{"editor":[{"first_name":"Catia","full_name":"Pesquita, Catia","last_name":"Pesquita"},{"full_name":"Jimenez-Ruiz, Ernesto","last_name":"Jimenez-Ruiz","first_name":"Ernesto"},{"last_name":"McCusker","full_name":"McCusker, Jamie","first_name":"Jamie"},{"last_name":"Faria","full_name":"Faria, Daniel","first_name":"Daniel"},{"last_name":"Dragoni","full_name":"Dragoni, Mauro","first_name":"Mauro"},{"full_name":"Dimou, Anastasia","last_name":"Dimou","first_name":"Anastasia"},{"first_name":"Raphael","full_name":"Troncy, Raphael","last_name":"Troncy"},{"first_name":"Sven","full_name":"Hertling, Sven","last_name":"Hertling"}],"project":[{"_id":"410","name":"KnowGraphs: KnowGraphs: Knowledge Graphs at Scale"},{"name":"ENEXA: Efficient Explainable Learning on Knowledge Graphs","_id":"407","grant_number":"101070305"},{"name":"SAIL: SAIL: SustAInable Life-cycle of Intelligent Socio-Technical Systems","_id":"285","grant_number":"NW21-059D"}],"doi":"https://doi.org/10.1007/978-3-031-33455-9_13","citation":{"ama":"KOUAGOU NJ, Heindorf S, Demir C, Ngonga Ngomo A-C. Neural Class Expression Synthesis. In: Pesquita C, Jimenez-Ruiz E, McCusker J, et al., eds. <i>The Semantic Web - 20th Extended Semantic Web Conference (ESWC 2023)</i>. Vol 13870. Springer International Publishing; 2023:209-226. doi:<a href=\"https://doi.org/10.1007/978-3-031-33455-9_13\">https://doi.org/10.1007/978-3-031-33455-9_13</a>","short":"N.J. KOUAGOU, S. Heindorf, C. Demir, A.-C. Ngonga Ngomo, in: C. Pesquita, E. Jimenez-Ruiz, J. McCusker, D. Faria, M. Dragoni, A. Dimou, R. Troncy, S. Hertling (Eds.), The Semantic Web - 20th Extended Semantic Web Conference (ESWC 2023), Springer International Publishing, 2023, pp. 209–226.","chicago":"KOUAGOU, N’Dah Jean, Stefan Heindorf, Caglar Demir, and Axel-Cyrille Ngonga Ngomo. “Neural Class Expression Synthesis.” In <i>The Semantic Web - 20th Extended Semantic Web Conference (ESWC 2023)</i>, edited by Catia Pesquita, Ernesto Jimenez-Ruiz, Jamie McCusker, Daniel Faria, Mauro Dragoni, Anastasia Dimou, Raphael Troncy, and Sven Hertling, 13870:209–26. Springer International Publishing, 2023. <a href=\"https://doi.org/10.1007/978-3-031-33455-9_13\">https://doi.org/10.1007/978-3-031-33455-9_13</a>.","ieee":"N. J. KOUAGOU, S. Heindorf, C. Demir, and A.-C. Ngonga Ngomo, “Neural Class Expression Synthesis,” in <i>The Semantic Web - 20th Extended Semantic Web Conference (ESWC 2023)</i>, Hersonissos, Crete, Greece, 2023, vol. 13870, pp. 209–226, doi: <a href=\"https://doi.org/10.1007/978-3-031-33455-9_13\">https://doi.org/10.1007/978-3-031-33455-9_13</a>.","apa":"KOUAGOU, N. J., Heindorf, S., Demir, C., &#38; Ngonga Ngomo, A.-C. (2023). Neural Class Expression Synthesis. In C. Pesquita, E. Jimenez-Ruiz, J. McCusker, D. Faria, M. Dragoni, A. Dimou, R. Troncy, &#38; S. Hertling (Eds.), <i>The Semantic Web - 20th Extended Semantic Web Conference (ESWC 2023)</i> (Vol. 13870, pp. 209–226). Springer International Publishing. <a href=\"https://doi.org/10.1007/978-3-031-33455-9_13\">https://doi.org/10.1007/978-3-031-33455-9_13</a>","bibtex":"@inproceedings{KOUAGOU_Heindorf_Demir_Ngonga Ngomo_2023, title={Neural Class Expression Synthesis}, volume={13870}, DOI={<a href=\"https://doi.org/10.1007/978-3-031-33455-9_13\">https://doi.org/10.1007/978-3-031-33455-9_13</a>}, booktitle={The Semantic Web - 20th Extended Semantic Web Conference (ESWC 2023)}, publisher={Springer International Publishing}, author={KOUAGOU, N’Dah Jean and Heindorf, Stefan and Demir, Caglar and Ngonga Ngomo, Axel-Cyrille}, 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}, year={2023}, pages={209–226} }","mla":"KOUAGOU, N’Dah Jean, et al. “Neural Class Expression Synthesis.” <i>The Semantic Web - 20th Extended Semantic Web Conference (ESWC 2023)</i>, edited by Catia Pesquita et al., vol. 13870, Springer International Publishing, 2023, pp. 209–26, doi:<a href=\"https://doi.org/10.1007/978-3-031-33455-9_13\">https://doi.org/10.1007/978-3-031-33455-9_13</a>."},"publication":"The Semantic Web - 20th Extended Semantic Web Conference (ESWC 2023)","department":[{"_id":"574"},{"_id":"760"}],"publisher":"Springer International Publishing","conference":{"name":"20th Extended Semantic Web Conference","location":"Hersonissos, Crete, Greece","end_date":"2023-06-01","start_date":"2023-05-28"},"oa":"1","author":[{"full_name":"KOUAGOU, N'Dah Jean","last_name":"KOUAGOU","id":"87189","first_name":"N'Dah Jean"},{"first_name":"Stefan","id":"11871","orcid":"0000-0002-4525-6865","last_name":"Heindorf","full_name":"Heindorf, Stefan"},{"id":"43817","first_name":"Caglar","full_name":"Demir, Caglar","last_name":"Demir"},{"first_name":"Axel-Cyrille","id":"65716","last_name":"Ngonga Ngomo","full_name":"Ngonga Ngomo, Axel-Cyrille"}],"external_id":{"unknown":["https://link.springer.com/chapter/10.1007/978-3-031-33455-9_13"]},"main_file_link":[{"url":"https://2023.eswc-conferences.org/wp-content/uploads/2023/05/paper_Kouagou_2023_Neural.pdf","open_access":"1"}],"volume":13870,"publication_identifier":{"unknown":["978-3-031-33455-9"]},"date_created":"2022-10-15T19:20:11Z","date_updated":"2023-07-02T18:10:02Z","status":"public","_id":"33734","page":"209 - 226","abstract":[{"lang":"eng","text":"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"}],"intvolume":"     13870","type":"conference","language":[{"iso":"eng"}],"user_id":"11871","keyword":["Neural network","Concept learning","Description logics"],"publication_status":"published","title":"Neural Class Expression Synthesis","year":"2023"}