understanding both the emotion and the intention behind citations is emphasized, reflecting their critical role in scientific communication. In addition, this study presents the challenges faced by researchers (such as complex scientific terminology, multilingualism, and the abstract nature of scientific discourse), highlighting the need for specialized language processing techniques. Finally, future research directions include improving the quality of datasets as well as exploring architectures and models to improve the accuracy of sentiment detection.