@inbook{65670, abstract = {{Ensuring the veracity of assertions is {vital for building reliable and consistent knowledge graphs}. A variety of automatic fact-checking approaches have been proposed over the past decade. Among these, path-based fact-checking approaches are particularly attractive due to their independence of supplementary external knowledge and their faster runtimes compared to methods reliant on external corpora or embeddings. However, the effectiveness of these approaches is fundamentally limited by the incompleteness of existing knowledge graphs, which often lack the paths necessary to support or refute assertions. To address this limitation, we propose \system{}, a framework that supplements the knowledge graph with shallow knowledge---automatically extracted RDF assertions from external unstructured sources---even if this additional knowledge may not always fit a well-defined ontology nor be fully verified. By appending such shallow knowledge, we enhance the graph’s coverage and increase the chances of finding relevant evidence for fact checking. Comprehensive experiments on three widely used benchmark datasets demonstrate that integrating \system{} consistently and significantly enhances the performance of {state-of-the-art path-based fact-checking approaches}, yielding improvements of up to 0.24 in Area Under the Receiver Operating Characteristic Curve (AUROC). These results establish \system{} as a broadly applicable auxiliary component for improving the reliability and coverage of automatic fact checking in knowledge graphs. Our code is open-source and can be found at \url{https://github.com/dice-group/ShallKnow}.}}, author = {{Qudus, Umair and Pokharel, Neha and Röder, Michael and Ngonga Ngomo, Axel-Cyrille}}, booktitle = {{Lecture Notes in Computer Science}}, isbn = {{9783032251558}}, issn = {{0302-9743}}, keywords = {{fact checking}}, publisher = {{Springer Nature Switzerland}}, title = {{{No Need to Be a Know-It-All: Fact Checking with Shallow Knowledge}}}, doi = {{10.1007/978-3-032-25156-5_23}}, year = {{2026}}, } @inbook{65736, author = {{Kamdem Teyou, Louis Mozart and Demir, Caglar and Ngonga Ngomo, Axel-Cyrille}}, booktitle = {{Lecture Notes in Computer Science}}, isbn = {{9783032251558}}, issn = {{0302-9743}}, publisher = {{Springer Nature Switzerland}}, title = {{{Semantics-Aware Caching for Concept Learning}}}, doi = {{10.1007/978-3-032-25156-5_26}}, year = {{2026}}, } @inproceedings{65764, abstract = {{Information systems (IS) research is increasingly exploring the potential of generative artificial intelligence (GenAI), such as large language models (LLMs). For design science research (DSR), such technologies foster entirely new vistas for the design of IT artifacts that make use of their generative capabilities, but also influence DSR methodology. This shift is much more profound than it has been discussed so far. To identify existing implications of GenAI for design-oriented research in IS, we report results from an integrative literature review of recent DSR publications in leading IS outlets. Thereby, we synthesize five major theoretical challenges that arise when using GenAI in DSR projects: (1) an obscure composition of the artifact, (2) an opaque contextualization of the LLM, (3) a fragile internal consistency of the artifact, (4) a rapid erosion of prescriptive knowledge, and (5) missing methodological guidance. We investigate these challenges and conceptualize a set of three guidelines that inform DSR in the rising era of GenAI. These guidelines support researchers in designing and justifying GenAI-related DSR processes and in precisely articulating the theoretical grounding of their design decisions and evaluation strategies.