@inbook{62702,
  abstract     = {{<jats:p>Clifford algebras are a natural extension of division algebras, including real numbers, complex numbers, quaternions, and octonions. Previous research in knowledge graph embeddings has focused exclusively on Clifford algebras of a specific type, which do not include nilpotent base vectors—elements that square to zero. In this work, we introduce a novel approach by incorporating nilpotent base vectors with a nilpotency index of two, leading to a more general form of Clifford algebras named degenerate Clifford algebras. This generalization to degenerate Clifford algebras does allow for covering dual numbers and as such include translations and rotations models under the same generalization paradigm for the first time. We develop two models to determine the parameters that define the algebra: one using a greedy search and another predicting the parameters based on neural network embeddings of the input knowledge graph. Our evaluation on seven benchmark datasets demonstrates that this incorporation of nilpotent vectors enhances the quality of embeddings. Additionally, our method outperforms state-of-the-art approaches in terms of generalization, particularly regarding the mean reciprocal rank achieved on validation data. Finally, we show that even a simple greedy search can effectively discover optimal or near-optimal parameters for the algebra.</jats:p>}},
  author       = {{Kamdem Teyou, Louis Mozart and Demir, Caglar and Ngonga Ngomo, Axel-Cyrille}},
  booktitle    = {{Frontiers in Artificial Intelligence and Applications}},
  isbn         = {{9781643685489}},
  issn         = {{0922-6389}},
  location     = {{Santiago de Compostela}},
  publisher    = {{IOS Press}},
  title        = {{{Embedding Knowledge Graphs in Degenerate Clifford Algebras}}},
  doi          = {{10.3233/faia240627}},
  year         = {{2024}},
}

@inproceedings{62703,
  abstract     = {{We introduce a novel embedding method diverging from conventional approaches by operating within function spaces of finite dimension rather than finite vector space, thus departing significantly from standard knowledge graph embedding techniques. Initially employing polynomial functions to compute embeddings, we progress to more intricate representations using neural networks with varying layer complexities. We argue that employing functions for embedding computation enhances expressiveness and allows for more degrees of freedom, enabling operations such as composition, derivatives and primitive of entities representation. Additionally, we meticulously outline the step-by-step construction of our approach and provide code for reproducibility, thereby facilitating further exploration and application in the field.}},
  author       = {{Kamdem Teyou, Louis Mozart and Demir, Caglar and Ngonga Ngomo, Axel-Cyrille}},
  booktitle    = {{Proceedings of the 33rd ACM International Conference on Information and Knowledge Management}},
  location     = {{Boise}},
  publisher    = {{ACM}},
  title        = {{{Embedding Knowledge Graphs in Function Spaces}}},
  doi          = {{10.1145/3627673.3679819}},
  year         = {{2024}},
}

@inbook{46460,
  author       = {{Ngonga Ngomo, Axel-Cyrille and Demir, Caglar and Kouagou, N'Dah Jean and Heindorf, Stefan and Karalis, Nikoloas and Bigerl, Alexander}},
  booktitle    = {{Compendium of Neurosymbolic Artificial Intelligence}},
  pages        = {{272–286}},
  publisher    = {{IOS Press}},
  title        = {{{Class Expression Learning with Multiple Representations}}},
  year         = {{2023}},
}

@article{46248,
  author       = {{Demir, Caglar and Wiebesiek, Michel and Lu, Renzhong and Ngonga Ngomo, Axel-Cyrille and Heindorf, Stefan}},
  journal      = {{ECML PKDD}},
  location     = {{Torino}},
  title        = {{{LitCQD: Multi-Hop Reasoning in Incomplete Knowledge Graphs with Numeric Literals}}},
  year         = {{2023}},
}

