Jennifer D'Souza

Hamed Babaei Giglou, Jennifer D'Souza, Sören Auer

We propose the LLMs4OL approach, which utilizes Large Language Models (LLMs) for Ontology Learning (OL). LLMs have shown significant advancements in natural language processing, demonstrating their ability to capture complex language patterns in different knowledge domains. Our LLMs4OL paradigm investigates the following hypothesis: \textit{Can LLMs effectively apply their language pattern capturing capability to OL, which involves automatically extracting and structuring knowledge from natural language text?} To test this hypothesis, we conduct a comprehensive evaluation using the zero-shot prompting method. We evaluate nine different LLM model families for three main OL tasks: term typing, taxonomy discovery, and extraction of non-taxonomic relations. Additionally, the evaluations encompass diverse genres of ontological knowledge, including lexicosemantic knowledge in WordNet, geographical knowledge in GeoNames, and medical knowledge in UMLS.

Jennifer D'Souza, Sören Auer

Domain-specific named entity recognition (NER) on Computer Science (CS) scholarly articles is an information extraction task that is arguably more challenging for the various annotation aims that can beset the task and has been less studied than NER in the general domain. Given that significant progress has been made on NER, we believe that scholarly domain-specific NER will receive increasing attention in the years to come. Currently, progress on CS NER -- the focus of this work -- is hampered in part by its recency and the lack of a standardized annotation aim for scientific entities/terms. This work proposes a standardized task by defining a set of seven contribution-centric scholarly entities for CS NER viz., research problem, solution, resource, language, tool, method, and dataset. Following which, its main contributions are: combines existing CS NER resources that maintain their annotation focus on the set or subset of contribution-centric scholarly entities we consider; further, noting the need for big data to train neural NER models, this work additionally supplies thousands of contribution-centric entity annotations from article titles and abstracts, thus releasing a cumulative large novel resource for CS NER; and, finally, trains a sequence labeling CS NER model inspired after state-of-the-art neural architectures from the general domain NER task. Throughout the work, several practical considerations are made which can be useful to information technology designers of the digital libraries.

Hamed Babaei Giglou, Jennifer D'Souza, Sören Auer

In response to the growing complexity and volume of scientific literature, this paper introduces the LLMs4Synthesis framework, designed to enhance the capabilities of Large Language Models (LLMs) in generating high-quality scientific syntheses. This framework addresses the need for rapid, coherent, and contextually rich integration of scientific insights, leveraging both open-source and proprietary LLMs. It also examines the effectiveness of LLMs in evaluating the integrity and reliability of these syntheses, alleviating inadequacies in current quantitative metrics. Our study contributes to this field by developing a novel methodology for processing scientific papers, defining new synthesis types, and establishing nine detailed quality criteria for evaluating syntheses. The integration of LLMs with reinforcement learning and AI feedback is proposed to optimize synthesis quality, ensuring alignment with established criteria. The LLMs4Synthesis framework and its components are made available, promising to enhance both the generation and evaluation processes in scientific research synthesis.

Salomon Kabongo, Jennifer D'Souza, Sören Auer

We present a large-scale empirical investigation of the zero-shot learning phenomena in a specific recognizing textual entailment (RTE) task category, i.e. the automated mining of leaderboards for Empirical AI Research. The prior reported state-of-the-art models for leaderboards extraction formulated as an RTE task, in a non-zero-shot setting, are promising with above 90% reported performances. However, a central research question remains unexamined: did the models actually learn entailment? Thus, for the experiments in this paper, two prior reported state-of-the-art models are tested out-of-the-box for their ability to generalize or their capacity for entailment, given leaderboard labels that were unseen during training. We hypothesize that if the models learned entailment, their zero-shot performances can be expected to be moderately high as well--perhaps, concretely, better than chance. As a result of this work, a zero-shot labeled dataset is created via distant labeling formulating the leaderboard extraction RTE task.

Julia Evans, Jennifer D'Souza, Sören Auer

Our study explores how well the state-of-the-art Large Language Models (LLMs), like GPT-4 and Mistral, can assess the quality of scientific summaries or, more fittingly, scientific syntheses, comparing their evaluations to those of human annotators. We used a dataset of 100 research questions and their syntheses made by GPT-4 from abstracts of five related papers, checked against human quality ratings. The study evaluates both the closed-source GPT-4 and the open-source Mistral model's ability to rate these summaries and provide reasons for their judgments. Preliminary results show that LLMs can offer logical explanations that somewhat match the quality ratings, yet a deeper statistical analysis shows a weak correlation between LLM and human ratings, suggesting the potential and current limitations of LLMs in scientific synthesis evaluation.

Jennifer D'Souza

We are faced with an unprecedented production in scholarly publications worldwide. Stakeholders in the digital libraries posit that the document-based publishing paradigm has reached the limits of adequacy. Instead, structured, machine-interpretable, fine-grained scholarly knowledge publishing as Knowledge Graphs (KG) is strongly advocated. In this work, we develop and analyze a large-scale structured dataset of STEM articles across 10 different disciplines, viz. Agriculture, Astronomy, Biology, Chemistry, Computer Science, Earth Science, Engineering, Material Science, Mathematics, and Medicine. Our analysis is defined over a large-scale corpus comprising 60K abstracts structured as four scientific entities process, method, material, and data. Thus our study presents, for the first-time, an analysis of a large-scale multidisciplinary corpus under the construct of four named entity labels that are specifically defined and selected to be domain-independent as opposed to domain-specific. The work is then inadvertently a feasibility test of characterizing multidisciplinary science with domain-independent concepts. Further, to summarize the distinct facets of scientific knowledge per concept per discipline, a set of word cloud visualizations are offered. The STEM-NER-60k corpus, created in this work, comprises over 1M extracted entities from 60k STEM articles obtained from a major publishing platform and is publicly released https://github.com/jd-coderepos/stem-ner-60k.

