[{"date_created":"2025-11-28T14:09:17Z","publisher":"Springer Nature Switzerland","title":"Tree-Based OWL Class Expression Learner over Large Graphs","year":"2025","language":[{"iso":"eng"}],"keyword":["Decision Tree","OWL Class Expression Learning","Description Logic","Knowledge Graph","Large Language Model","Verbalizer"],"publication":"Lecture Notes in Computer Science","abstract":[{"lang":"eng","text":"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":[{"first_name":"Caglar","last_name":"Demir","full_name":"Demir, Caglar"},{"first_name":"Moshood","full_name":"Yekini, Moshood","last_name":"Yekini"},{"full_name":"Röder, Michael","last_name":"Röder","first_name":"Michael"},{"full_name":"Mahmood, Yasir","last_name":"Mahmood","first_name":"Yasir"},{"full_name":"Ngonga Ngomo, Axel-Cyrille","last_name":"Ngonga Ngomo","first_name":"Axel-Cyrille"}],"date_updated":"2025-11-28T14:57:39Z","doi":"10.1007/978-3-032-06066-2_29","conference":{"location":"Porto, Portugal","end_date":"2025-09-19","start_date":"2025-09-15","name":"European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases - ECML PKDD"},"publication_status":"published","publication_identifier":{"issn":["0302-9743","1611-3349"],"isbn":["9783032060655","9783032060662"]},"citation":{"ieee":"C. Demir, M. Yekini, M. Röder, Y. Mahmood, and A.-C. Ngonga Ngomo, “Tree-Based OWL Class Expression Learner over Large Graphs,” in <i>Lecture Notes in Computer Science</i>, Cham: Springer Nature Switzerland, 2025.","chicago":"Demir, Caglar, Moshood Yekini, Michael Röder, Yasir Mahmood, and Axel-Cyrille Ngonga Ngomo. “Tree-Based OWL Class Expression Learner over Large Graphs.” In <i>Lecture Notes in Computer Science</i>. Cham: Springer Nature Switzerland, 2025. <a href=\"https://doi.org/10.1007/978-3-032-06066-2_29\">https://doi.org/10.1007/978-3-032-06066-2_29</a>.","ama":"Demir C, Yekini M, Röder M, Mahmood Y, Ngonga Ngomo A-C. Tree-Based OWL Class Expression Learner over Large Graphs. In: <i>Lecture Notes in Computer Science</i>. Springer Nature Switzerland; 2025. doi:<a href=\"https://doi.org/10.1007/978-3-032-06066-2_29\">10.1007/978-3-032-06066-2_29</a>","apa":"Demir, C., Yekini, M., Röder, M., Mahmood, Y., &#38; Ngonga Ngomo, A.-C. (2025). Tree-Based OWL Class Expression Learner over Large Graphs. In <i>Lecture Notes in Computer Science</i>. European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases - ECML PKDD, Porto, Portugal. Springer Nature Switzerland. <a href=\"https://doi.org/10.1007/978-3-032-06066-2_29\">https://doi.org/10.1007/978-3-032-06066-2_29</a>","bibtex":"@inbook{Demir_Yekini_Röder_Mahmood_Ngonga Ngomo_2025, place={Cham}, title={Tree-Based OWL Class Expression Learner over Large Graphs}, DOI={<a href=\"https://doi.org/10.1007/978-3-032-06066-2_29\">10.1007/978-3-032-06066-2_29</a>}, booktitle={Lecture Notes in Computer Science}, publisher={Springer Nature Switzerland}, author={Demir, Caglar and Yekini, Moshood and Röder, Michael and Mahmood, Yasir and Ngonga Ngomo, Axel-Cyrille}, year={2025} }","mla":"Demir, Caglar, et al. “Tree-Based OWL Class Expression Learner over Large Graphs.” <i>Lecture Notes in Computer Science</i>, Springer Nature Switzerland, 2025, doi:<a href=\"https://doi.org/10.1007/978-3-032-06066-2_29\">10.1007/978-3-032-06066-2_29</a>.","short":"C. Demir, M. Yekini, M. Röder, Y. Mahmood, A.-C. Ngonga Ngomo, in: Lecture Notes in Computer Science, Springer Nature Switzerland, Cham, 2025."