@inbook{61210, abstract = {{Knowledge graphs (KGs) differ significantly over multiple different versions of the same data source. They also often contain blank nodes that do not have a constant identifier over all versions. Linking such blank nodes from different versions is a challenging task. Previous works propose different approaches to create signatures for all blank nodes based on named nodes in their neighborhood to match blank nodes with similar signatures. However, these works struggle to find a good mapping when the difference between the KGs’ versions grows too large. In this work, we propose Blink, an embedding-based approach for blank node linking. Blink merges two KGs’ versions and embeds the merged graph into a latent vector space based on translational embeddings and subsequently matches the closest pairs of blank nodes from different graphs. We evaluate our approach using real-world datasets against state-of-the-art approaches by computing the blank node matching for isomorphic graphs and graphs that contain triple changes (i.e., added or removed triples). The results indicate that Blink achieves perfect accuracy for isomorphic graphs. For graph versions that contain changes, such as having up to 20% of triples removed in one version, Blink still produces a mapping with an Optimal Mapping Deviation Ratio of under 1%. These results show that Blink leads to a better linking of KGs over different versions and similar graphs adhering to the linked data guidelines.}}, author = {{Becker, Alexander and Sherif, Mohamed and Ngonga Ngomo, Axel-Cyrille}}, booktitle = {{Lecture Notes in Computer Science}}, isbn = {{9783031778438}}, issn = {{0302-9743}}, location = {{Baltimore, USA}}, publisher = {{Springer Nature Switzerland}}, title = {{{Blink: Blank Node Matching Using Embeddings}}}, doi = {{10.1007/978-3-031-77844-5_12}}, year = {{2024}}, } @inproceedings{54084, author = {{Karalis, Nikolaos and Bigerl, Alexander and Heidrich, Liss and Sherif, Mohamed and Ngonga Ngomo, Axel-Cyrille}}, booktitle = {{ESWC}}, keywords = {{bigerl dice enexa heidrich karalis ngonga sail sherif}}, title = {{{Efficient Evaluation of Conjunctive Regular Path Queries Using Multi-way Joins}}}, year = {{2024}}, } @inproceedings{61219, author = {{Kumar, Ajay and Naumann, Marius and Henne, Kevin and Sherif, Mohamed}}, booktitle = {{Joint Proceedings of Posters, Demos, Workshops, and Tutorials of the 20th International Conference on Semantic Systems co-located with 20th International Conference on Semantic Systems (SEMANTiCS 2024), Amsterdam, The Netherlands, September 17-19, 2024}}, editor = {{Garijo, Daniel and Gentile, Anna Lisa and Kurteva, Anelia and Mannocci, Andrea and Osborne, Francesco and Vahdati, Sahar}}, keywords = {{kumar sherif enexa climatebowl ingrid simba dice whale}}, location = {{ Amsterdam,The Netherlands}}, publisher = {{CEUR-WS.org}}, title = {{{PCFWebUI: Data-driven WebUI for holistic decarbonization based on PCF-Tracking}}}, volume = {{3759}}, year = {{2024}}, } @book{61182, editor = {{Herzig, Bardo and Eickelmann, Birgit and Schwabl, Franziska and Schulze, Johanna and Niemann, Jan}}, isbn = {{978-3-8309-4837-7}}, issn = {{2944-6791}}, pages = {{285}}, publisher = {{Waxmann}}, title = {{{Lehrkräftebildung in der digitalen Welt. Zukunftsorientierte Forschungs- und Praxisperspektiven }}}, doi = {{10.31244/9783830998372}}, volume = {{1}}, year = {{2024}}, } @book{61186, editor = {{Herzig, Bardo and Eickelmann, Birgit and Schwabl, Franziska and Schulze, Johanna and Niemann, Jan}}, isbn = {{978-3-8309-4837-7}}, issn = {{2944-6791}}, pages = {{285}}, publisher = {{Waxmann}}, title = {{{Lehrkräftebildung in der digitalen Welt. Zukunftsorientierte Forschungs- und Praxisperspektiven}}}, volume = {{1}}, year = {{2024}}, } @inproceedings{55094, author = {{Zahera, Hamada Mohamed Abdelsamee and Manzoor, Ali and Sherif, Mohamed and Moussallem, Diego and Ngonga Ngomo, Axel-Cyrille}}, booktitle = {{SEMANTiCS}}, keywords = {{TRR318 climatebowl colide dice enexa kiam manzoor moussallem ngonga sailproject sherif simba zahera}}, title = {{{Generating SPARQL from Natural Language Using Chain-of-Thoughts Prompting}}}, year = {{2024}}, } @article{56190, abstract = {{This study investigates the potential of using advanced conversational artificial intelligence (AI) to help people understand complex AI systems. In line with conversation-analytic research, we view the participatory role of AI as dynamically unfolding in a situation rather than being predetermined by its architecture. To study user sensemaking of intransparent AI systems, we set up a naturalistic encounter between human participants and two AI systems developed in-house: a reinforcement learning simulation and a GPT-4-based explainer chatbot. Our results reveal that an explainer-AI only truly functions as such when participants actively engage with it as a co-constructive agent. Both the interface’s spatial configuration and the asynchronous temporal nature of the explainer AI – combined with the users’ presuppositions about its role – contribute to the decision whether to treat the AI as a dialogical co-participant in the interaction. Participants establish evidentiality conventions and sensemaking procedures that may diverge from a system’s intended design or function.