Omar Arab Oghli, Jennifer D'Souza, Sören Auer

When semantically describing knowledge graphs (KGs), users have to make a critical choice of a vocabulary (i.e. predicates and resources). The success of KG building is determined by the convergence of shared vocabularies so that meaning can be established. The typical lifecycle for a new KG construction can be defined as follows: nascent phases of graph construction experience terminology divergence, while later phases of graph construction experience terminology convergence and reuse. In this paper, we describe our approach tailoring two AI-based clustering algorithms for recommending predicates (in RDF statements) about resources in the Open Research Knowledge Graph (ORKG) https://orkg.org/. Such a service to recommend existing predicates to semantify new incoming data of scholarly publications is of paramount importance for fostering terminology convergence in the ORKG. Our experiments show very promising results: a high precision with relatively high recall in linear runtime performance. Furthermore, this work offers novel insights into the predicate groups that automatically accrue loosely as generic semantification patterns for semantification of scholarly knowledge spanning 44 research fields.

Jennifer D'Souza, Anita Monteverdi, Muhammad Haris et al.

Background: Recent years are seeing a growing impetus in the semantification of scholarly knowledge at the fine-grained level of scientific entities in knowledge graphs. The Open Research Knowledge Graph (ORKG) https://www.orkg.org/ represents an important step in this direction, with thousands of scholarly contributions as structured, fine-grained, machine-readable data. There is a need, however, to engender change in traditional community practices of recording contributions as unstructured, non-machine-readable text. For this in turn, there is a strong need for AI tools designed for scientists that permit easy and accurate semantification of their scholarly contributions. We present one such tool, ORKG-assays. Implementation: ORKG-assays is a freely available AI micro-service in ORKG written in Python designed to assist scientists obtain semantified bioassays as a set of triples. It uses an AI-based clustering algorithm which on gold-standard evaluations over 900 bioassays with 5,514 unique property-value pairs for 103 predicates shows competitive performance. Results and Discussion: As a result, semantified assay collections can be surveyed on the ORKG platform via tabulation or chart-based visualizations of key property values of the chemicals and compounds offering smart knowledge access to biochemists and pharmaceutical researchers in the advancement of drug development.

Jennifer D'Souza, Soren Auer, Eleni Poupaki et al.

Scientific reviews are central to knowledge integration in materials science, yet their key insights remain locked in narrative text and static PDF tables, limiting reuse by humans and machines alike. This article presents a case study in atomic layer deposition and etching (ALD/E) where we publish review tables as FAIR, machine-actionable comparisons in the Open Research Knowledge Graph (ORKG), turning them into structured, queryable knowledge. Building on this, we contrast symbolic querying over ORKG with large language model-based querying, and argue that a curated symbolic layer should remain the backbone of reliable neurosymbolic AI in materials science, with LLMs serving as complementary, symbolically grounded interfaces rather than standalone sources of truth.

Hamed Babaei Giglou, Jennifer D'Souza, Sören Auer

This paper outlines the LLMs4OL 2024, the first edition of the Large Language Models for Ontology Learning Challenge. LLMs4OL is a community development initiative collocated with the 23rd International Semantic Web Conference (ISWC) to explore the potential of Large Language Models (LLMs) in Ontology Learning (OL), a vital process for enhancing the web with structured knowledge to improve interoperability. By leveraging LLMs, the challenge aims to advance understanding and innovation in OL, aligning with the goals of the Semantic Web to create a more intelligent and user-friendly web. In this paper, we give an overview of the 2024 edition of the LLMs4OL challenge and summarize the contributions.

Jennifer D'Souza, Sameer Sadruddin, Maximilian Kähler et al.

Subject indexing is vital for discovery but hard to sustain at scale and across languages. We release a large bilingual (English/German) corpus of catalog records annotated with the Integrated Authority File (GND), plus a machine-actionable GND taxonomy. The resource enables ontology-aware multi-label classification, mapping text to authority terms, and agent-assisted cataloging with reproducible, authority-grounded evaluation. We provide a brief statistical profile and qualitative error analyses of three systems. We invite the community to assess not only accuracy but usefulness and transparency, toward authority-anchored AI co-pilots that amplify catalogers' work.

Gollam Rabby, Sören Auer, Jennifer D'Souza et al.

The increasing amount of published scholarly articles, exceeding 2.5 million yearly, raises the challenge for researchers in following scientific progress. Integrating the contributions from scholarly articles into a novel type of cognitive knowledge graph (CKG) will be a crucial element for accessing and organizing scholarly knowledge, surpassing the insights provided by titles and abstracts. This research focuses on effectively conveying structured scholarly knowledge by utilizing large language models (LLMs) to categorize scholarly articles and describe their contributions in a structured and comparable manner. While previous studies explored language models within specific research domains, the extensive domain-independent knowledge captured by LLMs offers a substantial opportunity for generating structured contribution descriptions as CKGs. Additionally, LLMs offer customizable pathways through prompt engineering or fine-tuning, thus facilitating to leveraging of smaller LLMs known for their efficiency, cost-effectiveness, and environmental considerations. Our methodology involves harnessing LLM knowledge, and complementing it with domain expert-verified scholarly data sourced from a CKG. This strategic fusion significantly enhances LLM performance, especially in tasks like scholarly article categorization and predicate recommendation. Our method involves fine-tuning LLMs with CKG knowledge and additionally injecting knowledge from a CKG with a novel prompting technique significantly increasing the accuracy of scholarly knowledge extraction. We integrated our approach in the Open Research Knowledge Graph (ORKG), thus enabling precise access to organized scholarly knowledge, crucially benefiting domain-independent scholarly knowledge exchange and dissemination among policymakers, industrial practitioners, and the general public.

