Christoph Lange

Md. Rezaul Karim, Tanhim Islam, Oya Beyan et al.

Artificial intelligence (AI) systems utilizing deep neural networks (DNNs) and machine learning (ML) algorithms are widely used for solving important problems in bioinformatics, biomedical informatics, and precision medicine. However, complex DNNs or ML models, which are often perceived as opaque and black-box, can make it difficult to understand the reasoning behind their decisions. This lack of transparency can be a challenge for both end-users and decision-makers, as well as AI developers. Additionally, in sensitive areas like healthcare, explainability and accountability are not only desirable but also legally required for AI systems that can have a significant impact on human lives. Fairness is another growing concern, as algorithmic decisions should not show bias or discrimination towards certain groups or individuals based on sensitive attributes. Explainable artificial intelligence (XAI) aims to overcome the opaqueness of black-box models and provide transparency in how AI systems make decisions. Interpretable ML models can explain how they make predictions and the factors that influence their outcomes. However, most state-of-the-art interpretable ML methods are domain-agnostic and evolved from fields like computer vision, automated reasoning, or statistics, making direct application to bioinformatics problems challenging without customization and domain-specific adaptation. In this paper, we discuss the importance of explainability in the context of bioinformatics, provide an overview of model-specific and model-agnostic interpretable ML methods and tools, and outline their potential caveats and drawbacks. Besides, we discuss how to customize existing interpretable ML methods for bioinformatics problems. Nevertheless, we demonstrate how XAI methods can improve transparency through case studies in bioimaging, cancer genomics, and text mining.

Zeyd Boukhers, Christoph Lange, Oya Beyan

Our vision paper outlines a plan to improve the future of semantic interoperability in data spaces through the application of machine learning. The use of data spaces, where data is exchanged among members in a self-regulated environment, is becoming increasingly popular. However, the current manual practices of managing metadata and vocabularies in these spaces are time-consuming, prone to errors, and may not meet the needs of all stakeholders. By leveraging the power of machine learning, we believe that semantic interoperability in data spaces can be significantly improved. This involves automatically generating and updating metadata, which results in a more flexible vocabulary that can accommodate the diverse terminologies used by different sub-communities. Our vision for the future of data spaces addresses the limitations of conventional data exchange and makes data more accessible and valuable for all members of the community.

Zeyd Boukhers, Oya Beyan, Cong Yang et al.

Scientific knowledge on the Web is published as passive assertions and cannot decide when to validate evidence, reconcile contradictions, or update confidence as findings accumulate. Curation depends on centralised middleware and institutional continuity, but when registries close, active stewardship stops even when data remain online. We advance the concept of Autonomous FAIR Digital Objects (aFDOs) from an abstract idea to an operational model, to offer a route from passive scientific publication toward accountable, standards-aligned automation that can outlive its publishing institutions. aFDO augments FDOs with three capabilities anchored in Semantic Web standards, namely 1) a policy layer over RDF-star aligned with PROV-O, SHACL, and ODRL for portable condition-action rules, 2) an announcement layer over ActivityStreams 2.0 that bounds per-announcement evaluation cost, and 3) an agreement layer that resolves multi-source contradictions through reputation and confidence weighted agreement under a bounded adversarial model. We provide a formal definition that distinguishes policy specifications, event handlers, and communication interfaces. We evaluate an open reference implementation on 4,305 FDOs grounded in rare-disease ontologies, namely ClinVar, HPO, and Orphanet, combined with controlled synthetic observations. The consensus mechanism resolves 56.3% of 3,914 naturally occurring ClinVar conflicts where multiple submitters disagree and an expert panel has subsequently adjudicated. Under Sybil, collusion, and poisoning attacks, the mechanism degrades gracefully within its design Byzantine-tolerance bound (f < n/5), and fails as predicted beyond that bound.

Mario Koddenbrock, Christoph Lange, Robin Legner et al.