}}, author = {{Klowait, Nils and Erofeeva, Maria and Lenke, Michael and Horwath, Ilona and Buschmeier, Hendrik}}, journal = {{Discourse & Communication}}, number = {{6}}, pages = {{917--930}}, publisher = {{Sage}}, title = {{{Can AI explain AI? Interactive co-construction of explanations among human and artificial agents}}}, doi = {{10.1177/17504813241267069}}, volume = {{18}}, year = {{2024}}, } @inproceedings{58224, author = {{Kenneweg, Philip and Kenneweg, Tristan and Fumagalli, Fabian and Hammer, Barbara}}, booktitle = {{2024 International Joint Conference on Neural Networks (IJCNN)}}, keywords = {{Training, Schedules, Codes, Search methods, Source coding, Computer architecture, Transformers}}, pages = {{1--8}}, title = {{{No learning rates needed: Introducing SALSA - Stable Armijo Line Search Adaptation}}}, doi = {{10.1109/IJCNN60899.2024.10650124}}, year = {{2024}}, } @inproceedings{53073, abstract = {{While shallow decision trees may be interpretable, larger ensemble models like gradient-boosted trees, which often set the state of the art in machine learning problems involving tabular data, still remain black box models. As a remedy, the Shapley value (SV) is a well-known concept in explainable artificial intelligence (XAI) research for quantifying additive feature attributions of predictions. The model-specific TreeSHAP methodology solves the exponential complexity for retrieving exact SVs from tree-based models. Expanding beyond individual feature attribution, Shapley interactions reveal the impact of intricate feature interactions of any order. In this work, we present TreeSHAP-IQ, an efficient method to compute any-order additive Shapley interactions for predictions of tree-based models. TreeSHAP-IQ is supported by a mathematical framework that exploits polynomial arithmetic to compute the interaction scores in a single recursive traversal of the tree, akin to Linear TreeSHAP. We apply TreeSHAP-IQ on state-of-the-art tree ensembles and explore interactions on well-established benchmark datasets.}}, author = {{Muschalik, Maximilian and Fumagalli, Fabian and Hammer, Barbara and Huellermeier, Eyke}}, booktitle = {{Proceedings of the AAAI Conference on Artificial Intelligence (AAAI)}}, issn = {{2374-3468}}, keywords = {{Explainable Artificial Intelligence}}, number = {{13}}, pages = {{14388--14396}}, title = {{{Beyond TreeSHAP: Efficient Computation of Any-Order Shapley Interactions for Tree Ensembles}}}, doi = {{10.1609/aaai.v38i13.29352}}, volume = {{38}}, year = {{2024}}, } @inproceedings{55311, abstract = {{Addressing the limitations of individual attribution scores via the Shapley value (SV), the field of explainable AI (XAI) has recently explored intricate interactions of features or data points. In particular, extensions of the SV, such as the Shapley Interaction Index (SII), have been proposed as a measure to still benefit from the axiomatic basis of the SV. However, similar to the SV, their exact computation remains computationally prohibitive. Hence, we propose with SVARM-IQ a sampling-based approach to efficiently approximate Shapley-based interaction indices of any order. SVARM-IQ can be applied to a broad class of interaction indices, including the SII, by leveraging a novel stratified representation. We provide non-asymptotic theoretical guarantees on its approximation quality and empirically demonstrate that SVARM-IQ achieves state-of-the-art estimation results in practical XAI scenarios on different model classes and application domains.}}, author = {{Kolpaczki, Patrick and Muschalik, Maximilian and Fumagalli, Fabian and Hammer, Barbara and Huellermeier, Eyke}}, booktitle = {{Proceedings of The 27th International Conference on Artificial Intelligence and Statistics (AISTATS)}}, pages = {{3520–3528}}, publisher = {{PMLR}}, title = {{{SVARM-IQ: Efficient Approximation of Any-order Shapley Interactions through Stratification}}}, volume = {{238}}, year = {{2024}}, } @inproceedings{58223, abstract = {{The Shapley value (SV) is a prevalent approach of allocating credit to machine learning (ML) entities to understand black box ML models. Enriching such interpretations with higher-order interactions is inevitable for complex systems, where the Shapley Interaction Index (SII) is a direct axiomatic extension of the SV. While it is well-known that the SV yields an optimal approximation of any game via a weighted least square (WLS) objective, an extension of this result to SII has been a long-standing open problem, which even led to the proposal of an alternative index. In this work, we characterize higher-order SII as a solution to a WLS problem, which constructs an optimal approximation via SII and k-Shapley values (k-SII). We prove this representation for the SV and pairwise SII and give empirically validated conjectures for higher orders. As a result, we propose KernelSHAP-IQ, a direct extension of KernelSHAP for SII, and demonstrate state-of-the-art performance for feature interactions.