Zhiyin Tan, Jennifer D'Souza

Systematic reviews and meta-analyses rely on converting narrative articles into structured, numerically grounded study records. Despite rapid advances in large language models (LLMs), it remains unclear whether they can meet the structural requirements of this process, which hinge on preserving roles, methods, and effect-size attribution across documents rather than on recognizing isolated entities. We propose a structural, diagnostic framework that evaluates LLM-based evidence extraction as a progression of schema-constrained queries with increasing relational and numerical complexity, enabling precise identification of failure points beyond atom-level extraction. Using a manually curated corpus spanning five scientific domains, together with a unified query suite and evaluation protocol, we evaluate two state-of-the-art LLMs under both per-document and long-context, multi-document input regimes. Across domains and models, performance remains moderate for single-property queries but degrades sharply once tasks require stable binding between variables, roles, statistical methods, and effect sizes. Full meta-analytic association tuples are extracted with near-zero reliability, and long-context inputs further exacerbate these failures. Downstream aggregation amplifies even minor upstream errors, rendering corpus-level statistics unreliable. Our analysis shows that these limitations stem not from entity recognition errors, but from systematic structural breakdowns, including role reversals, cross-analysis binding drift, instance compression in dense result sections, and numeric misattribution, indicating that current LLMs lack the structural fidelity, relational binding, and numerical grounding required for automated meta-analysis. The code and data are publicly available at GitHub (https://github.com/zhiyintan/LLM-Meta-Analysis).

Anisa Rula, Jennifer D'Souza

Recent advancements in the field of Natural Language Processing, particularly the development of large-scale language models that are pretrained on vast amounts of knowledge, are creating novel opportunities within the realm of Knowledge Engineering. In this paper, we investigate the usage of large language models (LLMs) in both zero-shot and in-context learning settings to tackle the problem of extracting procedures from unstructured PDF text in an incremental question-answering fashion. In particular, we leverage the current state-of-the-art GPT-4 (Generative Pre-trained Transformer 4) model, accompanied by two variations of in-context learning that involve an ontology with definitions of procedures and steps and a limited number of samples of few-shot learning. The findings highlight both the promise of this approach and the value of the in-context learning customisations. These modifications have the potential to significantly address the challenge of obtaining sufficient training data, a hurdle often encountered in deep learning-based Natural Language Processing techniques for procedure extraction.

Sören Auer, Allard Oelen, Mohamad Yaser Jaradeh et al.

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.

Jennifer D'Souza, Hamed Babaei Giglou, Quentin Münch

Large Language Models (LLMs) drive scientific question-answering on modern search engines, yet their evaluation robustness remains underexplored. We introduce YESciEval, an open-source framework that combines fine-grained rubric-based assessment with reinforcement learning to mitigate optimism bias in LLM evaluators. We release multidisciplinary scienceQ&A datasets, including adversarial variants, with evaluation scores from multiple LLMs. Independent of proprietary models and human feedback, our approach enables scalable, cost-free evaluation. By advancing reliable LLM-as-a-judge models, this work supports AI alignment and fosters robust, transparent evaluation essential for scientific inquiry.

Hamed Babaei Giglou, Jennifer D'Souza, Oliver Karras et al.

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.

Zhiyin Tan, Jennifer D'Souza

This study presents a framework for automated evaluation of dynamically evolving topic models using Large Language Models (LLMs). Topic modeling is essential for organizing and retrieving scholarly content in digital library systems, helping users navigate complex and evolving knowledge domains. However, widely used automated metrics, such as coherence and diversity, often capture only narrow statistical patterns and fail to explain semantic failures in practice. We introduce a purpose-oriented evaluation framework that employs nine LLM-based metrics spanning four key dimensions of topic quality: lexical validity, intra-topic semantic soundness, inter-topic structural soundness, and document-topic alignment soundness. The framework is validated through adversarial and sampling-based protocols, and is applied across datasets spanning news articles, scholarly publications, and social media posts, as well as multiple topic modeling methods and open-source LLMs. Our analysis shows that LLM-based metrics provide interpretable, robust, and task-relevant assessments, uncovering critical weaknesses in topic models such as redundancy and semantic drift, which are often missed by traditional metrics. These results support the development of scalable, fine-grained evaluation tools for maintaining topic relevance in dynamic datasets. All code and data supporting this work are accessible at https://github.com/zhiyintan/topic-model-LLMjudgment.

Jennifer D'Souza, Endres Keno Sander, Andrei Aioanei

We introduce DeepResearch$^{\text{Eco}}$, a novel agentic LLM-based system for automated scientific synthesis that supports recursive, depth- and breadth-controlled exploration of original research questions -- enhancing search diversity and nuance in the retrieval of relevant scientific literature. Unlike conventional retrieval-augmented generation pipelines, DeepResearch enables user-controllable synthesis with transparent reasoning and parameter-driven configurability, facilitating high-throughput integration of domain-specific evidence while maintaining analytical rigor. Applied to 49 ecological research questions, DeepResearch achieves up to a 21-fold increase in source integration and a 14.9-fold rise in sources integrated per 1,000 words. High-parameter settings yield expert-level analytical depth and contextual diversity. Source code available at: https://github.com/sciknoworg/deep-research.

Salomon Kabongo, Jennifer D'Souza

This study demonstrates the application of instruction finetuning of pretrained Large Language Models (LLMs) to automate the generation of AI research leaderboards, extracting (Task, Dataset, Metric, Score) quadruples from articles. It aims to streamline the dissemination of advancements in AI research by transitioning from traditional, manual community curation, or otherwise taxonomy-constrained natural language inference (NLI) models, to an automated, generative LLM-based approach. Utilizing the FLAN-T5 model, this research enhances LLMs' adaptability and reliability in information extraction, offering a novel method for structured knowledge representation.

