arXiv subjects

Oliver Karras

Publications and source records attributed to Oliver Karras.

At least 19 recordsLinked to original sources

arXiv2026

Addressing Predicate Redundancy in Research Knowledge Graphs: Duplicate Detection, Resolution, and Prevention

Research Knowledge Graphs (RKGs) enable the structured representation of scientific knowledge, but their weakly enforced schemas make them prone to inconsistencies, particularly in how predicates are defined and used. Duplicate predicates, i.e., distinct identifiers expressing the same or highly similar relationships, introduce semantic redundancy, hinder reuse, and reduce RKG quality. While prior work has addressed duplicate detection for downstream tasks such as query answering or schema alignment, predicate redundancy as a data quality challenge, remains underexplored, particularly in terms of resolution, prevention, and semi-automated curator support. In this paper, we propose a framework for managing duplicate predicates in RKGs that covers detection, resolution, and prevention. The framework combines automated similarity-based methods with human validation and is designed for integration into the lifecycle of evolving, crowdsourced RKGs. We implement the framework in the context of the Open Research Knowledge Graph (ORKG) by extending its existing curation dashboard SciKGDash with embedding-based clustering, interactive inspection, and resolution actions such as merging and deleting. We evaluate the framework on the ORKG, where clustering reveals that up to 30% of predicates are potentially redundant. The analysis also shows recurring modeling patterns that lead to predicate redundancy, user-induced duplication, inconsistent identifier usage, and a lack of standardization in predicate naming and usage. Our findings demonstrate that duplicate predicates arise from user behavior and interface design. Addressing this, requires combining automated methods with human-centered curation and preventive mechanisms. This work positions predicate redundancy as a central data quality challenge and provides a foundation for more systematic and proactive RKG curation.

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arXiv2026

Speaker Mining -- FAIR Data on Public Broadcasts for Question Answering

Public broadcasts are at the center of civic discourse: Traditional television talk shows, alongside emerging podcast and web video formats, capture and guide the attention of our societies, shaping how citizens encounter politics, science, and societal issues. Yet, systematic or even simple analyses of these formats face similar challenges: guest and content metadata are scarce, fleeting, fragmented, and not standardized. Research conducted and questions answered are based on extensive, laborious, yet isolated data-curation efforts that capture only a fraction of the relevant landscape. This work seeks to address this issue using a scaling-oriented framework for FAIR data curation in public broadcasting. Evaluated on 15 broadcasting programs, the pipeline aggregates ZDF Archive PDFs, fernsehserien.de, and Wikidata into a unified knowledge graph. Of the 31,817 candidate guest mentions from these three sources, 17,729 could be automatically disambiguated, further 5,958 via 64 hours of manual reconciling using OpenRefine. Results are published at speakermining.wikibase.cloud and linked to Wikidata, enabling SPARQL-based question answering based on gender, age, occupation, or institutional affiliation across 8,436 canonical persons with 23,527 appearances in 6,469 aligned episodes. Our iterative experience reveals that correctly disambiguating and deduplicating speaker data from heterogeneous sources demands dedicated effort on sustainable infrastructure. For scalable and reliable question answering on public broadcasts to be accessible to everyone, we recommend fostering the potential of linked open data: Advancing alignment and utilization approaches like this work, particularly towards crowdsourced development and curation, but also more FAIR data interfaces from public broadcast service providers.

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arXiv2026

EmpiRE-Compass: A Neuro-Symbolic Dashboard for Sustainable and Dynamic Knowledge Exploration, Synthesis, and Reuse