}}, author = {{Fumagalli, Fabian and Muschalik, Maximilian and Kolpaczki, Patrick and Hüllermeier, Eyke and Hammer, Barbara}}, booktitle = {{Proceedings of the 41st International Conference on Machine Learning (ICML)}}, pages = {{14308–14342}}, publisher = {{PMLR}}, title = {{{KernelSHAP-IQ: Weighted Least Square Optimization for Shapley Interactions}}}, volume = {{235}}, year = {{2024}}, } @inproceedings{61228, author = {{Muschalik, Maximilian and Baniecki, Hubert and Fumagalli, Fabian and Kolpaczki, Patrick and Hammer, Barbara and Huellermeier, Eyke}}, booktitle = {{Advances in Neural Information Processing Systems (NeurIPS)}}, pages = {{130324–130357}}, title = {{{shapiq: Shapley interactions for machine learning}}}, volume = {{37}}, year = {{2024}}, } @inproceedings{61230, author = {{Kolpaczki, Patrick and Bengs, Viktor and Muschalik, Maximilian and Hüllermeier, Eyke}}, booktitle = {{Proceedings of the AAAI conference on Artificial Intelligence (AAAI)}}, number = {{12}}, pages = {{13246–13255}}, title = {{{Approximating the shapley value without marginal contributions}}}, volume = {{38}}, year = {{2024}}, } @inproceedings{61180, abstract = {{Starting from the assumption that LLMs are systems bearing only formal but not functional linguistic competence, this short paper explores how the understanding capabilities of LLMs could be implicitly explained based on a “pause and refect” strategy. Specifically, we propose to include a virtual embodied agent in human interactions with LLM-based chatbots. The agent will use air quotes as multimodal metalinguistic markers to explicitly point to those parts of the LLM’s output that are relevant to explaining the LLM’s meaning understanding capabilities. At the same time, by scafolding users to perceive the output as ‘mentioned language’ inferred from a metalinguistic function of multimodal markers, the agent implicitly explains how the meaning of the output should be understood. In this proposal, users will actively participate in the co-construction of the implicit explanation by providing feedback and deciding when and to what extent the agent’s scafold (e.g., the air quotes) is used.}}, author = {{Belosevic, Milena and Buschmeier, Hendrik}}, booktitle = {{ICMI Companion ’24: Companion Proceedings of the 26th International Conference on Multimodal Interaction}}, location = {{San José, Costa Rica}}, pages = {{225–227}}, publisher = {{ACM}}, title = {{{Quote to explain: Using multimodal metalinguistic markers to explain large language models’ understanding capabilities}}}, doi = {{10.1145/3686215.3689203}}, year = {{2024}}, } @inproceedings{61176, abstract = {{We revisit the phenomenon of syntactic complexity convergence in conversational interaction, originally found for English dialogue, which has theoretical implication for dialogical concepts such as mutual understanding. We use a modified metric to quantify syntactic complexity based on dependency parsing. The results show that syntactic complexity convergence can be statistically confirmed in one of three selected German datasets that were analysed. Given that the dataset which shows such convergence is much larger than the other two selected datasets, the empirical results indicate a certain degree of linguistic generality of syntactic complexity convergence in conversational interaction. We also found a different type of syntactic complexity convergence in one of the datasets while further investigation is still necessary.}}, author = {{Wang, Yu and Buschmeier, Hendrik}}, booktitle = {{Proceedings of the 20th Conference on Natural Language Processing (KONVENS 2024)}}, location = {{Vienna, Austria}}, pages = {{75–80}}, title = {{{Revisiting the phenomenon of syntactic complexity convergence on German dialogue data}}}, year = {{2024}}, } @inproceedings{55911, abstract = {{According to the Entropy Rate Constancy (ERC) principle, the information density of a text is approximately constant over its length. Whether this principle also applies to nonverbal communication signals is still under investigation. We perform empirical analyses of video-recorded dialogue data and investigate whether listener gaze, as an important nonverbal communication signal, adheres to the ERC principle. Results show (1) that the ERC principle holds for listener gaze; and (2) that the two linguistic factors syntactic complexity and turn transition potential are weakly correlated with local entropy of listener gaze.