Swathi Anil, Jennifer D'Souza

Noninvasive brain stimulation (NIBS) encompasses transcranial stimulation techniques that can influence brain excitability. These techniques have the potential to treat conditions like depression, anxiety, and chronic pain, and to provide insights into brain function. However, a lack of standardized reporting practices limits its reproducibility and full clinical potential. This paper aims to foster interinterdisciplinarity toward adopting Computer Science Semantic reporting methods for the standardized documentation of Neuroscience NIBS studies making them explicitly Findable, Accessible, Interoperable, and Reusable (FAIR). In a large-scale systematic review of 600 repetitive transcranial magnetic stimulation (rTMS), a subarea of NIBS, dosages, we describe key properties that allow for structured descriptions and comparisons of the studies. This paper showcases the semantic publishing of NIBS in the ecosphere of knowledge-graph-based next-generation scholarly digital libraries. Specifically, the FAIR Semantic Web resource(s)-based publishing paradigm is implemented for the 600 reviewed rTMS studies in the Open Research Knowledge Graph.

Steffen Eger, Yong Cao, Jennifer D'Souza et al.

With the advent of large multimodal language models, science is now at a threshold of an AI-based technological transformation. Recently, a plethora of new AI models and tools has been proposed, promising to empower researchers and academics worldwide to conduct their research more effectively and efficiently. This includes all aspects of the research cycle, especially (1) searching for relevant literature; (2) generating research ideas and conducting experimentation; generating (3) text-based and (4) multimodal content (e.g., scientific figures and diagrams); and (5) AI-based automatic peer review. In this survey, we provide an in-depth overview over these exciting recent developments, which promise to fundamentally alter the scientific research process for good. Our survey covers the five aspects outlined above, indicating relevant datasets, methods and results (including evaluation) as well as limitations and scope for future research. Ethical concerns regarding shortcomings of these tools and potential for misuse (fake science, plagiarism, harms to research integrity) take a particularly prominent place in our discussion. We hope that our survey will not only become a reference guide for newcomers to the field but also a catalyst for new AI-based initiatives in the area of "AI4Science".

Hamed Babaei Giglou, Jennifer D'Souza, Felix Engel et al.

Ontology Matching (OM), is a critical task in knowledge integration, where aligning heterogeneous ontologies facilitates data interoperability and knowledge sharing. Traditional OM systems often rely on expert knowledge or predictive models, with limited exploration of the potential of Large Language Models (LLMs). We present the LLMs4OM framework, a novel approach to evaluate the effectiveness of LLMs in OM tasks. This framework utilizes two modules for retrieval and matching, respectively, enhanced by zero-shot prompting across three ontology representations: concept, concept-parent, and concept-children. Through comprehensive evaluations using 20 OM datasets from various domains, we demonstrate that LLMs, under the LLMs4OM framework, can match and even surpass the performance of traditional OM systems, particularly in complex matching scenarios. Our results highlight the potential of LLMs to significantly contribute to the field of OM.

Jennifer D'Souza, Sameer Sadruddin, Holger Israel et al.

We present SemEval-2025 Task 5: LLMs4Subjects, a shared task on automated subject tagging for scientific and technical records in English and German using the GND taxonomy. Participants developed LLM-based systems to recommend top-k subjects, evaluated through quantitative metrics (precision, recall, F1-score) and qualitative assessments by subject specialists. Results highlight the effectiveness of LLM ensembles, synthetic data generation, and multilingual processing, offering insights into applying LLMs for digital library classification.

Yoel Zimmermann, Adib Bazgir, Zartashia Afzal et al.

Here, we present the outcomes from the second Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry, which engaged participants across global hybrid locations, resulting in 34 team submissions. The submissions spanned seven key application areas and demonstrated the diverse utility of LLMs for applications in (1) molecular and material property prediction; (2) molecular and material design; (3) automation and novel interfaces; (4) scientific communication and education; (5) research data management and automation; (6) hypothesis generation and evaluation; and (7) knowledge extraction and reasoning from scientific literature. Each team submission is presented in a summary table with links to the code and as brief papers in the appendix. Beyond team results, we discuss the hackathon event and its hybrid format, which included physical hubs in Toronto, Montreal, San Francisco, Berlin, Lausanne, and Tokyo, alongside a global online hub to enable local and virtual collaboration. Overall, the event highlighted significant improvements in LLM capabilities since the previous year's hackathon, suggesting continued expansion of LLMs for applications in materials science and chemistry research. These outcomes demonstrate the dual utility of LLMs as both multipurpose models for diverse machine learning tasks and platforms for rapid prototyping custom applications in scientific research.

Vladyslav Nechakhin, Jennifer D'Souza, Steffen Eger

Structured science summaries or research contributions using properties or dimensions beyond traditional keywords enhances science findability. Current methods, such as those used by the Open Research Knowledge Graph (ORKG), involve manually curating properties to describe research papers' contributions in a structured manner, but this is labor-intensive and inconsistent between the domain expert human curators. We propose using Large Language Models (LLMs) to automatically suggest these properties. However, it's essential to assess the readiness of LLMs like GPT-3.5, Llama 2, and Mistral for this task before application. Our study performs a comprehensive comparative analysis between ORKG's manually curated properties and those generated by the aforementioned state-of-the-art LLMs. We evaluate LLM performance through four unique perspectives: semantic alignment and deviation with ORKG properties, fine-grained properties mapping accuracy, SciNCL embeddings-based cosine similarity, and expert surveys comparing manual annotations with LLM outputs. These evaluations occur within a multidisciplinary science setting. Overall, LLMs show potential as recommendation systems for structuring science, but further finetuning is recommended to improve their alignment with scientific tasks and mimicry of human expertise.

Jennifer D'Souza, Zachary Laubach, Tarek Al Mustafa et al.

This paper presents an exploratory study that harnesses the capabilities of large language models (LLMs) to mine key ecological entities from invasion biology literature. Specifically, we focus on extracting species names, their locations, associated habitats, and ecosystems, information that is critical for understanding species spread, predicting future invasions, and informing conservation efforts. Traditional text mining approaches often struggle with the complexity of ecological terminology and the subtle linguistic patterns found in these texts. By applying general-purpose LLMs without domain-specific fine-tuning, we uncover both the promise and limitations of using these models for ecological entity extraction. In doing so, this study lays the groundwork for more advanced, automated knowledge extraction tools that can aid researchers and practitioners in understanding and managing biological invasions.