Software engineering (SE) and requirements engineering (RE) face a significant increase in secondary studies, particularly literature reviews (LRs), due to the ever-growing number of scientific publications. Generative artificial intelligence (GenAI) exacerbates this trend by producing LRs rapidly but often at the expense of quality, rigor, and transparency. At the same time, secondary studies often fail to share underlying data and artifacts, limiting replication and reuse. This paper introduces EmpiRE-Compass, a neuro-symbolic dashboard designed to lower barriers for accessing, replicating, and reusing LR data. Its overarching goal is to demonstrate how LRs can become more sustainable by semantically structuring their underlying data in research knowledge graphs (RKGs) and by leveraging large language models (LLMs) for easy and dynamic access, replication, and reuse. Building on two RE use cases, we developed EmpiRE-Compass with a modular system design and workflows for curated and custom competency questions. The dashboard is freely available online, accompanied by a demonstration video. To manage operational costs, a limit of 25 requests per IP address per day applies to the default LLM (GPT-4o mini). All source code and documentation are released as an open-source project to foster reuse, adoption, and extension. EmpiRE-Compass provides three core capabilities: (1) Exploratory visual analytics for curated competency questions; (2) Neuro-symbolic synthesis for custom competency questions; and (3) Reusable knowledge with all queries, analyses, and results openly available. By unifying RKGs and LLMs in a neuro-symbolic dashboard, EmpiRE-Compass advances sustainable LRs in RE, SE, and beyond. It lowers technical barriers, fosters transparency and reproducibility, and enables collaborative, continuously updated, and reusable LRs

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arXiv2026

The Ethos of the PEERfect REVIEWer: Scientific Care and Collegial Welfare

Peer review remains a cornerstone in academia, yet it frequently falls short in fostering joint progress and well-being. While peer review primarily emphasizes scientific rigor, it often lacks the empathy essential for supporting and encouraging all peers involved. In this experience report, I aim to highlight that peer review is a practice that demands both scientific care for quality and collegial welfare for the joint progress and well-being of all peers involved, including authors, co-reviewers, workshop or conference organizers, and journal editors. Drawing on my ten years of experience in academia, I propose the ethos of the PEERfect REVIEWer, grounded in the two core values: Scientific care and collegial welfare. Through reflection shaped by professional exchanges with colleagues, consideration of literature, and an examination of both self-authored and received reviews, I formulated an accompanying guideline with 16 practical recommendations to guide reviewers in their actions to achieve these two values. The ethos of the PEERfect REVIEWer and its accompanying guideline help reviewers in upholding high scientific standards and conducting peer review in a constructive, supportive, respectful, and timely manner. They demonstrate that scientific rigor and empathy are complementary forces that promote impactful peer review practice. By placing scientific care and collegial welfare at the core of peer review, this experience report reaffirms the importance of scientific rigor while also advocating for greater attention to empathy. It invites reviewers to reconsider their role not merely as gatekeepers but as partners in the academic journey of each peer involved. The PEERfect REVIEWer is both a caretaker of quality and a steward of joint progress and well-being - as truly impactful peer review practice requires scientific rigor and empathy in equal measure.

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arXiv2026

SciKGDash: The Scientific Knowledge Graph Dashboard for Supporting Knowledge Curation

Research knowledge graphs (RKGs) have emerged as essential technology for organizing scientific knowledge, but their success depends heavily on the quality of their underlying content. Knowledge curation is a critical task to ensure the quality of (research) knowledge graphs ((R)KGs), with human curation being the gold standard despite its time- and resource-intensive nature. Automated methods, while efficient, lack the precision of human expertise. Hybrid approaches, combining automated processes with human oversight, offer a promising solution to this challenge. Dashboards can act as supportive tools in hybrid curation approaches, offering real-time updates and visual overviews. This paper presents an action research study, conducted in collaboration with the Curation and Community Building (C&CB) team of the Open Research Knowledge Graph (ORKG), to explore the development of a dashboard, called SciKGDash, designed to support knowledge curation of the ORKG. SciKGDash serves as a minimum viable product (MVP) tailored to the needs of the C&CB team, with potential for adaptation to other (R)KGs. An experiment with 15 participants demonstrated the usability of SciKGDash, with successful completion of 4 out of 5 curation tasks in under 5 minutes. In addition, SciKGDash received a positive user experience rating (UEQ score of 1.93). While the tailored solution proved effective for the ORKG, the research also highlights limitations in applying specific quality metrics across diverse (R)KGs. Future work should focus on identifying common quality metrics and enhancing SciKGDash with user-friendly features for querying customized quality metrics. Overall, knowledge curation in RKGs remains an under-explored field, warranting further research.