}}, author = {{zur Heiden, Philipp and Beverungen, Daniel and Bartelheimer, Christian and Breidbach, Christoph}}, booktitle = {{Lecture Notes in Computer Science}}, isbn = {{9783032283122}}, issn = {{0302-9743}}, location = {{Muenster}}, publisher = {{Springer Nature Switzerland}}, title = {{{Design Science Research in an Era of Generative AI—Challenges and Theoretical Guidelines}}}, doi = {{10.1007/978-3-032-28313-9_22}}, volume = {{16606}}, year = {{2026}}, } @inbook{65835, author = {{Koldewey, Christian and Avermeyer, Celina Maleen and van der Valk, Hendrik and Zerbin, Julian and Dumitrescu, Roman}}, booktitle = {{Lecture Notes in Computer Science}}, isbn = {{9783032283122}}, issn = {{0302-9743}}, publisher = {{Springer Nature Switzerland}}, title = {{{Fundamental Patterns – A Taxonomy for Archetype Development in Information Systems}}}, doi = {{10.1007/978-3-032-28313-9_18}}, year = {{2026}}, } @inbook{66437, author = {{Krings, Sarah Claudia}}, booktitle = {{Lecture Notes in Computer Science}}, isbn = {{9783032260505}}, issn = {{0302-9743}}, publisher = {{Springer Nature Switzerland}}, title = {{{Cross-Reality Context Awareness}}}, doi = {{10.1007/978-3-032-26051-2_4}}, year = {{2026}}, } @inbook{60048, author = {{Gerlach, Raphael and von der Gracht, Sören and Dellnitz, Michael}}, booktitle = {{Lecture Notes in Computer Science}}, isbn = {{9783031917356}}, issn = {{0302-9743}}, publisher = {{Springer Nature Switzerland}}, title = {{{On the Dynamical Hierarchy in Gathering Protocols with Circulant Topologies}}}, doi = {{10.1007/978-3-031-91736-3_19}}, year = {{2025}}, } @inproceedings{62285, abstract = {{The sliding square model is a widely used abstraction for studying self-reconfigurable robotic systems, where modules are square-shaped robots that move by sliding or rotating over one another. In this paper, we propose a novel distributed algorithm that enables a group of modules to reconfigure into a rhombus shape, starting from an arbitrary side-connected configuration. It is connectivity-preserving and operates under minimal assumptions: one leader module, common chirality, constant memory per module, and visibility and communication restricted to immediate neighbors. Unlike prior work, which relaxes the original sliding square move-set, our approach uses the unmodified move-set, addressing the additional challenge of handling locked configurations. Our algorithm is sequential in nature and operates with a worst-case time complexity of O(n^2) rounds, which is optimal for sequential algorithms. To improve runtime, we introduce two parallel variants of the algorithm. Both rely on a spanning tree data structure, allowing modules to make decisions based on local connectivity. Our experimental results show a significant speedup for the first variant, and a linear average runtime for the second variant, which is worst-case optimal for parallel algorithms.}}, author = {{Kostitsyna, Irina and Liedtke, David Jan and Scheideler, Christian}}, booktitle = {{Stabilization, Safety, and Security of Distributed Systems}}, editor = {{Bonomi, Silvia and Mandal, Partha Sarathi and Robinson, Peter and Sharma, Gokarna and Tixeuil, Sebastien}}, isbn = {{9783032111265}}, issn = {{0302-9743}}, location = {{Kathmandu}}, pages = {{325--342}}, publisher = {{Springer Nature Switzerland}}, title = {{{Invited Paper: Distributed Rhombus Formation of Sliding Squares}}}, doi = {{10.1007/978-3-032-11127-2_26}}, year = {{2025}}, } @inbook{64201, author = {{DeAndres-Tame, Ivan and Faisal, Muhammad and Tolosana, Ruben and Al-Refai, Rouqaiah and Vera-Rodriguez, Ruben and Terhörst, Philipp}}, booktitle = {{Lecture Notes in Computer Science}}, isbn = {{9783031876561}}, issn = {{0302-9743}}, publisher = {{Springer Nature Switzerland}}, title = {{{From Pixels to Words: Leveraging Explainability in Face Recognition Through Interactive Natural Language Processing}}}, doi = {{10.1007/978-3-031-87657-8_22}}, year = {{2025}}, } @inbook{62988, author = {{Amakor, Augustina C. and Berkemeier, Manuel B. and Wohlleben, Meike Claudia and Sextro, Walter and Peitz, Sebastian}}, booktitle = {{Lecture Notes in Computer Science}}, isbn = {{9783032045546}}, issn = {{0302-9743}}, publisher = {{Springer Nature Switzerland}}, title = {{{Surrogate-Assisted Multi-objective Design of Complex Multibody Systems}}}, doi = {{10.1007/978-3-032-04555-3_21}}, year = {{2025}}, } @inbook{61222, author = {{Lenke, Michael and Klowait, Nils and Biere, Lea and Schulte, Carsten}}, booktitle = {{Lecture Notes in Computer Science}}, isbn = {{9783032012210}}, issn = {{0302-9743}}, publisher = {{Springer Nature Switzerland}}, title = {{{Assessing AI Literacy: A Systematic Review of Questionnaires with Emphasis on Affective, Behavioral, Cognitive, and Ethical Aspects}}}, doi = {{10.1007/978-3-032-01222-7_8}}, year = {{2025}}, } @inproceedings{61375, author = {{Reineke, Malte Fabian and Löhr, Bernd and Aßbrock, Agnes and Bartelheimer, Christian and Beverungen, Daniel}}, booktitle = {{International Conference on Business Process Management 2025}}, isbn = {{9783032028662}}, issn = {{0302-9743}}, publisher = {{Springer Nature Switzerland}}, title = {{{From Temporary Fixes to Informed Decisions—Design Echelons for Evaluating Workarounds}}}, doi = {{10.1007/978-3-032-02867-9_32}}, year = {{2025}}, } @inbook{61765, author = {{Mazur, Andreas and Peters, Henning and Artelt, André and Koller, Lukas and Hartmann, Christoph and Trächtler, Ansgar and Hammer, Barbara}}, booktitle = {{Lecture Notes in Computer Science}}, isbn = {{9783032045546}}, issn = {{0302-9743}}, publisher = {{Springer Nature Switzerland}}, title = {{{Studying the Generalization Behavior of Surrogate Models for Punch-Bending by Generating Plausible Counterfactuals}}}, doi = {{10.1007/978-3-032-04555-3_16}}, year = {{2025}}, } @inbook{62069, author = {{Kyi, Lin and Santos, Cristiana and Ammanaghatta Shivakumar, Sushil and Roesner, Franziska and Biega, Asia}}, booktitle = {{Lecture Notes in Computer Science}}, isbn = {{9783032075734}}, issn = {{0302-9743}}, publisher = {{Springer Nature Switzerland}}, title = {{{Turning to Online Forums for Legal Information: A Case Study of GDPR’s Legitimate Interests}}}, doi = {{10.1007/978-3-032-07574-1_7}}, year = {{2025}}, } @inbook{62186, author = {{Jafari, Atousa and Platzner, Marco}}, booktitle = {{Lecture Notes in Computer Science}}, isbn = {{9783031879944}}, issn = {{0302-9743}}, publisher = {{Springer Nature Switzerland}}, title = {{{Ultra-Low Latency and Extreme-Throughput Echo State Neural Networks on FPGA}}}, doi = {{10.1007/978-3-031-87995-1_11}}, year = {{2025}}, } @inbook{62701, abstract = {{Learning continuous vector representations for knowledge graphs has significantly improved state-of-the-art performances in many challenging tasks. Yet, deep-learning-based models are only post-hoc and locally explainable. In contrast, learning Web Ontology Language (OWL) class expressions in Description Logics (DLs) is ante-hoc and globally explainable. However, state-of-the-art learners have two well-known lim-itations: scaling to large knowledge graphs and handling missing infor-mation. Here, we present a decision-tree-based learner (tDL) to learn Web Ontology Languages (OWLs) class expressions over large knowl-edge graphs, while imputing missing triples. Given positive and negative example individuals, tDL firstly constructs unique OWL expressions in .SHOIN from concise bounded descriptions of individuals. Each OWL class expression is used as a feature in a binary classification problem to represent input individuals. Thereafter, tDL fits a CART decision tree to learn Boolean decision rules distinguishing positive examples from nega-tive examples. A final OWL expression in.SHOIN is built by traversing the built CART decision tree from the root node to leaf nodes for each positive example. By this, tDL can learn OWL class expressions without exploration, i.e., the number of queries to a knowledge graph is bounded by the number of input individuals. Our empirical results show that tDL outperforms the current state-of-the-art models across datasets. Impor-tantly, our experiments over a large knowledge graph (DBpedia with 1.1 billion triples) show that tDL can effectively learn accurate OWL class expressions, while the state-of-the-art models fail to return any results. Finally, expressions learned by tDL can be seamlessly translated into natural language explanations using a pre-trained large language model and a DL verbalizer.