Mahsa Shamsabadi, Jennifer D'Souza

This demo will present the Research Assistant (RA) tool developed to assist with six main types of research tasks defined as standardized instruction templates, instantiated with user input, applied finally as prompts to well-known--for their sophisticated natural language processing abilities--AI tools, such as ChatGPT (https://chat.openai.com/) and Gemini (https://gemini.google.com/app). The six research tasks addressed by RA are: creating FAIR research comparisons, ideating research topics, drafting grant applications, writing scientific blogs, aiding preliminary peer reviews, and formulating enhanced literature search queries. RA's reliance on generative AI tools like ChatGPT or Gemini means the same research task assistance can be offered in any scientific discipline. We demonstrate its versatility by sharing RA outputs in Computer Science, Virology, and Climate Science, where the output with the RA tool assistance mirrored that from a domain expert who performed the same research task.

Sameer Sadruddin, Jennifer D'Souza, Eleni Poupaki et al.

Extracting structured information from unstructured text is crucial for modeling real-world processes, but traditional schema mining relies on semi-structured data, limiting scalability. This paper introduces schema-miner, a novel tool that combines large language models with human feedback to automate and refine schema extraction. Through an iterative workflow, it organizes properties from text, incorporates expert input, and integrates domain-specific ontologies for semantic depth. Applied to materials science--specifically atomic layer deposition--schema-miner demonstrates that expert-guided LLMs generate semantically rich schemas suitable for diverse real-world applications.

Zhiyin Tan, Jennifer D'Souza

This study presents a framework for automated evaluation of dynamically evolving topic taxonomies in scientific literature using Large Language Models (LLMs). In digital library systems, topic modeling plays a crucial role in efficiently organizing and retrieving scholarly content, guiding researchers through complex knowledge landscapes. As research domains proliferate and shift, traditional human centric and static evaluation methods struggle to maintain relevance. The proposed approach harnesses LLMs to measure key quality dimensions, such as coherence, repetitiveness, diversity, and topic-document alignment, without heavy reliance on expert annotators or narrow statistical metrics. Tailored prompts guide LLM assessments, ensuring consistent and interpretable evaluations across various datasets and modeling techniques. Experiments on benchmark corpora demonstrate the method's robustness, scalability, and adaptability, underscoring its value as a more holistic and dynamic alternative to conventional evaluation strategies.

Julia Evans, Sameer Sadruddin, Jennifer D'Souza

In this study, we address one of the challenges of developing NER models for scholarly domains, namely the scarcity of suitable labeled data. We experiment with an approach using predictions from a fine-tuned LLM model to aid non-domain experts in annotating scientific entities within astronomy literature, with the goal of uncovering whether such a collaborative process can approximate domain expertise. Our results reveal moderate agreement between a domain expert and the LLM-assisted non-experts, as well as fair agreement between the domain expert and the LLM model's predictions. In an additional experiment, we compare the performance of finetuned and default LLMs on this task. We have also introduced a specialized scientific entity annotation scheme for astronomy, validated by a domain expert. Our approach adopts a scholarly research contribution-centric perspective, focusing exclusively on scientific entities relevant to the research theme. The resultant dataset, containing 5,000 annotated astronomy article titles, is made publicly available.

Mahsa Shamsabadi, Jennifer D'Souza

This paper highlights the growing importance of information retrieval (IR) engines in the scientific community, addressing the inefficiency of traditional keyword-based search engines due to the rising volume of publications. The proposed solution involves structured records, underpinning advanced information technology (IT) tools, including visualization dashboards, to revolutionize how researchers access and filter articles, replacing the traditional text-heavy approach. This vision is exemplified through a proof of concept centered on the "reproductive number estimate of infectious diseases" research theme, using a fine-tuned large language model (LLM) to automate the creation of structured records to populate a backend database that now goes beyond keywords. The result is a next-generation information access system as an IR method accessible at https://orkg.org/usecases/r0-estimates.

Hamed Babaei Giglou, Jennifer D'Souza, Sören Auer et al.

Ontology Alignment (OA) is essential for enabling semantic interoperability across heterogeneous knowledge systems. While recent advances have focused on large language models (LLMs) for capturing contextual semantics, this work revisits the underexplored potential of Knowledge Graph Embedding (KGE) models, which offer scalable, structure-aware representations well-suited to ontology-based tasks. Despite their effectiveness in link prediction, KGE methods remain underutilized in OA, with most prior work focusing narrowly on a few models. To address this gap, we reformulate OA as a link prediction problem over merged ontologies represented as RDF-style triples and develop a modular framework, integrated into the OntoAligner library, that supports 17 diverse KGE models. The system learns embeddings from a combined ontology and aligns entities by computing cosine similarity between their representations. We evaluate our approach using standard metrics across seven benchmark datasets spanning five domains: Anatomy, Biodiversity, Circular Economy, Material Science and Engineering, and Biomedical Machine Learning. Two key findings emerge: first, KGE models like ConvE and TransF consistently produce high-precision alignments, outperforming traditional systems in structure-rich and multi-relational domains; second, while their recall is moderate, this conservatism makes KGEs well-suited for scenarios demanding high-confidence mappings. Unlike LLM-based methods that excel at contextual reasoning, KGEs directly preserve and exploit ontology structure, offering a complementary and computationally efficient strategy. These results highlight the promise of embedding-based OA and open pathways for further work on hybrid models and adaptive strategies.

Hamed Babaei Giglou, Tilahun Abedissa Taffa, Rana Abdullah et al.

