arXiv subjects

Serafeim Chatzopoulos

Publications and source records attributed to Serafeim Chatzopoulos.

12 recordsLinked to original sources

arXiv2026

BIP! Ranker: A Software Library for Citation-Based Impact Indicators on Large-Scale Graphs

Scientific impact is multidimensional: overall influence, current popularity, early citation momentum, and field-relative performance each capture a distinct facet of a publication's impact. Yet, in practice, these dimensions are often reduced to a single metric, such as citation count. Open solutions for computing multiple complementary impact indicators at scale remain scarce, particularly for citation graphs as large as those provided by major scholarly databases. We introduce BIP! Ranker, an open-source, Spark-based library for computing citation-based impact indicators at scale, capable of processing citation networks with billions of citations among hundreds of millions of publications.

cs.DL↗

arXiv2026

Scientific Knowledge Discovery in the Age of Large Language Models

The rapid growth of scholarly literature has made identifying relevant publications increasingly difficult, and conventional search systems still depend heavily on manually formulated queries and effortful manual inspection. Generative large language models (LLMs) offer a more flexible alternative, supporting literature retrieval and the screening of candidate studies against eligibility criteria. This chapter surveys 34 peer-reviewed papers applying generative LLMs to these two tasks, identified via a Boolean search over the OpenAIRE Graph (1,589 records screened to 34 inclusions). Reviewed studies are characterised by LLMs employed, model access and adaptation, prompting and architectural techniques, ground-truth sources, and evaluation metrics.

cs.DL↗

arXiv2026

A Template-Driven Platform for Contextualised Researcher Profiles

Modern researchers engage in diverse activities, assume multiple contribution roles, and produce a variety of outputs beyond traditional publications. This broader view of research contributions is increasingly recognised by responsible research assessment initiatives. However, existing researcher profiling platforms remain largely focused on publications and publication-centric indicators, offering limited support for contextualised and multi-dimensional representations of research careers. This paper presents BIP! Scholar, a platform that supports flexible researcher profiling through a template-driven approach. Researchers can create profiles tailored to different presentation or assessment contexts using track-based, narrative-style, or hybrid templates which support the representation of diverse outputs, contribution roles, and broader research activities. The platform also supports research assessment experts who wish to design and evaluate experimental profile templates.

cs.DL↗

arXiv2025

InsightGUIDE: An Opinionated AI Assistant for Guided Critical Reading of Scientific Literature

The proliferation of scientific literature presents an increasingly significant challenge for researchers. While Large Language Models (LLMs) offer promise, existing tools often provide verbose summaries that risk replacing, rather than assisting, the reading of the source material. This paper introduces InsightGUIDE, a novel AI-powered tool designed to function as a reading assistant, not a replacement. Our system provides concise, structured insights that act as a "map" to a paper's key elements by embedding an expert's reading methodology directly into its core AI logic. We present the system's architecture, its prompt-driven methodology, and a qualitative case study comparing its output to a general-purpose LLM. The results demonstrate that InsightGUIDE produces more structured and actionable guidance, serving as a more effective tool for the modern researcher.

cs.AI↗

arXiv2025

Accelerating Scientific Discovery with Multi-Document Summarization of Impact-Ranked Papers

The growing volume of scientific literature makes it challenging for scientists to move from a list of papers to a synthesized understanding of a topic. Because of the constant influx of new papers on a daily basis, even if a scientist identifies a promising set of papers, they still face the tedious task of individually reading through dozens of titles and abstracts to make sense of occasionally conflicting findings. To address this critical bottleneck in the research workflow, we introduce a summarization feature to BIP! Finder, a scholarly search engine that ranks literature based on distinct impact aspects like popularity and influence. Our approach enables users to generate two types of summaries from top-ranked search results: a concise summary for an instantaneous at-a-glance comprehension and a more comprehensive literature review-style summary for greater, better-organized comprehension. This ability dynamically leverages BIP! Finder's already existing impact-based ranking and filtering features to generate context-sensitive, synthesized narratives that can significantly accelerate literature discovery and comprehension.

cs.DL↗

arXiv2025

From raw affiliations to organization identifiers

Accurate affiliation matching, which links affiliation strings to standardized organization identifiers, is critical for improving research metadata quality, facilitating comprehensive bibliometric analyses, and supporting data interoperability across scholarly knowledge bases. Existing approaches fail to handle the complexity of affiliation strings that often include mentions of multiple organizations or extraneous information. In this paper, we present AffRo, a novel approach designed to address these challenges, leveraging advanced parsing and disambiguation techniques. We also introduce AffRoDB, an expert-curated dataset to systematically evaluate affiliation matching algorithms, ensuring robust benchmarking. Results demonstrate the effectiveness of AffRp in accurately identifying organizations from complex affiliation strings.

