@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}},
}

@inproceedings{33734,
  abstract     = {{Many applications require explainable node classification in knowledge graphs. Towards this end, a popular ``white-box'' approach is class expression learning: Given sets of positive and negative nodes, class expressions in description logics are learned that separate positive from negative nodes. Most existing approaches are search-based approaches generating many candidate class expressions and selecting the best one. However, they often take a long time to find suitable class expressions. In this paper, we cast class expression learning as a translation problem and propose a new family of class expression learning approaches which we dub neural class expression synthesizers. Training examples are ``translated'' into class expressions in a fashion akin to machine translation. Consequently, our synthesizers are not subject to the runtime limitations of search-based approaches. We study three instances of this novel family of approaches based on LSTMs, GRUs, and set transformers, respectively. An evaluation of our approach on four benchmark datasets suggests that it can effectively synthesize high-quality class expressions with respect to the input examples in approximately one second on average. Moreover, a comparison to state-of-the-art approaches suggests that we achieve better F-measures on large datasets. For reproducibility purposes, we provide our implementation as well as pretrained models in our public GitHub repository at https://github.com/dice-group/NeuralClassExpressionSynthesis}},
  author       = {{KOUAGOU, N'Dah Jean and Heindorf, Stefan and Demir, Caglar and Ngonga Ngomo, Axel-Cyrille}},
  booktitle    = {{The Semantic Web - 20th Extended Semantic Web Conference (ESWC 2023)}},
  editor       = {{Pesquita, Catia and Jimenez-Ruiz, Ernesto and McCusker, Jamie and Faria, Daniel and Dragoni, Mauro and Dimou, Anastasia and Troncy, Raphael and Hertling, Sven}},
  keywords     = {{Neural network, Concept learning, Description logics}},
  location     = {{Hersonissos, Crete, Greece}},
  pages        = {{209 -- 226}},
  publisher    = {{Springer International Publishing}},
  title        = {{{Neural Class Expression Synthesis}}},
  doi          = {{https://doi.org/10.1007/978-3-031-33455-9_13}},
  volume       = {{13870}},
  year         = {{2023}},
}

@article{46243,
  author       = {{Demir, Caglar and Ngonga Ngomo, Axel-Cyrille}},
  journal      = {{ECML-PKDD}},
  location     = {{Torino}},
  title        = {{{Clifford Embeddings – A Generalized Approach for Embedding in Normed Algebras}}},
  year         = {{2023}},
}

@article{46251,
  author       = {{Demir, Caglar and Ngonga Ngomo, Axel-Cyrille}},
  journal      = {{International Joint Conference on Artificial Intelligence}},
  location     = {{Macau}},
  title        = {{{Neuro-Symbolic Class Expression Learning}}},
  year         = {{2023}},
}