This paper introduces a scholarly Question Answering (QA) system on top of the NFDI4DataScience Gateway, employing a Retrieval Augmented Generation-based (RAG) approach. The NFDI4DS Gateway, as a foundational framework, offers a unified and intuitive interface for querying various scientific databases using federated search. The RAG-based scholarly QA, powered by a Large Language Model (LLM), facilitates dynamic interaction with search results, enhancing filtering capabilities and fostering a conversational engagement with the Gateway search. The effectiveness of both the Gateway and the scholarly QA system is demonstrated through experimental analysis.

Salomon Kabongo, Jennifer D'Souza, Sören Auer

This paper explores the impact of context selection on the efficiency of Large Language Models (LLMs) in generating Artificial Intelligence (AI) research leaderboards, a task defined as the extraction of (Task, Dataset, Metric, Score) quadruples from scholarly articles. By framing this challenge as a text generation objective and employing instruction finetuning with the FLAN-T5 collection, we introduce a novel method that surpasses traditional Natural Language Inference (NLI) approaches in adapting to new developments without a predefined taxonomy. Through experimentation with three distinct context types of varying selectivity and length, our study demonstrates the importance of effective context selection in enhancing LLM accuracy and reducing hallucinations, providing a new pathway for the reliable and efficient generation of AI leaderboards. This contribution not only advances the state of the art in leaderboard generation but also sheds light on strategies to mitigate common challenges in LLM-based information extraction.

Salomon Kabongo, Jennifer D'Souza, Sören Auer

The rapid advancements in Large Language Models (LLMs) have opened new avenues for automating complex tasks in AI research. This paper investigates the efficacy of different LLMs-Mistral 7B, Llama-2, GPT-4-Turbo and GPT-4.o in extracting leaderboard information from empirical AI research articles. We explore three types of contextual inputs to the models: DocTAET (Document Title, Abstract, Experimental Setup, and Tabular Information), DocREC (Results, Experiments, and Conclusions), and DocFULL (entire document). Our comprehensive study evaluates the performance of these models in generating (Task, Dataset, Metric, Score) quadruples from research papers. The findings reveal significant insights into the strengths and limitations of each model and context type, providing valuable guidance for future AI research automation efforts.

Mahsa Shamsabadi, Jennifer D'Souza, Sören Auer

In this paper, we champion the use of structured and semantic content representation of discourse-based scholarly communication, inspired by tools like Wikipedia infoboxes or structured Amazon product descriptions. These representations provide users with a concise overview, aiding scientists in navigating the dense academic landscape. Our novel automated approach leverages the robust text generation capabilities of LLMs to produce structured scholarly contribution summaries, offering both a practical solution and insights into LLMs' emergent abilities. For LLMs, the prime focus is on improving their general intelligence as conversational agents. We argue that these models can also be applied effectively in information extraction (IE), specifically in complex IE tasks within terse domains like Science. This paradigm shift replaces the traditional modular, pipelined machine learning approach with a simpler objective expressed through instructions. Our results show that finetuned FLAN-T5 with 1000x fewer parameters than the state-of-the-art GPT-davinci is competitive for the task.

Jennifer D'Souza, Moussab Hrou, Sören Auer

There have been many recent investigations into prompt-based training of transformer language models for new text genres in low-resource settings. The prompt-based training approach has been found to be effective in generalizing pre-trained or fine-tuned models for transfer to resource-scarce settings. This work, for the first time, reports results on adopting prompt-based training of transformers for \textit{scholarly knowledge graph object prediction}. The work is unique in the following two main aspects. 1) It deviates from the other works proposing entity and relation extraction pipelines for predicting objects of a scholarly knowledge graph. 2) While other works have tested the method on text genera relatively close to the general knowledge domain, we test the method for a significantly different domain, i.e. scholarly knowledge, in turn testing the linguistic, probabilistic, and factual generalizability of these large-scale transformer models. We find that (i) per expectations, transformer models when tested out-of-the-box underperform on a new domain of data, (ii) prompt-based training of the models achieve performance boosts of up to 40\% in a relaxed evaluation setting, and (iii) testing the models on a starkly different domain even with a clever training objective in a low resource setting makes evident the domain knowledge capture gap offering an empirically-verified incentive for investing more attention and resources to the scholarly domain in the context of transformer models.

Salomon Kabongo, Jennifer D'Souza, Sören Auer

The purpose of this work is to describe the Orkg-Leaderboard software designed to extract leaderboards defined as Task-Dataset-Metric tuples automatically from large collections of empirical research papers in Artificial Intelligence (AI). The software can support both the main workflows of scholarly publishing, viz. as LaTeX files or as PDF files. Furthermore, the system is integrated with the Open Research Knowledge Graph (ORKG) platform, which fosters the machine-actionable publishing of scholarly findings. Thus the system output, when integrated within the ORKG's supported Semantic Web infrastructure of representing machine-actionable 'resources' on the Web, enables: 1) broadly, the integration of empirical results of researchers across the world, thus enabling transparency in empirical research with the potential to also being complete contingent on the underlying data source(s) of publications; and 2) specifically, enables researchers to track the progress in AI with an overview of the state-of-the-art (SOTA) across the most common AI tasks and their corresponding datasets via dynamic ORKG frontend views leveraging tables and visualization charts over the machine-actionable data. Our best model achieves performances above 90% F1 on the \textit{leaderboard} extraction task, thus proving Orkg-Leaderboards a practically viable tool for real-world usage. Going forward, in a sense, Orkg-Leaderboards transforms the leaderboard extraction task to an automated digitalization task, which has been, for a long time in the community, a crowdsourced endeavor.

Ming Jiang, Jennifer D'Souza, Sören Auer et al.