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arXiv2025

Towards AI-Supported Research: a Vision of the TIB AIssistant

The rapid advancements in Generative AI and Large Language Models promise to transform the way research is conducted, potentially offering unprecedented opportunities to augment scholarly workflows. However, effectively integrating AI into research remains a challenge due to varying domain requirements, limited AI literacy, the complexity of coordinating tools and agents, and the unclear accuracy of Generative AI in research. We present the vision of the TIB AIssistant, a domain-agnostic human-machine collaborative platform designed to support researchers across disciplines in scientific discovery, with AI assistants supporting tasks across the research life cycle. The platform offers modular components - including prompt and tool libraries, a shared data store, and a flexible orchestration framework - that collectively facilitate ideation, literature analysis, methodology development, data analysis, and scholarly writing. We describe the conceptual framework, system architecture, and implementation of an early prototype that demonstrates the feasibility and potential impact of our approach.

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arXiv2025

SciCom Wiki: A Digital Library to Support the Science Communication Knowledge Infrastructure for Videos and Podcasts

Videos and Podcasts have established themselves as the medium of choice for civic dissemination, but also as carriers of misinformation. The emerging Science Communication Knowledge Infrastructure (SciCom KI), which curates these increasingly non-textual media, remains fragmented and inadequately equipped to scale against the content flood. Our work sets out to support the SciCom KI with a central, collaborative platform, the SciCom Wiki, to facilitate FAIR (findable, accessible, interoperable, reusable) media representation, particularly for videos and podcasts. We survey requirements from 53 stakeholders and individually refine these insights in 11 interviews. We then design and implement an open-source service system centered on Wikibase and evaluate our prototype with another 14 participants. Overall, our findings identified several needs to support the SciCom KI systematically. Our SciCom Wiki approach was found suitable to address the raised requirements. Further, we identified that the SciCom KI is severely underdeveloped regarding FAIR knowledge and related systems facilitating its collaborative creation and curation. Our system can provide a central knowledge node similar to Wikidata, yet a collaborative effort is required to scale the necessary features against the imminent (mis-)information flood.

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arXiv2025

ExtracTable: Human-in-the-Loop Transformation of Scientific Corpora into Structured Knowledge

As the volume of scientific literature grows, efficient knowledge organization becomes increasingly challenging. Traditional approaches to structuring scientific content are time-consuming and require significant domain expertise, highlighting the need for tool support. We present ExtracTable, a Human-in-the-Loop (HITL) workflow and framework that assists researchers in transforming unstructured publications into structured representations. The workflow combines large language models (LLMs) with user-defined schemas and is designed for downstream integration into knowledge graphs (KGs). Developed and evaluated in the context of the Open Research Knowledge Graph (ORKG), ExtracTable automates key steps such as document preprocessing and data extraction while ensuring user oversight through validation. In an evaluation with ORKG community participants following the Quality Improvement Paradigm (QIP), ExtracTable demonstrated high usability and practical value. Participants gave it an average System Usability Scale (SUS) score of 84.17 (A+, the highest rating). The time to progress from a research interest to literature-based insights was reduced from between 4 hours and 2 weeks to an average of 24:40 minutes. By streamlining corpus creation and structured data extraction for knowledge graph integration, ExtracTable leverages LLMs and user models to accelerate literature reviews. However, human validation remains essential to ensure quality, and future work will address improving extraction accuracy and entity linking to existing knowledge resources.

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arXiv2025

SciCom Wiki: Fact-Checking and FAIR Knowledge Distribution for Scientific Videos and Podcasts