},"place":"Cham","user_id":"114533","department":[{"_id":"34"},{"_id":"574"}],"project":[{"_id":"285","name":"SAIL - Nachhaltiger Lebenszyklus von intelligenten soziotechnischen Systemen"}],"_id":"62701","type":"book_chapter","status":"public"},{"publication":"Proceedings of the 24th International Semantic Web Conference (ISWC 2025)","abstract":[{"lang":"eng","text":"Large Language Models (LLMs) are increasingly deployed in real-world applications that require access to up-to-date knowledge. However, retraining LLMs is computationally expensive. Therefore, knowledge editing techniques are crucial for maintaining current information and correcting erroneous assertions within pre-trained models. Current benchmarks for knowledge editing primarily focus on recalling edited facts, often neglecting their logical consequences. To address this limitation, we introduce a new benchmark designed to evaluate how knowledge editing methods handle the logical consequences of a single fact edit. Our benchmark extracts relevant logical rules from a knowledge graph for a given edit. Then, it generates multi-hop questions based on these rules to assess the impact on logical consequences. Our findings indicate that while existing knowledge editing approaches can accurately insert direct assertions into LLMs, they frequently fail to inject entailed knowledge. Specifically, experiments with popular methods like ROME and FT reveal a substantial performance gap, up to 24%, between evaluations on directly edited knowledge and on entailed knowledge. This highlights the critical need for semantics-aware evaluation frameworks in knowledge editing."}],"keyword":["dice sailproject moteu kouagou zahera ngonga"],"language":[{"iso":"eng"}],"year":"2025","publisher":"Springer, Cham","date_created":"2025-08-27T13:17:55Z","title":"Benchmarking Knowledge Editing using Logical Rules","type":"conference","status":"public","project":[{"_id":"285","name":"SAIL - Nachhaltiger Lebenszyklus von intelligenten soziotechnischen Systemen"}],"_id":"61041","user_id":"99174","department":[{"_id":"574"}],"publication_status":"published","publication_identifier":{"isbn":["978-3-032-09530-5"]},"citation":{"apa":"Moteu Ngoli, T., Kouagou, N. J., Zahera, H. M. A., &#38; Ngonga Ngomo, A.-C. (2025). Benchmarking Knowledge Editing using Logical Rules. <i>Proceedings of the 24th International Semantic Web Conference (ISWC 2025)</i>, pp 41-56. <a href=\"https://doi.org/10.1007/978-3-032-09530-5_3\">https://doi.org/10.1007/978-3-032-09530-5_3</a>","bibtex":"@inproceedings{Moteu Ngoli_Kouagou_Zahera_Ngonga Ngomo_2025, title={Benchmarking Knowledge Editing using Logical Rules}, DOI={<a href=\"https://doi.org/10.1007/978-3-032-09530-5_3\">https://doi.org/10.1007/978-3-032-09530-5_3</a>}, booktitle={Proceedings of the 24th International Semantic Web Conference (ISWC 2025)}, publisher={Springer, Cham}, author={Moteu Ngoli, Tatiana and Kouagou, N’Dah Jean and Zahera, Hamada Mohamed Abdelsamee and Ngonga Ngomo, Axel-Cyrille}, year={2025}, pages={pp 41-56} }","short":"T. Moteu Ngoli, N.J. Kouagou, H.M.A. Zahera, A.