@inbook{47421,
  abstract     = {{Class expression learning in description logics has long been regarded as an iterative search problem in an infinite conceptual space. Each iteration of the search process invokes a reasoner and a heuristic function. The reasoner finds the instances of the current expression, and the heuristic function computes the information gain and decides on the next step to be taken. As the size of the background knowledge base grows, search-based approaches for class expression learning become prohibitively slow. Current neural class expression synthesis (NCES) approaches investigate the use of neural networks for class expression learning in the attributive language with complement (ALC). While they show significant improvements over search-based approaches in runtime and quality of the computed solutions, they rely on the availability of pretrained embeddings for the input knowledge base. Moreover, they are not applicable to ontologies in more expressive description logics. In this paper, we propose a novel NCES approach which extends the state of the art to the description logic ALCHIQ(D). Our extension, dubbed NCES2, comes with an improved training data generator and does not require pretrained embeddings for the input knowledge base as both the embedding model and the class expression synthesizer are trained jointly. Empirical results on benchmark datasets suggest that our approach inherits the scalability capability of current NCES instances with the additional advantage that it supports more complex learning problems. NCES2 achieves the highest performance overall when compared to search-based approaches and to its predecessor NCES. We provide our source code, datasets, and pretrained models at https://github.com/dice-group/NCES2.}},
  author       = {{Kouagou, N'Dah Jean and Heindorf, Stefan and Demir, Caglar and Ngonga Ngomo, Axel-Cyrille}},
  booktitle    = {{Machine Learning and Knowledge Discovery in Databases: Research Track}},
  isbn         = {{9783031434204}},
  issn         = {{0302-9743}},
  location     = {{Turin}},
  publisher    = {{Springer Nature Switzerland}},
  title        = {{{Neural Class Expression Synthesis in ALCHIQ(D)}}},
  doi          = {{10.1007/978-3-031-43421-1_12}},
  year         = {{2023}},
}

@unpublished{37937,
  abstract     = {{Knowledge bases are widely used for information management on the web,
enabling high-impact applications such as web search, question answering, and
natural language processing. They also serve as the backbone for automatic
decision systems, e.g. for medical diagnostics and credit scoring. As
stakeholders affected by these decisions would like to understand their
situation and verify fair decisions, a number of explanation approaches have
been proposed using concepts in description logics. However, the learned
concepts can become long and difficult to fathom for non-experts, even when
verbalized. Moreover, long concepts do not immediately provide a clear path of
action to change one's situation. Counterfactuals answering the question "How
must feature values be changed to obtain a different classification?" have been
proposed as short, human-friendly explanations for tabular data. In this paper,
we transfer the notion of counterfactuals to description logics and propose the
first algorithm for generating counterfactual explanations in the description
logic $\mathcal{ELH}$. Counterfactual candidates are generated from concepts
and the candidates with fewest feature changes are selected as counterfactuals.
In case of multiple counterfactuals, we rank them according to the likeliness
of their feature combinations. For evaluation, we conduct a user survey to
investigate which of the generated counterfactual candidates are preferred for
explanation by participants. In a second study, we explore possible use cases
for counterfactual explanations.}},
  author       = {{Sieger, Leonie Nora and Heindorf, Stefan and Blübaum, Lukas and Ngonga Ngomo, Axel-Cyrille}},
  booktitle    = {{arXiv:2301.05109}},
  title        = {{{Explaining ELH Concept Descriptions through Counterfactual Reasoning}}},
  year         = {{2023}},
}

@inproceedings{50797,
  author       = {{Röder, Michael and Kuchelev, Denis and Ngonga Ngomo, Axel-Cyrille}},
  booktitle    = {{Knowledge Graphs and Semantic Web}},
  editor       = {{Ortiz-Rodriguez, Fernando and Villazón-Terrazas, Boris and Tiwari, Sanju and Bobed, Carlos}},
  isbn         = {{978-3-031-47745-4}},
  keywords     = {{sail dice roeder kuchelev ngonga}},
  pages        = {{183–198}},
  publisher    = {{Springer Nature Switzerland}},
  title        = {{{A Topic Model for the Data Web}}},
  doi          = {{10.1007/978-3-031-47745-4_14}},
  year         = {{2023}},
}

@inbook{46516,
  abstract     = {{Linked knowledge graphs build the backbone of many data-driven applications such as search engines, conversational agents and e-commerce solutions. Declarative link discovery frameworks use complex link specifications to express the conditions under which a link between two resources can be deemed to exist. However, understanding such complex link specifications is a challenging task for non-expert users of link discovery frameworks. In this paper, we address this drawback by devising NMV-LS, a language model-based verbalization approach for translating complex link specifications into natural language. NMV-LS relies on the results of rule-based link specification verbalization to apply continuous training on T5, a large language model based on the Transformerarchitecture. We evaluated NMV-LS on English and German datasets using well-known machine translation metrics such as BLUE, METEOR, ChrF++ and TER. Our results suggest that our approach achieves a verbalization performance close to that of humans and outperforms state of the art approaches. Our source code and datasets are publicly available at https://github.com/dice-group/NMV-LS.}},
  author       = {{Ahmed, Abdullah Fathi Ahmed and Firmansyah, Asep Fajar and Sherif, Mohamed and Moussallem, Diego and Ngonga Ngomo, Axel-Cyrille}},
  booktitle    = {{Natural Language Processing and Information Systems}},
  isbn         = {{9783031353192}},
  issn         = {{0302-9743}},
  publisher    = {{Springer Nature Switzerland}},
  title        = {{{Explainable Integration of Knowledge Graphs Using Large Language Models}}},
  doi          = {{10.1007/978-3-031-35320-8_9}},
  year         = {{2023}},
}