Machine Learning (ML) has transformed many scientific fields, yet key applications still lack standardized benchmarks. Raman spectroscopy, a widely used technique for non-invasive molecular analysis, is one such field where progress is limited by fragmented datasets, inconsistent evaluation, and models that fail to capture the structure of spectral data. We introduce RamanBench, the first large-scale, fully reproducible benchmark for ML on Raman spectroscopy, consisting of streamlined data access, evaluation protocols and code, as well as a live leaderboard. It unifies 74 datasets (including 16 first released with this benchmark) across four domains, comprising 325,668 spectra and spanning classification and regression tasks under diverse experimental conditions. We benchmark 28 models under a standardized protocol, including classical methods (e.g., PLS), Raman-specific (e.g., RamanNet), Tabular Foundation Model (TFM) (e.g., TabPFN), and time-series approaches (e.g., ROCKET). TFM consistently outperform domain-specific and gradient boosting baselines, while time-series models remain competitive. However, no method generalizes across datasets, revealing a fundamental gap. Therefore, we invite the community to contribute new approaches to our living benchmark, with the potential to accelerate advances in critical applications such as medical diagnostics, biological research, and materials science.

Daham M. Mustafa, Abhishek Nadgeri, Diego Collarana et al.

This study presents an approach that uses large language models such as GPT-4 to generate usage policies in the W3C Open Digital Rights Language ODRL automatically from natural language instructions. Our approach uses the ODRL ontology and its documentation as a central part of the prompt. Our research hypothesis is that a curated version of existing ontology documentation will better guide policy generation. We present various heuristics for adapting the ODRL ontology and its documentation to guide an end-to-end KG construction process. We evaluate our approach in the context of dataspaces, i.e., distributed infrastructures for trustworthy data exchange between multiple participating organizations for the cultural domain. We created a benchmark consisting of 12 use cases of varying complexity. Our evaluation shows excellent results with up to 91.95% accuracy in the resulting knowledge graph.

Benedikt T. Arnold, Johannes Theissen-Lipp, Diego Collarana et al.

Dataspaces have recently gained adoption across various sectors, including traditionally less digitized domains such as culture. Leveraging Semantic Web technologies helps to make dataspaces FAIR, but their complexity poses a significant challenge to the adoption of dataspaces and increases their cost. The advent of Large Language Models (LLMs) raises the question of how these models can support the adoption of FAIR dataspaces. In this work, we demonstrate the potential of LLMs in dataspaces with a concrete example. We also derive a research agenda for exploring this emerging field.

Christoph Lange, Isabel Thiele, Lara Santolin et al.

In biotechnology Raman Spectroscopy is rapidly gaining popularity as a process analytical technology (PAT) that measures cell densities, substrate- and product concentrations. As it records vibrational modes of molecules it provides that information non-invasively in a single spectrum. Typically, partial least squares (PLS) is the model of choice to infer information about variables of interest from the spectra. However, biological processes are known for their complexity where convolutional neural networks (CNN) present a powerful alternative. They can handle non-Gaussian noise and account for beam misalignment, pixel malfunctions or the presence of additional substances. However, they require a lot of data during model training, and they pick up non-linear dependencies in the process variables. In this work, we exploit the additive nature of spectra in order to generate additional data points from a given dataset that have statistically independent labels so that a network trained on such data exhibits low correlations between the model predictions. We show that training a CNN on these generated data points improves the performance on datasets where the annotations do not bear the same correlation as the dataset that was used for model training. This data augmentation technique enables us to reuse spectra as training data for new contexts that exhibit different correlations. The additional data allows for building a better and more robust model. This is of interest in scenarios where large amounts of historical data are available but are currently not used for model training. We demonstrate the capabilities of the proposed method using synthetic spectra of Ralstonia eutropha batch cultivations to monitor substrate, biomass and polyhydroxyalkanoate (PHA) biopolymer concentrations during of the experiments.

Dharmen Punjani, Markos Iliakis, Theodoros Stefou et al.

