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ISWC 2025 Companion Volume, Nara, Japan

Attributes, Taxonomies and Semantic alignment for Automated Research Software Classification

Research software (RS) plays a critical role in computational science, yet remains poorly categorized and difficult to discover or reuse. This research explores RS classification by investigating how textual and metadata attributes can be leveraged to develop scalable, interpretable classification methodologies. Existing taxonomies are evaluated through alignment with scientific knowledge graphs to identify redundancies and structural gaps. Labeled datasets are constructed by linking publications to software repositories, and RS attributes, such as README files, abstracts, and source code features are benchmarked using multiple machine learning models and embedding strategies. A methodology that integrates semantic enrichment and transformer-based models is proposed for robust RS classification. Preliminary findings highlight the informativeness of publication abstracts for classification tasks and expose limitations in current community-defined taxonomies. Open related page

Natural Scientific Language Processing at ESWC 2025

A study of the categories used in ‘Papers with Code’

An increasing number of machine learning developers share research software online to support their scientific investigations. In order to improve software findability, the scientific community has developed domain-specific taxonomies. However, are these taxonomies appropriate for software classification? This paper explores this question through a case study on Papers with Code, a popular platform where authors share their publications together with their software implementations. We define and apply a comparative framework with state-ofthe-art text similarity techniques (TF-IDF, Sentence-BERT, CLIP), and we assess the level of overlap between different software categories defined in the platform, based on the methods descriptions contained in them. Our results show significant category overlap, which may limit the effectiveness of classification algorithms. While community-defined categories provide a useful foundation, they may require refinement, such as subcategories or refined definitions, to better capture interdisciplinary methods and improve classification accuracy. Open related page

Sci-K 2025 at ISWC, CEUR Workshop Proceedings 4065

Are Scientific Annotations Consistently Represented across Science Knowledge Graphs?

Scientific Knowledge Graphs (SKGs) are increasingly used to annotate and interlink research outputs. However, little is known about how consistently they annotate the same publication. This paper presents a comparative analysis of category annotations across four major SKGs (ORKG, OpenAlex, OpenAIRE, and Papers with Code) using a manually curated gold-standard dataset of 70 AI-related papers. We examine differences in annotation coverage, granularity, and semantic alignment, highlighting frequent inconsistencies such as label mismatches, overly generic terms, and coverage gaps. Our analysis reveals that manual curation offers high-quality but sparse annotations, while automated systems achieve broader coverage at the cost of precision. This work contributes insights into the reliability of SKG metadata and outlines pathways for improving interoperability and annotation practices. Open related page

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