-C. Ngonga Ngomo, in: Proceedings of the 24th International Semantic Web Conference (ISWC 2025), Springer, Cham, 2025, p. pp 41-56.","mla":"Moteu Ngoli, Tatiana, et al. “Benchmarking Knowledge Editing Using Logical Rules.” <i>Proceedings of the 24th International Semantic Web Conference (ISWC 2025)</i>, Springer, Cham, 2025, p. pp 41-56, doi:<a href=\"https://doi.org/10.1007/978-3-032-09530-5_3\">https://doi.org/10.1007/978-3-032-09530-5_3</a>.","ama":"Moteu Ngoli T, Kouagou NJ, Zahera HMA, Ngonga Ngomo A-C. Benchmarking Knowledge Editing using Logical Rules. In: <i>Proceedings of the 24th International Semantic Web Conference (ISWC 2025)</i>. Springer, Cham; 2025:pp 41-56. doi:<a href=\"https://doi.org/10.1007/978-3-032-09530-5_3\">https://doi.org/10.1007/978-3-032-09530-5_3</a>","ieee":"T. Moteu Ngoli, N. J. Kouagou, H. M. A. Zahera, and A.-C. Ngonga Ngomo, “Benchmarking Knowledge Editing using Logical Rules,” in <i>Proceedings of the 24th International Semantic Web Conference (ISWC 2025)</i>, Nara, Japan, 2025, p. pp 41-56, doi: <a href=\"https://doi.org/10.1007/978-3-032-09530-5_3\">https://doi.org/10.1007/978-3-032-09530-5_3</a>.","chicago":"Moteu Ngoli, Tatiana, N’Dah Jean Kouagou, Hamada Mohamed Abdelsamee Zahera, and Axel-Cyrille Ngonga Ngomo. “Benchmarking Knowledge Editing Using Logical Rules.” In <i>Proceedings of the 24th International Semantic Web Conference (ISWC 2025)</i>, pp 41-56. Springer, Cham, 2025. <a href=\"https://doi.org/10.1007/978-3-032-09530-5_3\">https://doi.org/10.1007/978-3-032-09530-5_3</a>."},"page":"pp 41-56","date_updated":"2025-12-01T10:04:25Z","author":[{"first_name":"Tatiana","last_name":"Moteu Ngoli","id":"99174","full_name":"Moteu Ngoli, Tatiana"},{"first_name":"N'Dah Jean","id":"87189","full_name":"Kouagou, N'Dah Jean","last_name":"Kouagou"},{"id":"72768","full_name":"Zahera, Hamada Mohamed Abdelsamee","orcid":"0000-0003-0215-1278","last_name":"Zahera","first_name":"Hamada Mohamed Abdelsamee"},{"first_name":"Axel-Cyrille","id":"65716","full_name":"Ngonga Ngomo, Axel-Cyrille","last_name":"Ngonga Ngomo"}],"doi":"https://doi.org/10.1007/978-3-032-09530-5_3","conference":{"start_date":"2025.11.2","name":"The 24th International Semantic Web Conference (ISWC 2025)","location":"Nara, Japan","end_date":"2025.11.6"}},{"main_file_link":[{"url":"https://papers.dice-research.org/2025/KCAP_ASWA/public.pdf"}],"doi":"https://doi.org/10.1145/3731443.3771365","conference":{"name":"Knowledge Capture Conference 2025","start_date":"2025-12-10","end_date":"2025-12-10","location":"Dayton, OH, USA"},"oa":"1","date_updated":"2025-12-04T09:15:07Z","author":[{"full_name":"Sapkota, Rupesh","id":"89326","last_name":"Sapkota","first_name":"Rupesh"},{"first_name":"Caglar","last_name":"Demir","full_name":"Demir, Caglar"},{"last_name":"Sharma","full_name":"Sharma, Arnab","first_name":"Arnab"},{"first_name":"Axel-Cyrille","full_name":"Ngonga Ngomo, Axel-Cyrille","last_name":"Ngonga Ngomo"}],"place":"Dayton, OH, USA","citation":{"ama":"Sapkota R, Demir C, Sharma A, Ngonga Ngomo A-C. Parameter Averaging in Link Prediction. In: <i>Proceedings of the Thirteenth International Conference on Knowledge Capture(K-CAP 2025)</i>. ACM; 2025. doi:<a href=\"https://doi.org/10.1145/3731443.3771365\">https://doi.org/10.1145/3731443.3771365</a>","ieee":"R. Sapkota, C. Demir, A. Sharma, and A.-C. Ngonga Ngomo, “Parameter Averaging in Link Prediction,” presented at the Knowledge Capture Conference 2025, Dayton, OH, USA, 2025, doi: <a href=\"https://doi.org/10.1145/3731443.3771365\">https://doi.org/10.1145/3731443.3771365</a>.","chicago":"Sapkota, Rupesh, Caglar Demir, Arnab Sharma, and Axel-Cyrille Ngonga Ngomo. “Parameter Averaging in Link Prediction.” In <i>Proceedings of the Thirteenth International Conference on Knowledge Capture(K-CAP 2025)</i>. Dayton, OH, USA: ACM, 2025. <a href=\"https://doi.org/10.1145/3731443.3771365\">https://doi.org/10.1145/3731443.3771365</a>.","apa":"Sapkota, R., Demir, C., Sharma, A., &#38; Ngonga Ngomo, A.