Faculty of Computer Science and Engineering

Permanent URI for this communityhttps://repository.ukim.mk/handle/20.500.12188/5

The Faculty of Computer Science and Engineering (FCSE) within UKIM is the largest and most prestigious faculty in the field of computer science and technologies in Macedonia, and among the largest faculties in that field in the region. The FCSE teaching staff consists of 50 professors and 30 associates. These include many “best in field” personnel, such as the most referenced scientists in Macedonia and the most influential professors in the ICT industry in the Republic of Macedonia.

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    MetriKG: Profiling Static and Evolving Knowledge Graphs
    (ACM, 2026-05-28)
    Günes, Hasan H.
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    Hose, Katja
    Knowledge graphs (KGs) are a foundational technology for representing and integrating information across heterogeneous domains. As some KGs evolve, understanding how their structural and semantic properties change over time is crucial for ensuring quality, consistency, and interpretability. Existing methods for KG evaluation often focus on static graphs or analyze evolution solely at the data level, leaving schema-level dynamics underexplored. To address this gap, we introduce MetriKG, a web-based application that computes a comprehensive set of metrics for both static and evolving KGs. MetriKG enables users to evaluate KGs provided as RDF files or through SPARQL endpoints, allowing for multi-dimensional analysis of aspects such as cohesion, connectivity, and inheritance depth. By supporting metric computation at both data and schema levels, MetriKG allows for systematic profiling, classification, and temporal monitoring of KGs. MetriKG is open-source and publicly available.
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    MetriKG: Profiling Static and Evolving Knowledge Graphs
    (ACM, 2026-05-28)
    Günes, Hasan H.
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    Hose, Katja
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    VulnerSec: A Flexible, Automated and Open-Source Cybersecurity Framework
    (Faculty of Computer Science and Engineering, 2025)
    Krajchevska, Evgenija
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    Ethical Dimensions of Using AI in Education
    (IATED, 2026-03)
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    RDFGraphGen: An RDF Graph Generator Based on SHACL Shapes
    (Springer Nature (Singapore), 2026-04-01)
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    Vecovska, Marija
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    Jakubowski, Maxime
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    Hose, Katja
    Developing and testing modern RDF-based applications often requires access to RDF datasets with certain characteristics. Unfortunately, it is very difficult to publicly find domain-specific knowledge graphs that conform to a particular set of characteristics. Hence, in this paper we propose RDFGraphGen, an open-source RDF graph generator that uses characteristics provided in the form of SHACL (Shapes Constraint Language) shapes to generate synthetic RDF graphs. RDFGraphGen is domain-agnostic, with configurable graph structure, value constraints, and distributions. It also comes with a number of predefined values for popular schema.org classes and properties, for more realistic graphs. Our results show that RDFGraphGen is scalable and can generate small, medium, and large RDF graphs in any domain.
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    RDFGraphGen: An RDF Graph Generator Based on SHACL Shapes
    (Springer Nature (Singapore), 2026-04-01)
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    Vecovska, Marija
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    Jakubowski, Maxime
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    Hose, Katja
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    Federated Execution of Scientific Workflows
    (IEEE, 2025-11-25)
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