cs.DL↗

arXiv2025

Can LLMs Predict Citation Intent? An Experimental Analysis of In-context Learning and Fine-tuning on Open LLMs

This work investigates the ability of open Large Language Models (LLMs) to predict citation intent through in-context learning and fine-tuning. Unlike traditional approaches relying on domain-specific pre-trained models like SciBERT, we demonstrate that general-purpose LLMs can be adapted to this task with minimal task-specific data. We evaluate twelve model variations across five prominent open LLM families using zero-, one-, few-, and many-shot prompting. Our experimental study identifies the top-performing model and prompting parameters through extensive in-context learning experiments. We then demonstrate the significant impact of task-specific adaptation by fine-tuning this model, achieving a relative F1-score improvement of 8% on the SciCite dataset and 4.3% on the ACL-ARC dataset compared to the instruction-tuned baseline. These findings provide valuable insights for model selection and prompt engineering. Additionally, we make our end-to-end evaluation framework and models openly available for future use.

cs.CL↗

arXiv2023

BIP! NDR (NoDoiRefs): A Dataset of Citations From Papers Without DOIs in Computer Science Conferences and Workshops

In the field of Computer Science, conference and workshop papers serve as important contributions, carrying substantial weight in research assessment processes, compared to other disciplines. However, a considerable number of these papers are not assigned a Digital Object Identifier (DOI), hence their citations are not reported in widely used citation datasets like OpenCitations and Crossref, raising limitations to citation analysis. While the Microsoft Academic Graph (MAG) previously addressed this issue by providing substantial coverage, its discontinuation has created a void in available data. BIP! NDR aims to alleviate this issue and enhance the research assessment processes within the field of Computer Science. To accomplish this, it leverages a workflow that identifies and retrieves Open Science papers lacking DOIs from the DBLP Corpus, and by performing text analysis, it extracts citation information directly from their full text. The current version of the dataset contains more than 510K citations made by approximately 60K open access Computer Science conference or workshop papers that, according to DBLP, do not have a DOI.

cs.DL↗

arXiv2023

Atrapos: Real-time Evaluation of Metapath Query Workloads

Heterogeneous information networks (HINs) represent different types of entities and relationships between them. Exploring, analysing, and extracting knowledge from such networks relies on metapath queries that identify pairs of entities connected by relationships of diverse semantics. While the real-time evaluation of metapath query workloads on large, web-scale HINs is highly demanding in computational cost, current approaches do not exploit interrelationships among the queries. In this paper, we present ATRAPOS, a new approach for the real-time evaluation of metapath query workloads that leverages a combination of efficient sparse matrix multiplication and intermediate result caching. ATRAPOS selects intermediate results to cache and reuse by detecting frequent sub-metapaths among workload queries in real time, using a tailor-made data structure, the Overlap Tree, and an associated caching policy. Our experimental study on real data shows that ATRAPOS accelerates exploratory data analysis and mining on HINs, outperforming off-the-shelf caching approaches and state-of-the-art research prototypes in all examined scenarios. -- Note that this version of our work is more extended than the one presented in TheWebConf 2023 (doi: 10.1145/3543507.3583322)

cs.DB↗

arXiv2023

Piloting topic-aware research impact assessment features in BIP! Services

Various research activities rely on citation-based impact indicators. However these indicators are usually globally computed, hindering their proper interpretation in applications like research assessment and knowledge discovery. In this work, we advocate for the use of topic-aware categorical impact indicators, to alleviate the aforementioned problem. In addition, we extend BIP! Services to support those indicators and showcase their benefits in real-world research activities.

cs.IR↗

arXiv2022

BIP! Scholar: A Service to Facilitate Fair Researcher Assessment

In recent years, assessing the performance of researchers has become a burden due to the extensive volume of the existing research output. As a result, evaluators often end up relying heavily on a selection of performance indicators like the h-index. However, over-reliance on such indicators may result in reinforcing dubious research practices, while overlooking important aspects of a researcher's career, such as their exact role in the production of particular research works or their contribution to other important types of academic or research activities (e.g., production of datasets, peer reviewing). In response, a number of initiatives that attempt to provide guidelines towards fairer research assessment frameworks have been established. In this work, we present BIP! Scholar, a Web-based service that offers researchers the opportunity to set up profiles that summarise their research careers taking into consideration well-established guidelines for fair research assessment, facilitating the work of evaluators who want to be more compliant with the respective practices.

cs.DL↗

arXiv2022

BIP! DB: A Dataset of Impact Measures for Scientific Publications

The growth rate of the number of scientific publications is constantly increasing, creating important challenges in the identification of valuable research and in various scholarly data management applications, in general. In this context, measures which can effectively quantify the scientific impact could be invaluable. In this work, we present BIP! DB, an open dataset that contains a variety of impact measures calculated for a large collection of more than 100 million scientific publications from various disciplines.

cs.DL↗