Democratic societies need accessible, reliable information. Videos and Podcasts have established themselves as the medium of choice for civic dissemination, but also as carriers of misinformation. The emerging Science Communication Knowledge Infrastructure (SciCom KI) curating non-textual media is still fragmented and not adequately equipped to scale against the content flood. Our work sets out to support the SciCom KI with a central, collaborative platform, the SciCom Wiki, to facilitate FAIR (findable, accessible, interoperable, reusable) media representation and the fact-checking of their content, particularly for videos and podcasts. Building an open-source service system centered around Wikibase, we survey requirements from 53 stakeholders, refine these in 11 interviews, and evaluate our prototype based on these requirements with another 14 participants. To address the most requested feature, fact-checking, we developed a neurosymbolic computational fact-checking approach, converting heterogenous media into knowledge graphs. This increases machine-readability and allows comparing statements against equally represented ground-truth. Our computational fact-checking tool was iteratively evaluated through 10 expert interviews, a public user survey with 43 participants verified the necessity and usability of our tool. Overall, our findings identified several needs to systematically support the SciCom KI. The SciCom Wiki, as a FAIR digital library complementing our neurosymbolic computational fact-checking framework, was found suitable to address the raised requirements. Further, we identified that the SciCom KI is severely underdeveloped regarding FAIR knowledge and related systems facilitating its collaborative creation and curation. Our system can provide a central knowledge node, yet a collaborative effort is required to scale against the imminent (mis-)information flood.

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arXiv2025

SciMantify -- A Hybrid Approach for the Evolving Semantification of Scientific Knowledge

Scientific publications, primarily digitized as PDFs, remain static and unstructured, limiting the accessibility and reusability of the contained knowledge. At best, scientific knowledge from publications is provided in tabular formats, which lack semantic context. A more flexible, structured, and semantic representation is needed to make scientific knowledge understandable and processable by both humans and machines. We propose an evolution model of knowledge representation, inspired by the 5-star Linked Open Data (LOD) model, with five stages and defined criteria to guide the stepwise transition from a digital artifact, such as a PDF, to a semantic representation integrated in a knowledge graph (KG). Based on an exemplary workflow implementing the entire model, we developed a hybrid approach, called SciMantify, leveraging tabular formats of scientific knowledge, e.g., results from secondary studies, to support its evolving semantification. In the approach, humans and machines collaborate closely by performing semantic annotation tasks (SATs) and refining the results to progressively improve the semantic representation of scientific knowledge. We implemented the approach in the Open Research Knowledge Graph (ORKG), an established platform for improving the findability, accessibility, interoperability, and reusability of scientific knowledge. A preliminary user experiment showed that the approach simplifies the preprocessing of scientific knowledge, reduces the effort for the evolving semantification, and enhances the knowledge representation through better alignment with the KG structures.

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arXiv2025

OntoAligner: A Comprehensive Modular and Robust Python Toolkit for Ontology Alignment

Ontology Alignment (OA) is fundamental for achieving semantic interoperability across diverse knowledge systems. We present OntoAligner, a comprehensive, modular, and robust Python toolkit for ontology alignment, designed to address current limitations with existing tools faced by practitioners. Existing tools are limited in scalability, modularity, and ease of integration with recent AI advances. OntoAligner provides a flexible architecture integrating existing lightweight OA techniques such as fuzzy matching but goes beyond by supporting contemporary methods with retrieval-augmented generation and large language models for OA. The framework prioritizes extensibility, enabling researchers to integrate custom alignment algorithms and datasets. This paper details the design principles, architecture, and implementation of the OntoAligner, demonstrating its utility through benchmarks on standard OA tasks. Our evaluation highlights OntoAligner's ability to handle large-scale ontologies efficiently with few lines of code while delivering high alignment quality. By making OntoAligner open-source, we aim to provide a resource that fosters innovation and collaboration within the OA community, empowering researchers and practitioners with a toolkit for reproducible OA research and real-world applications.

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arXiv2025

Ten Recommendations for Engineering Research Software in Energy Research

Energy research software (ERS) is a central cornerstone to facilitate energy research. However, ERS is developed by researchers who, in many cases, lack formal training in software engineering. This reduces the quality of ERS, leading to limited reproducibility and reusability. To address these issues, we developed ten central recommendations for the development of ERS, covering areas such as conceptualization, development, testing, and publication of ERS. The recommendations are based on the outcomes of two workshops with a diverse group of energy researchers and aim to improve the awareness of research software engineering in the energy domain. The recommendations should enhance the quality of ERS and, therefore, the reproducibility of energy research.