Large amounts of geospatial data have been made available recently on the linked open data cloud and the portals of many national cartographic agencies (e.g., OpenStreetMap data, administrative geographies of various countries, or land cover/land use data sets). These datasets use various geospatial vocabularies and can be queried using SPARQL or its OGC-standardized extension GeoSPARQL. In this paper, we go beyond these approaches to offer a question-answering engine for natural language questions on top of linked geospatial data sources. Our system has been implemented as re-usable components of the Frankenstein question answering architecture. We give a detailed description of the system's architecture, its underlying algorithms, and its evaluation using a set of 201 natural language questions. The set of questions is offered to the research community as a gold standard dataset for the comparative evaluation of future geospatial question answering engines.

Md. Rezaul Karim, Michael Cochez, Oya Beyan et al.

The discovery of important biomarkers is a significant step towards understanding the molecular mechanisms of carcinogenesis; enabling accurate diagnosis for, and prognosis of, a certain cancer type. Before recommending any diagnosis, genomics data such as gene expressions(GE) and clinical outcomes need to be analyzed. However, complex nature, high dimensionality, and heterogeneity in genomics data make the overall analysis challenging. Convolutional neural networks(CNN) have shown tremendous success in solving such problems. However, neural network models are perceived mostly as `black box' methods because of their not well-understood internal functioning. However, interpretability is important to provide insights on why a given cancer case has a certain type. Besides, finding the most important biomarkers can help in recommending more accurate treatments and drug repositioning. In this paper, we propose a new approach called OncoNetExplainer to make explainable predictions of cancer types based on GE data. We used genomics data about 9,074 cancer patients covering 33 different cancer types from the Pan-Cancer Atlas on which we trained CNN and VGG16 networks using guided-gradient class activation maps++(GradCAM++). Further, we generate class-specific heat maps to identify significant biomarkers and computed feature importance in terms of mean absolute impact to rank top genes across all the cancer types. Quantitative and qualitative analyses show that both models exhibit high confidence at predicting the cancer types correctly giving an average precision of 96.25%. To provide comparisons with the baselines, we identified top genes, and cancer-specific driver genes using gradient boosted trees and SHapley Additive exPlanations(SHAP). Finally, our findings were validated with the annotations provided by the TumorPortal.

Afshin Sadeghi, Sarven Capadisli, Johannes Wilm et al.

An increasing number of scientific publications are created in open and transparent peer review models: a submission is published first, and then reviewers are invited, or a submission is reviewed in a closed environment but then these reviews are published with the final article, or combinations of these. Reasons for open peer review include giving better credit to reviewers and enabling readers to better appraise the quality of a publication. In most cases, the full, unstructured text of an open review is published next to the full, unstructured text of the article reviewed. This approach prevents human readers from getting a quick impression of the quality of parts of an article, and it does not easily support secondary exploitation, e.g., for scientometrics on reviews. While document formats have been proposed for publishing structured articles including reviews, integrated tool support for entire open peer review workflows resulting in such documents is still scarce. We present AR-Annotator, the Automatic Article and Review Annotator which employs a semantic information model of an article and its reviews, using semantic markup and unique identifiers for all entities of interest. The fine-grained article structure is not only exposed to authors and reviewers but also preserved in the published version. We publish articles and their reviews in a Linked Data representation and thus maximize their reusability by third-party applications. We demonstrate this reusability by running quality-related queries against the structured representation of articles and their reviews.

Behnam Ghavimi, Philipp Mayr, Sahar Vahdati et al.

Scientific full text papers are usually stored in separate places than their underlying research datasets. Authors typically make references to datasets by mentioning them for example by using their titles and the year of publication. However, in most cases explicit links that would provide readers with direct access to referenced datasets are missing. Manually detecting references to datasets in papers is time consuming and requires an expert in the domain of the paper. In order to make explicit all links to datasets in papers that have been published already, we suggest and evaluate a semi-automatic approach for finding references to datasets in social sciences papers. Our approach does not need a corpus of papers (no cold start problem) and it performs well on a small test corpus (gold standard). Our approach achieved an F-measure of 0.84 for identifying references in full texts and an F-measure of 0.83 for finding correct matches of detected references in the da|ra dataset registry.