The rapid growth of research publications has placed great demands on digital libraries (DL) for advanced information management technologies. To cater to these demands, techniques relying on knowledge-graph structures are being advocated. In such graph-based pipelines, inferring semantic relations between related scientific concepts is a crucial step. Recently, BERT-based pre-trained models have been popularly explored for automatic relation classification. Despite significant progress, most of them were evaluated in different scenarios, which limits their comparability. Furthermore, existing methods are primarily evaluated on clean texts, which ignores the digitization context of early scholarly publications in terms of machine scanning and optical character recognition (OCR). In such cases, the texts may contain OCR noise, in turn creating uncertainty about existing classifiers' performances. To address these limitations, we started by creating OCR-noisy texts based on three clean corpora. Given these parallel corpora, we conducted a thorough empirical evaluation of eight Bert-based classification models by focusing on three factors: (1) Bert variants; (2) classification strategies; and, (3) OCR noise impacts. Experiments on clean data show that the domain-specific pre-trained Bert is the best variant to identify scientific relations. The strategy of predicting a single relation each time outperforms the one simultaneously identifying multiple relations in general. The optimal classifier's performance can decline by around 10% to 20% in F-score on the noisy corpora. Insights discussed in this study can help DL stakeholders select techniques for building optimal knowledge-graph-based systems.

Marco Anteghini, Jennifer D'Souza, Vitor A. P. Martins dos Santos et al.

Biological data and knowledge bases increasingly rely on Semantic Web technologies and the use of knowledge graphs for data integration, retrieval and federated queries. We propose a solution for automatically semantifying biological assays. Our solution contrasts the problem of automated semantification as labeling versus clustering where the two methods are on opposite ends of the method complexity spectrum. Characteristically modeling our problem, we find the clustering solution significantly outperforms a deep neural network state-of-the-art labeling approach. This novel contribution is based on two factors: 1) a learning objective closely modeled after the data outperforms an alternative approach with sophisticated semantic modeling; 2) automatically semantifying biological assays achieves a high performance F1 of nearly 83%, which to our knowledge is the first reported standardized evaluation of the task offering a strong benchmark model.

Jennifer D'Souza, Isaiah Onando Mulang', Soeren Auer

In multiple-choice exams, students select one answer from among typically four choices and can explain why they made that particular choice. Students are good at understanding natural language questions and based on their domain knowledge can easily infer the question's answer by 'connecting the dots' across various pertinent facts. Considering automated reasoning for elementary science question answering, we address the novel task of generating explanations for answers from human-authored facts. For this, we examine the practically scalable framework of feature-rich support vector machines leveraging domain-targeted, hand-crafted features. Explanations are created from a human-annotated set of nearly 5,000 candidate facts in the WorldTree corpus. Our aim is to obtain better matches for valid facts of an explanation for the correct answer of a question over the available fact candidates. To this end, our features offer a comprehensive linguistic and semantic unification paradigm. The machine learning problem is the preference ordering of facts, for which we test pointwise regression versus pairwise learning-to-rank. Our contributions are: (1) a case study in which two preference ordering approaches are systematically compared; (2) it is a practically competent approach that can outperform some variants of BERT-based reranking models; and (3) the human-engineered features make it an interpretable machine learning model for the task.

Jennifer D'Souza, Soeren Auer

We describe a rule-based approach for the automatic acquisition of salient scientific entities from Computational Linguistics (CL) scholarly article titles. Two observations motivated the approach: (i) noting salient aspects of an article's contribution in its title; and (ii) pattern regularities capturing the salient terms that could be expressed in a set of rules. Only those lexico-syntactic patterns were selected that were easily recognizable, occurred frequently, and positionally indicated a scientific entity type. The rules were developed on a collection of 50,237 CL titles covering all articles in the ACL Anthology. In total, 19,799 research problems, 18,111 solutions, 20,033 resources, 1,059 languages, 6,878 tools, and 21,687 methods were extracted at an average precision of 75%.

Salomon Kabongo, Jennifer D'Souza, Sören Auer

With the rapid growth of research publications, empowering scientists to keep oversight over the scientific progress is of paramount importance. In this regard, the Leaderboards facet of information organization provides an overview on the state-of-the-art by aggregating empirical results from various studies addressing the same research challenge. Crowdsourcing efforts like PapersWithCode among others are devoted to the construction of Leaderboards predominantly for various subdomains in Artificial Intelligence. Leaderboards provide machine-readable scholarly knowledge that has proven to be directly useful for scientists to keep track of research progress. The construction of Leaderboards could be greatly expedited with automated text mining. This study presents a comprehensive approach for generating Leaderboards for knowledge-graph-based scholarly information organization. Specifically, we investigate the problem of automated Leaderboard construction using state-of-the-art transformer models, viz. Bert, SciBert, and XLNet. Our analysis reveals an optimal approach that significantly outperforms existing baselines for the task with evaluation scores above 90% in F1. This, in turn, offers new state-of-the-art results for Leaderboard extraction. As a result, a vast share of empirical AI research can be organized in the next-generation digital libraries as knowledge graphs.

Jennifer D'Souza, Sören Auer, Ted Pedersen

There is currently a gap between the natural language expression of scholarly publications and their structured semantic content modeling to enable intelligent content search. With the volume of research growing exponentially every year, a search feature operating over semantically structured content is compelling. The SemEval-2021 Shared Task NLPContributionGraph (a.k.a. 'the NCG task') tasks participants to develop automated systems that structure contributions from NLP scholarly articles in the English language. Being the first-of-its-kind in the SemEval series, the task released structured data from NLP scholarly articles at three levels of information granularity, i.e. at sentence-level, phrase-level, and phrases organized as triples toward Knowledge Graph (KG) building. The sentence-level annotations comprised the few sentences about the article's contribution. The phrase-level annotations were scientific term and predicate phrases from the contribution sentences. Finally, the triples constituted the research overview KG. For the Shared Task, participating systems were then expected to automatically classify contribution sentences, extract scientific terms and relations from the sentences, and organize them as KG triples. Overall, the task drew a strong participation demographic of seven teams and 27 participants. The best end-to-end task system classified contribution sentences at 57.27% F1, phrases at 46.41% F1, and triples at 22.28% F1. While the absolute performance to generate triples remains low, in the conclusion of this article, the difficulty of producing such data and as a consequence of modeling it is highlighted.