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arXiv2024

SWARM-SLR -- Streamlined Workflow Automation for Machine-actionable Systematic Literature Reviews

Authoring survey or review articles still requires significant tedious manual effort, despite many advancements in research knowledge management having the potential to improve efficiency, reproducibility, and reuse. However, these advancements bring forth an increasing number of approaches, tools, and systems, which often cover only specific stages and lack a comprehensive workflow utilizing their task-specific strengths. We propose the Streamlined Workflow Automation for Machine-actionable Systematic Literature Reviews (SWARM-SLR) to crowdsource the improvement of SLR efficiency while maintaining scientific integrity in a state-of-the-art knowledge discovery and distribution process. The workflow aims to domain-independently support researchers in collaboratively and sustainably managing the rising scholarly knowledge corpus. By synthesizing guidelines from the literature, we have composed a set of 65 requirements, spanning from planning to reporting a review. Existing tools were assessed against these requirements and synthesized into the SWARM-SLR workflow prototype, a ready-for-operation software support tool. The SWARM-SLR was evaluated via two online surveys, which largely confirmed the validity of the 65 requirements and situated 11 tools to the different life-cycle stages. The SWARM-SLR workflow was similarly evaluated and found to be supporting almost the entire span of an SLR, excelling specifically in search and retrieval, information extraction, knowledge synthesis, and distribution. Our SWARM-SLR requirements and workflow support tool streamlines the SLR support for researchers, allowing sustainable collaboration by linking individual efficiency improvements to crowdsourced knowledge management. If these efforts are continued, we expect the increasing number of tools to be manageable and usable inside fully structured, (semi-)automated literature review workflows.

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arXiv2024

KG-EmpiRE: A Community-Maintainable Knowledge Graph for a Sustainable Literature Review on the State and Evolution of Empirical Research in Requirements Engineering

In the last two decades, several researchers provided snapshots of the "current" state and evolution of empirical research in requirements engineering (RE) through literature reviews. However, these literature reviews were not sustainable, as none built on or updated previous works due to the unavailability of the extracted and analyzed data. KG-EmpiRE is a Knowledge Graph (KG) of empirical research in RE based on scientific data extracted from currently 680 papers published in the IEEE International Requirements Engineering Conference (1994-2022). KG-EmpiRE is maintained in the Open Research Knowledge Graph (ORKG), making all data openly and long-term available according to the FAIR data principles. Our long-term goal is to constantly maintain KG-EmpiRE with the research community to synthesize a comprehensive, up-to-date, and long-term available overview of the state and evolution of empirical research in RE. Besides KG-EmpiRE, we provide its analysis with all supplementary materials in a repository. This repository contains all files with instructions for replicating and (re-)using the analysis locally or via executable environments and for repeating the research approach. Since its first release based on 199 papers (2014-2022), KG-EmpiRE and its analysis have been updated twice, currently covering over 650 papers. KG-EmpiRE and its analysis demonstrate how innovative infrastructures, such as the ORKG, can be leveraged to make data from literature reviews FAIR, openly available, and maintainable for the research community in the long term. In this way, we can enable replicable, (re-)usable, and thus sustainable literature reviews to ensure the quality, reliability, and timeliness of their research results.

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arXiv2024

Organizing Scientific Knowledge From Energy System Research Using the Open Research Knowledge Graph

Engineering sciences, such as energy system research, play an important role in developing solutions to technical, environmental, economic, and social challenges of our modern society. In this context, the transformation of energy systems into climate-neutral systems is one of the key strategies for mitigating climate change. For the transformation of energy systems, engineers model, simulate and analyze scenarios and transformation pathways to initiate debates about possible transformation strategies. For these debates and research in general, all steps of the research process must be traceable to guarantee the trustworthiness of published results, avoid redundancies, and ensure their social acceptance. However, the analysis of energy systems is an interdisciplinary field as the investigations of large, complex energy systems often require the use of different software applications and large amounts of heterogeneous data. Engineers must therefore communicate, understand, and (re)use heterogeneous scientific knowledge and data. Although the importance of FAIR scientific knowledge and data in the engineering sciences and energy system research is increasing, little research has been conducted on this topic. When it comes to publishing scientific knowledge and data from publications, software, and datasets (such as models, scenarios, and simulations) openly available and transparent, energy system research lags behind other research domains. According to Schmitt et al. and Nieße et al., engineers need technical support in the form of infrastructures, services, and terminologies to improve communication, understanding, and (re)use of scientific knowledge and data.