Saeedeh Shekarpour, Denis Lukovnikov, Ashwini Jaya Kumar et al.

Question Answering (QA) systems are becoming the inspiring model for the future of search engines. While recently, underlying datasets for QA systems have been promoted from unstructured datasets to structured datasets with highly semantic-enriched metadata, but still question answering systems involve serious challenges which cause to be far beyond desired expectations. In this paper, we raise the challenges for building a Question Answering (QA) system especially with the focus of employing structured data (i.e. knowledge graph). This paper provide an exhaustive insight of the known challenges, so far. Thus, it helps researchers to easily spot open rooms for the future research agenda.

Christoph Lange, Saeedeh Shekarpour, Soren Auer

WDAqua is a Marie Curie Innovative Training Network (ITN) and is funded under EU grant number 642795 and runs from January 2015 to December 2018. WDAqua aims at advancing the state of the art by intertwining training, research and innovation efforts, centered around one service: data-driven question answering. Question answering is immediately useful to a wide audience of end users, and we will demonstrate this in settings including e-commerce, public sector information, publishing and smart cities. Question answering also covers web science and data science broadly, leading to transferrable research results and to transferrable skills of the researchers who have finished our training programme. To ensure that our research improves question answering overall, every individual research project connects at least two of these steps. Intersectional secondments (within a consortium covering academia, research institutes and industrial research as well as network-wide workshops, R and D challenges and innovation projects further balance ground-breaking research and the needs of society and industry. Training-wise these offers equip early stage researchers with the expertise and transferable technical and non-technical skills that will allow them to pursue a successful career as an academic, decision maker, practitioner or entrepreneur.

Jeremy Debattista, Christoph Lange, Sören Auer

With the increasing adoption and growth of the Linked Open Data cloud [9], with RDFa, Microformats and other ways of embedding data into ordinary Web pages, and with initiatives such as schema.org, the Web is currently being complemented with a Web of Data. Thus, the Web of Data shares many characteristics with the original Web of Documents, which also varies in quality. This heterogeneity makes it challenging to determine the quality of the data published on the Web and to subsequently make this information explicit to data consumers. The main contribution of this article is LUZZU, a quality assessment framework for Linked Open Data. Apart from providing quality metadata and quality problem reports that can be used for data cleaning, LUZZU is extensible: third party metrics can be easily plugged-in the framework. The framework does not rely on SPARQL endpoints, and is thus free of all the problems that come with them, such as query timeouts. Another advantage over SPARQL based qual- ity assessment frameworks is that metrics implemented in LUZZU can have more complex functionality than triple matching. Using the framework, we performed a quality assessment of a number of statistical linked datasets that are available on the LOD cloud. For this evaluation, 25 metrics from ten different dimensions were implemented.

Christoph Lange, Angelo Di Iorio

Linked Open Datasets about scholarly publications enable the development and integration of sophisticated end-user services; however, richer datasets are still needed. The first goal of this Challenge was to investigate novel approaches to obtain such semantic data. In particular, we were seeking methods and tools to extract information from scholarly publications, to publish it as LOD, and to use queries over this LOD to assess quality. This year we focused on the quality of workshop proceedings, and of journal articles w.r.t. their citation network. A third, open task, asked to showcase how such semantic data could be exploited and how Semantic Web technologies could help in this emerging context.

Christoph Lange, Till Mossakowski, Oliver Kutz et al.

The Distributed Ontology Language (DOL) is currently being standardized within the OntoIOp (Ontology Integration and Interoperability) activity of ISO/TC 37/SC 3. It aims at providing a unified framework for (1) ontologies formalized in heterogeneous logics, (2) modular ontologies, (3) links between ontologies, and (4) annotation of ontologies. This paper presents the current state of DOL's standardization. It focuses on use cases where distributed ontologies enable interoperability and reusability. We demonstrate relevant features of the DOL syntax and semantics and explain how these integrate into existing knowledge engineering environments.