}}, author = {{Demir, Caglar and Yekini, Moshood and Röder, Michael and Mahmood, Yasir and Ngonga Ngomo, Axel-Cyrille}}, booktitle = {{Lecture Notes in Computer Science}}, isbn = {{9783032060655}}, issn = {{0302-9743}}, keywords = {{Decision Tree, OWL Class Expression Learning, Description Logic, Knowledge Graph, Large Language Model, Verbalizer}}, location = {{Porto, Portugal}}, publisher = {{Springer Nature Switzerland}}, title = {{{Tree-Based OWL Class Expression Learner over Large Graphs}}}, doi = {{10.1007/978-3-032-06066-2_29}}, year = {{2025}}, } @inbook{63507, author = {{Pandit, Gaurav and Röder, Michael and Ngonga Ngomo, Axel-Cyrille}}, booktitle = {{Lecture Notes in Computer Science}}, isbn = {{9783031945748}}, issn = {{0302-9743}}, publisher = {{Springer Nature Switzerland}}, title = {{{Evaluating Approximate Nearest Neighbour Search Systems on Knowledge Graph Embeddings}}}, doi = {{10.1007/978-3-031-94575-5_4}}, year = {{2025}}, } @inproceedings{63572, author = {{Demir, Caglar and Yekini, Moshood Olawale and Röder, Michael and Mahmood, Yasir and Ngonga Ngomo, Axel-Cyrille}}, booktitle = {{Lecture Notes in Computer Science}}, isbn = {{9783032060655}}, issn = {{0302-9743}}, location = {{Porto}}, publisher = {{Springer Nature Switzerland}}, title = {{{Tree-Based OWL Class Expression Learner over Large Graphs}}}, doi = {{10.1007/978-3-032-06066-2_29}}, year = {{2025}}, } @inproceedings{63575, author = {{Kapoor, Sourabh and Sharma, Arnab and Röder, Michael and Demir, Caglar and Ngonga Ngomo, Axel-Cyrille}}, booktitle = {{Lecture Notes in Computer Science}}, isbn = {{9783031945748}}, issn = {{0302-9743}}, publisher = {{Springer Nature Switzerland}}, title = {{{Robustness Evaluation of Knowledge Graph Embedding Models Under Non-targeted Attacks}}}, doi = {{10.1007/978-3-031-94575-5_15}}, year = {{2025}}, } @inproceedings{63573, author = {{Memariani, Adel and Röder, Michael and Sharma, Arnab and Demir, Caglar and Ngonga Ngomo, Axel-Cyrille}}, booktitle = {{Lecture Notes in Computer Science}}, isbn = {{9783032095268}}, issn = {{0302-9743}}, publisher = {{Springer Nature Switzerland}}, title = {{{Link Prediction Under Non-targeted Attacks: Do Soft Labels Always Help?}}}, doi = {{10.1007/978-3-032-09527-5_6}}, year = {{2025}}, } @inproceedings{60497, abstract = {{Despite the advantages that the virtual knowledge graph paradigm has brought to many application domains, state-of-the-art systems still do not support popular graph database management systems like Neo4j. Their query rewriting algorithms focus on languages like conjunctive queries and their unions, which were developed for relational data and are poorly suited for graph data. Moreover, they also limit the expressiveness of the ontology languages that admit rewritings, restricting them to those that enjoy the so-called FO-rewritability property. Rewritings have thus focused on the DL-Lite family of Description Logics. In this paper, we propose a technique for rewriting a family of navigational queries for a suitably tailored fragment of ELHI. Leveraging navigational features in the target query language, we can include some widely-used axiom shapes not supported by DL-Lite. We implemented a proof-of-concept prototype that rewrites into Cypher queries, and tested it on a real-world cognitive neuroscience use case with promising results.}}, author = {{Löhnert, Bianca and Augsten, Nikolaus and Okulmus, Cem and Ortiz, Magdalena}}, booktitle = {{The Semantic Web - 22nd European Semantic Web Conference, {ESWC} 2025, Portoroz, Slovenia, June 1-5, 2025, Proceedings, Part {I}}}, isbn = {{9783031945748}}, issn = {{0302-9743}}, keywords = {{Ontology-based Data Access, Property Graphs, Navigational Queries}}, location = {{Portorož, Slovenia}}, pages = {{342----361}}, publisher = {{Springer Nature Switzerland}}, title = {{{Towards Practicable Algorithms for Rewriting Graph Queries Beyond DL-Lite}}}, doi = {{10.1007/978-3-031-94575-5_19}}, volume = {{15718}}, year = {{2025}}, }