-C. (2025). Parameter Averaging in Link Prediction. <i>Proceedings of the Thirteenth International Conference on Knowledge Capture(K-CAP 2025)</i>. Knowledge Capture Conference 2025, Dayton, OH, USA. <a href=\"https://doi.org/10.1145/3731443.3771365\">https://doi.org/10.1145/3731443.3771365</a>","short":"R. Sapkota, C. Demir, A. Sharma, A.-C. Ngonga Ngomo, in: Proceedings of the Thirteenth International Conference on Knowledge Capture(K-CAP 2025), ACM, Dayton, OH, USA, 2025.","mla":"Sapkota, Rupesh, et al. “Parameter Averaging in Link Prediction.” <i>Proceedings of the Thirteenth International Conference on Knowledge Capture(K-CAP 2025)</i>, ACM, 2025, doi:<a href=\"https://doi.org/10.1145/3731443.3771365\">https://doi.org/10.1145/3731443.3771365</a>.","bibtex":"@inproceedings{Sapkota_Demir_Sharma_Ngonga Ngomo_2025, place={Dayton, OH, USA}, title={Parameter Averaging in Link Prediction}, DOI={<a href=\"https://doi.org/10.1145/3731443.3771365\">https://doi.org/10.1145/3731443.3771365</a>}, booktitle={Proceedings of the Thirteenth International Conference on Knowledge Capture(K-CAP 2025)}, publisher={ACM}, author={Sapkota, Rupesh and Demir, Caglar and Sharma, Arnab and Ngonga Ngomo, Axel-Cyrille}, year={2025} }"},"has_accepted_license":"1","file_date_updated":"2025-10-28T10:02:13Z","project":[{"name":"SAIL - Nachhaltiger Lebenszyklus von intelligenten soziotechnischen Systemen","_id":"285"}],"_id":"62007","user_id":"89326","department":[{"_id":"574"}],"status":"public","type":"conference","title":"Parameter Averaging in Link Prediction","publisher":"ACM","date_created":"2025-10-28T10:02:40Z","year":"2025","ddc":["000"],"keyword":["Knowledge Graphs","Embeddings","Ensemble Learning"],"language":[{"iso":"eng"}],"abstract":[{"lang":"eng","text":"Ensemble methods are widely employed to improve generalization in machine learning. This has also prompted the adoption of ensemble learning for the knowledge graph embedding (KGE) models in performing link prediction. Typical approaches to this end train multiple models as part of the ensemble, and the diverse predictions are then averaged. However, this approach has some significant drawbacks. For instance, the computational overhead of training multiple models increases latency and memory overhead. In contrast, model merging approaches offer a promising alternative that does not require training multiple models. In this work, we introduce model merging, specifically weighted averaging, in\r\nKGE models. Herein, a running average of model parameters from a training epoch onward is maintained and used for predictions. To address this, we additionally propose an approach that selectively updates the running average of the ensemble model parameters only when the generalization performance improves on a validation dataset. We evaluate these two different weighted averaging approaches on link prediction tasks, comparing the state-of-the-art benchmark ensemble approach. Additionally, we evaluate the weighted averaging approach considering literal-augmented KGE models and multi-hop query answering tasks as well. The results demonstrate that the proposed weighted averaging approach consistently improves performance across diverse evaluation settings."