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arXiv2023

Divide and Conquer the EmpiRE: A Community-Maintainable Knowledge Graph of Empirical Research in Requirements Engineering

[Background.] Empirical research in requirements engineering (RE) is a constantly evolving topic, with a growing number of publications. Several papers address this topic using literature reviews to provide a snapshot of its "current" state and evolution. However, these papers have never built on or updated earlier ones, resulting in overlap and redundancy. The underlying problem is the unavailability of data from earlier works. Researchers need technical infrastructures to conduct sustainable literature reviews. [Aims.] We examine the use of the Open Research Knowledge Graph (ORKG) as such an infrastructure to build and publish an initial Knowledge Graph of Empirical research in RE (KG-EmpiRE) whose data is openly available. Our long-term goal is to continuously maintain KG-EmpiRE with the research community to synthesize a comprehensive, up-to-date, and long-term available overview of the state and evolution of empirical research in RE. [Method.] We conduct a literature review using the ORKG to build and publish KG-EmpiRE which we evaluate against competency questions derived from a published vision of empirical research in software (requirements) engineering for 2020 - 2025. [Results.] From 570 papers of the IEEE International Requirements Engineering Conference (2000 - 2022), we extract and analyze data on the reported empirical research and answer 16 out of 77 competency questions. These answers show a positive development towards the vision, but also the need for future improvements. [Conclusions.] The ORKG is a ready-to-use and advanced infrastructure to organize data from literature reviews as knowledge graphs. The resulting knowledge graphs make the data openly available and maintainable by research communities, enabling sustainable literature reviews.

cs.SE↗

arXiv2023

A Metadata-Based Ecosystem to Improve the FAIRness of Research Software

The reuse of research software is central to research efficiency and academic exchange. The application of software enables researchers with varied backgrounds to reproduce, validate, and expand upon study findings. Furthermore, the analysis of open source code aids in the comprehension, comparison, and integration of approaches. Often, however, no further use occurs because relevant software cannot be found or is incompatible with existing research processes. This results in repetitive software development, which impedes the advancement of individual researchers and entire research communities. In this article, the DataDesc ecosystem is presented, an approach to describing data models of software interfaces with detailed and machine-actionable metadata. In addition to a specialized metadata schema, an exchange format and support tools for easy collection and the automated publishing of software documentation are introduced. This approach practically increases the FAIRness, i.e., findability, accessibility, interoperability, and so the reusability of research software, as well as effectively promotes its impact on research.

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arXiv2023

SciKGTeX -- A LaTeX Package to Semantically Annotate Contributions in Scientific Publications

Scientific knowledge graphs have been proposed as a solution to structure the content of research publications in a machine-actionable way and enable more efficient, computer-assisted workflows for many research activities. Crowd-sourcing approaches are used frequently to build and maintain such scientific knowledge graphs. To contribute to scientific knowledge graphs, researchers need simple and easy-to-use solutions to generate new knowledge graph elements and establish the practice of semantic representations in scientific communication. In this paper, we present a workflow for authors of scientific documents to specify their contributions with a LaTeX package, called SciKGTeX, and upload them to a scientific knowledge graph. The SciKGTeX package allows authors of scientific publications to mark the main contributions of their work directly in LaTeX source files. The package embeds marked contributions as metadata into the generated PDF document, from where they can be extracted automatically and imported into a scientific knowledge graph, such as the ORKG. This workflow is simpler and faster than current approaches, which make use of external web interfaces for data entry. Our user evaluation shows that SciKGTeX is easy to use, with a score of 79 out of 100 on the System Usability Scale, as participants of the study needed only 7 minutes on average to annotate the main contributions on a sample abstract of a published paper. Further testing shows that the embedded contributions can be successfully uploaded to ORKG within ten seconds. SciKGTeX simplifies the process of manual semantic annotation of research contributions in scientific articles. Our workflow demonstrates how a scientific knowledge graph can automatically ingest research contributions from document metadata.

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