}}, author = {{Wang, Yu and Xu, Yang and Skantze, Gabriel and Buschmeier, Hendrik}}, booktitle = {{Findings of the Association for Computational Linguistics ACL 2024}}, location = {{Bangkok, Thailand}}, pages = {{3533–3545}}, title = {{{How much does nonverbal communication conform to entropy rate constancy?: A case study on listener gaze in interaction}}}, year = {{2024}}, } @inproceedings{56314, author = {{Riechmann, Alina Naomi and Buschmeier, Hendrik}}, booktitle = {{Book of Abstracts of the 2nd International Multimodal Communication Symposium}}, location = {{Frankfurt am Main, Germany}}, pages = {{38–39}}, title = {{{Automatic reconstruction of dialogue participants’ coordinating gaze behavior from multiple camera perspectives}}}, year = {{2024}}, } @inproceedings{58722, abstract = {{Dialects introduce syntactic and lexical variations in language that occur in regional or social groups. Most NLP methods are not sensitive to such variations. This may lead to unfair behavior of the methods, conveying negative bias towards dialect speakers. While previous work has studied dialect-related fairness for aspects like hate speech, other aspects of biased language, such as lewdness, remain fully unexplored. To fill this gap, we investigate performance disparities between dialects in the detection of five aspects of biased language and how to mitigate them. To alleviate bias, we present a multitask learning approach that models dialect language as an auxiliary task to incorporate syntactic and lexical variations. In our experiments with African-American English dialect, we provide empirical evidence that complementing common learning approaches with dialect modeling improves their fairness. Furthermore, the results suggest that multitask learning achieves state-of-the-art performance and helps to detect properties of biased language more reliably.}}, author = {{Spliethöver, Maximilian and Menon, Sai Nikhil and Wachsmuth, Henning}}, booktitle = {{Findings of the Association for Computational Linguistics: ACL 2024}}, editor = {{Ku, Lun-Wei and Martins, Andre and Srikumar, Vivek}}, pages = {{9294–9313}}, publisher = {{Association for Computational Linguistics}}, title = {{{Disentangling Dialect from Social Bias via Multitask Learning to Improve Fairness}}}, doi = {{10.18653/v1/2024.findings-acl.553}}, year = {{2024}}, } @article{61251, abstract = {{We theoretically investigate strategies for the deterministic creation of trains of time-bin entangled photons using an individual quantum emitter described by a Λ-type electronic system. We explicitly demonstrate the theoretical generation of linear cluster states with substantial numbers of entangled photonic qubits in full microscopic numerical simulations. The underlying scheme is based on the manipulation of ground state coherences through precise optical driving. One important finding is that the most easily accessible quality metrics, the achievable rotation fidelities, fall short in assessing the actual quantum correlations of the emitted photons in the face of losses. To address this, we explicitly calculate stabilizer generator expectation values as a superior gauge for the quantum properties of the generated many-photon state. With widespread applicability in other emitter and excitation–emission schemes also, our work lays the conceptual foundations for an in-depth practical analysis of time-bin entanglement based on full numerical simulations with predictive capabilities for realistic systems and setups, including losses and imperfections. The specific results shown in the present work illustrate that with controlled minimization of losses and realistic system parameters for quantum-dot type systems, useful linear cluster states of significant lengths can be generated in the calculations, discussing the possibility of scalability for quantum information processing endeavors.}}, author = {{Bauch, David and Köcher, Nikolas and Heinisch, Nils and Schumacher, Stefan}}, issn = {{2835-0103}}, journal = {{APL Quantum}}, number = {{3}}, publisher = {{AIP Publishing}}, title = {{{Time-bin entanglement in the deterministic generation of linear photonic cluster states}}}, doi = {{10.1063/5.0214197}}, volume = {{1}}, year = {{2024}}, } @article{61250, author = {{Bennenhei, Christoph and Shan, Hangyong and Struve, Marti and Kunte, Nils and Eilenberger, Falk and Ohmer, Jürgen and Fischer, Utz and Schumacher, Stefan and Ma, Xuekai and Schneider, Christian and Esmann, Martin}}, issn = {{2330-4022}}, journal = {{ACS Photonics}}, number = {{8}}, pages = {{3046--3054}}, publisher = {{American Chemical Society (ACS)}}, title = {{{Organic Room-Temperature Polariton Condensate in a Higher-Order Topological Lattice}}}, doi = {{10.1021/acsphotonics.4c00268}}, volume = {{11}}, year = {{2024}}, }