@inproceedings{54581,
  author       = {{Manzoor, Ali and Saleem, Muhammad and Ngonga Ngomo, Axel-Cyrille}},
  booktitle    = {{2023 IEEE 17th International Conference on Semantic Computing (ICSC)}},
  pages        = {{274–277}},
  title        = {{{Unsupervised Relation Extraction with Sentence level Distributional Semantics}}},
  year         = {{2023}},
}

@phdthesis{54607,
  author       = {{Röder, Michael}},
  keywords     = {{dice roeder}},
  publisher    = {{Paderborn University}},
  title        = {{{Automating the Discovery of Linking Candidates}}},
  doi          = {{10.17619/UNIPB/1-1666}},
  year         = {{2023}},
}

@inproceedings{54612,
  author       = {{KOUAGOU, N'Dah Jean and Heindorf, Stefan and Demir, Caglar and Ngonga Ngomo, Axel-Cyrille}},
  booktitle    = {{NeSy 2023, 17th International Workshop on Neural-Symbolic Learning and Reasoning, Certosa di Pontignano, Siena, Italy}},
  keywords     = {{318 SFB-TRR demir dice enexa heindorf knowgraphs kouagou ngonga sail}},
  publisher    = {{CEUR-WS}},
  title        = {{{Neural Class Expression Synthesis (Extended Abstract)}}},
  year         = {{2023}},
}

@inproceedings{54608,
  author       = {{Zahera, Hamada Mohamed Abdelsamee and Vitiugin, Fedor and Sherif, Mohamed and Castillo, Carlos and Ngonga Ngomo, Axel-Cyrille}},
  booktitle    = {{SEMANTiCS}},
  keywords     = {{dice kiam ngonga porque sherif zahera}},
  title        = {{{Using Pre-trained Language Models for Abstractive DBPEDIA Summarization: A Comparative Study}}},
  year         = {{2023}},
}

@inproceedings{54610,
  author       = {{Wilke, Adrian and Ngonga Ngomo, Axel-Cyrille}},
  booktitle    = {{The Semantic Web (ESWC 2023)}},
  keywords     = {{dice eml4u ngonga opal wilke}},
  title        = {{{LauNuts: A Knowledge Graph to identify and compare geographic regions in the European Union}}},
  year         = {{2023}},
}

@inproceedings{54611,
  author       = {{Karalis, Nikolaos and Bigerl, Alexander and Ngonga Ngomo, Axel-Cyrille}},
  booktitle    = {{SEMANTiCS}},
  keywords     = {{bigerl dice enexa karalis knowgraphs ngonga sail}},
  title        = {{{Native Execution of GraphQL Queries over RDF Graphs Using Multi-way Joins}}},
  year         = {{2023}},
}

@article{54577,
  abstract     = {{<jats:p>Argumentation is a well-established formalism dealing with conflicting information by generating and comparing arguments. It has been playing a major role in AI for decades. In logic-based argumentation, we explore the internal structure of an argument. Informally, a set of formulas is the support for a given claim if it is consistent, subset-minimal, and implies the claim. In such a case, the pair of the support and the claim together is called an argument. In this article, we study the propositional variants of the following three computational tasks studied in argumentation: ARG (exists a support for a given claim with respect to a given set of formulas), ARG-Check (is a given set a support for a given claim), and ARG-Rel (similarly as ARG plus requiring an additionally given formula to be contained in the support). ARG-Check is complete for the complexity class DP, and the other two problems are known to be complete for the second level of the polynomial hierarchy (Creignou et al. 2014 and Parson et al., 2003) and, accordingly, are highly intractable. Analyzing the reason for this intractability, we perform a two-dimensional classification: First, we consider all possible propositional fragments of the problem within Schaefer’s framework (STOC 1978) and then study different parameterizations for each of the fragments. We identify a list of reasonable structural parameters (size of the claim, support, knowledge base) that are connected to the aforementioned decision problems. Eventually, we thoroughly draw a fine border of parameterized intractability for each of the problems showing where the problems are fixed-parameter tractable and when this exactly stops. Surprisingly, several cases are of very high intractability (para-NP and beyond).</jats:p>}},
  author       = {{Mahmood, Yasir and Meier, Arne and Schmidt, Johannes}},
  issn         = {{1529-3785}},
  journal      = {{ACM Transactions on Computational Logic}},
  number       = {{3}},
  pages        = {{1--25}},
  publisher    = {{Association for Computing Machinery (ACM)}},
  title        = {{{Parameterized Complexity of Logic-based Argumentation in Schaefer’s Framework}}},
  doi          = {{10.1145/3582499}},
  volume       = {{24}},
  year         = {{2023}},
}