Jennifer D'Souza, Sören Auer

Purpose: The aim of this work is to normalize the NLPCONTRIBUTIONS scheme (henceforward, NLPCONTRIBUTIONGRAPH) to structure, directly from article sentences, the contributions information in Natural Language Processing (NLP) scholarly articles via a two-stage annotation methodology: 1) pilot stage - to define the scheme (described in prior work); and 2) adjudication stage - to normalize the graphing model (the focus of this paper). Design/methodology/approach: We re-annotate, a second time, the contributions-pertinent information across 50 prior-annotated NLP scholarly articles in terms of a data pipeline comprising: contribution-centered sentences, phrases, and triple statements. To this end, specifically, care was taken in the adjudication annotation stage to reduce annotation noise while formulating the guidelines for our proposed novel NLP contributions structuring and graphing scheme. Findings: The application of NLPCONTRIBUTIONGRAPH on the 50 articles resulted finally in a dataset of 900 contribution-focused sentences, 4,702 contribution-information-centered phrases, and 2,980 surface-structured triples. The intra-annotation agreement between the first and second stages, in terms of F1, was 67.92% for sentences, 41.82% for phrases, and 22.31% for triple statements indicating that with increased granularity of the information, the annotation decision variance is greater. Practical Implications: We demonstrate NLPCONTRIBUTIONGRAPH data integrated into the Open Research Knowledge Graph (ORKG), a next-generation KG-based digital library with intelligent computations enabled over structured scholarly knowledge, as a viable aid to assist researchers in their day-to-day tasks.

Marco Anteghini, Jennifer D'Souza, Vitor A. P. Martins dos Santos et al.

As a novel contribution to the problem of semantifying biological assays, in this paper, we propose a neural-network-based approach to automatically semantify, thereby structure, unstructured bioassay text descriptions. Experimental evaluations, to this end, show promise as the neural-based semantification significantly outperforms a naive frequency-based baseline approach. Specifically, the neural method attains 72% F1 versus 47% F1 from the frequency-based method.

Jennifer D'Souza, Sören Auer

We describe an annotation initiative to capture the scholarly contributions in natural language processing (NLP) articles, particularly, for the articles that discuss machine learning (ML) approaches for various information extraction tasks. We develop the annotation task based on a pilot annotation exercise on 50 NLP-ML scholarly articles presenting contributions to five information extraction tasks 1. machine translation, 2. named entity recognition, 3. question answering, 4. relation classification, and 5. text classification. In this article, we describe the outcomes of this pilot annotation phase. Through the exercise we have obtained an annotation methodology; and found ten core information units that reflect the contribution of the NLP-ML scholarly investigations. The resulting annotation scheme we developed based on these information units is called NLPContributions. The overarching goal of our endeavor is four-fold: 1) to find a systematic set of patterns of subject-predicate-object statements for the semantic structuring of scholarly contributions that are more or less generically applicable for NLP-ML research articles; 2) to apply the discovered patterns in the creation of a larger annotated dataset for training machine readers of research contributions; 3) to ingest the dataset into the Open Research Knowledge Graph (ORKG) infrastructure as a showcase for creating user-friendly state-of-the-art overviews; 4) to integrate the machine readers into the ORKG to assist users in the manual curation of their respective article contributions. We envision that the NLPContributions methodology engenders a wider discussion on the topic toward its further refinement and development. Our pilot annotated dataset of 50 NLP-ML scholarly articles according to the NLPContributions scheme is openly available to the research community at https://doi.org/10.25835/0019761.

Ming Jiang, Jennifer D'Souza, Sören Auer et al.

With the rapid growth of research publications, there is a vast amount of scholarly knowledge that needs to be organized in digital libraries. To deal with this challenge, techniques relying on knowledge-graph structures are being advocated. Within such graph-based pipelines, inferring relation types between related scientific concepts is a crucial step. Recently, advanced techniques relying on language models pre-trained on the large corpus have been popularly explored for automatic relation classification. Despite remarkable contributions that have been made, many of these methods were evaluated under different scenarios, which limits their comparability. To this end, we present a thorough empirical evaluation on eight Bert-based classification models by focusing on two key factors: 1) Bert model variants, and 2) classification strategies. Experiments on three corpora show that domain-specific pre-training corpus benefits the Bert-based classification model to identify the type of scientific relations. Although the strategy of predicting a single relation each time achieves a higher classification accuracy than the strategy of identifying multiple relation types simultaneously in general, the latter strategy demonstrates a more consistent performance in the corpus with either a large or small size of annotations. Our study aims to offer recommendations to the stakeholders of digital libraries for selecting the appropriate technique to build knowledge-graph-based systems for enhanced scholarly information organization.

Jennifer D'Souza, Anett Hoppe, Arthur Brack et al.

We introduce the STEM (Science, Technology, Engineering, and Medicine) Dataset for Scientific Entity Extraction, Classification, and Resolution, version 1.0 (STEM-ECR v1.0). The STEM-ECR v1.0 dataset has been developed to provide a benchmark for the evaluation of scientific entity extraction, classification, and resolution tasks in a domain-independent fashion. It comprises abstracts in 10 STEM disciplines that were found to be the most prolific ones on a major publishing platform. We describe the creation of such a multidisciplinary corpus and highlight the obtained findings in terms of the following features: 1) a generic conceptual formalism for scientific entities in a multidisciplinary scientific context; 2) the feasibility of the domain-independent human annotation of scientific entities under such a generic formalism; 3) a performance benchmark obtainable for automatic extraction of multidisciplinary scientific entities using BERT-based neural models; 4) a delineated 3-step entity resolution procedure for human annotation of the scientific entities via encyclopedic entity linking and lexicographic word sense disambiguation; and 5) human evaluations of Babelfy returned encyclopedic links and lexicographic senses for our entities. Our findings cumulatively indicate that human annotation and automatic learning of multidisciplinary scientific concepts as well as their semantic disambiguation in a wide-ranging setting as STEM is reasonable.