@inproceedings{54578,
  abstract     = {{<jats:p>Argumentation is a well-established formalism for nonmonotonic reasoning and a vibrant area of research in AI. Claim-augmented argumentation frameworks (CAFs) have been introduced to deploy a conclusion-oriented perspective. CAFs expand argumentation frameworks by an additional step which involves retaining claims for an accepted set of arguments. We introduce a novel concept of a justification status for claims, a quantitative measure of extensions supporting a particular claim. The well-studied problems of credulous and skeptical reasoning can then be seen as simply the two endpoints of the spectrum when considered as a justification level of a claim. Furthermore, we explore the parameterized complexity of various reasoning problems for CAFs, including the quantitative reasoning for claim assertions. We begin by presenting a suitable graph representation that includes arguments and their associated claims. Our analysis includes the parameter treewidth, and we present decomposition-guided reductions between reasoning problems in CAF and the validity problem for QBF.</jats:p>}},
  author       = {{Fichte, Johannes K. and Hecher, Markus and Mahmood, Yasir and Meier, Arne}},
  booktitle    = {{Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence}},
  publisher    = {{International Joint Conferences on Artificial Intelligence Organization}},
  title        = {{{Quantitative Reasoning and Structural Complexity for Claim-Centric Argumentation}}},
  doi          = {{10.24963/ijcai.2023/358}},
  year         = {{2023}},
}

@inbook{54579,
  author       = {{Mahmood, Yasir and Virtema, Jonni}},
  booktitle    = {{Logic, Language, Information, and Computation}},
  isbn         = {{9783031397837}},
  issn         = {{0302-9743}},
  publisher    = {{Springer Nature Switzerland}},
  title        = {{{Parameterized Complexity of Propositional Inclusion and Independence Logic}}},
  doi          = {{10.1007/978-3-031-39784-4_17}},
  year         = {{2023}},
}

@inproceedings{54089,
  author       = {{Hannula, Miika and Hirvonen, Minna and Kontinen, Juha and Mahmood, Yasir and Meier, Arne and Virtema, Jonni}},
  booktitle    = {{18th European Conference on Logics in Artificial Intelligence, JELIA 2023, Proceedings}},
  keywords     = {{dice enexa mahmood}},
  title        = {{{Logics with probabilistic team semantics and the Boolean negation}}},
  year         = {{2023}},
}

@inbook{54613,
  author       = {{Hanselle, Jonas Manuel and Hüllermeier, Eyke and Mohr, Felix and Ngonga Ngomo, Axel-Cyrille and Sherif, Mohamed and Tornede, Alexander and Wever, Marcel Dominik}},
  booktitle    = {{On-The-Fly Computing – Individualized IT-services in dynamic markets}},
  editor       = {{Haake, Claus-Jochen and Meyer auf der Heide, Friedhelm and Platzner, Marco and Wachsmuth, Henning and Wehrheim, Heike}},
  keywords     = {{dice ngonga sfb901 sherif}},
  pages        = {{85–104}},
  publisher    = {{Heinz Nixdorf Institut, Universität Paderborn}},
  title        = {{{Configuration and Evaluation}}},
  doi          = {{10.5281/zenodo.8068466}},
  volume       = {{412}},
  year         = {{2023}},
}

@inproceedings{54614,
  author       = {{Srivastava, Nikit and Perevalov, Aleksandr and Kuchelev, Denis and Moussallem, Diego and Ngonga Ngomo, Axel-Cyrille and Both, Andreas}},
  booktitle    = {{Proceedings of the 12th Knowledge Capture Conference 2023, {K-CAP} 2023, Pensacola, FL, USA, December 5-7, 2023}},
  editor       = {{Venable, Kristen Brent and Garijo, Daniel and Jalaian, Brian}},
  keywords     = {{dice kuchelev moussallem ngonga srivastava}},
  pages        = {{122–130}},
  publisher    = {{ACM}},
  title        = {{{Lingua Franca - Entity-Aware Machine Translation Approach for Question Answering over Knowledge Graphs}}},
  doi          = {{10.1145/3587259.3627567}},
  year         = {{2023}},
}