}],"file":[{"relation":"main_file","content_type":"application/pdf","file_size":837462,"file_id":"62008","file_name":"public.pdf","access_level":"open_access","date_updated":"2025-10-28T10:02:13Z","date_created":"2025-10-28T10:02:13Z","creator":"rupezzz"}],"publication":"Proceedings of the Thirteenth International Conference on Knowledge Capture(K-CAP 2025)"},{"place":"Boise, Idaho, USA","year":"2024","citation":{"ieee":"R. Sapkota, D. Köhler, and S. Heindorf, “EDGE: Evaluation Framework for Logical vs. Subgraph Explanations for Node Classifiers on Knowledge Graphs,” presented at the 33rd ACM International Conference on Information and Knowledge Management, Boise, Idaho, USA, 2024, doi: <a href=\"https://doi.org/10.1145/3627673.3679904\">10.1145/3627673.3679904</a>.","chicago":"Sapkota, Rupesh, Dominik Köhler, and Stefan Heindorf. “EDGE: Evaluation Framework for Logical vs. Subgraph Explanations for Node Classifiers on Knowledge Graphs.” In <i>Proceedings of the 33rd ACM International Conference on Information and Knowledge Management (CIKM ’24),</i>. Boise, Idaho, USA: ACM, 2024. <a href=\"https://doi.org/10.1145/3627673.3679904\">https://doi.org/10.1145/3627673.3679904</a>.","ama":"Sapkota R, Köhler D, Heindorf S. EDGE: Evaluation Framework for Logical vs. Subgraph Explanations for Node Classifiers on Knowledge Graphs. In: <i>Proceedings of the 33rd ACM International Conference on Information and Knowledge Management (CIKM ’24),</i>. ACM; 2024. doi:<a href=\"https://doi.org/10.1145/3627673.3679904\">10.1145/3627673.3679904</a>","short":"R. Sapkota, D. Köhler, S. Heindorf, in: Proceedings of the 33rd ACM International Conference on Information and Knowledge Management (CIKM ’24), ACM, Boise, Idaho, USA, 2024.","mla":"Sapkota, Rupesh, et al. “EDGE: Evaluation Framework for Logical vs. Subgraph Explanations for Node Classifiers on Knowledge Graphs.” <i>Proceedings of the 33rd ACM International Conference on Information and Knowledge Management (CIKM ’24),</i> ACM, 2024, doi:<a href=\"https://doi.org/10.1145/3627673.3679904\">10.1145/3627673.3679904</a>.","bibtex":"@inproceedings{Sapkota_Köhler_Heindorf_2024, place={Boise, Idaho, USA}, title={EDGE: Evaluation Framework for Logical vs. Subgraph Explanations for Node Classifiers on Knowledge Graphs}, DOI={<a href=\"https://doi.org/10.1145/3627673.3679904\">10.1145/3627673.3679904</a>}, booktitle={Proceedings of the 33rd ACM International Conference on Information and Knowledge Management (CIKM ’24),}, publisher={ACM}, author={Sapkota, Rupesh and Köhler, Dominik and Heindorf, Stefan}, year={2024} }","apa":"Sapkota, R., Köhler, D., &#38; Heindorf, S. (2024). EDGE: Evaluation Framework for Logical vs. Subgraph Explanations for Node Classifiers on Knowledge Graphs. <i>Proceedings of the 33rd ACM International Conference on Information and Knowledge Management (CIKM ’24),</i>. 33rd ACM International Conference on Information and Knowledge Management, Boise, Idaho, USA. <a href=\"https://doi.org/10.1145/3627673.3679904\">https://doi.org/10.1145/3627673.3679904</a>"},"title":"EDGE: Evaluation Framework for Logical vs. Subgraph Explanations for Node Classifiers on Knowledge Graphs","doi":"10.1145/3627673.3679904","conference":{"end_date":"2024-10-25","location":"Boise, Idaho, USA","name":"33rd ACM International Conference on Information and Knowledge Management","start_date":"2024-10-21"},"main_file_link":[{"url":"https://papers.dice-research.org/2024/CIKM_EDGE/public.pdf","open_access":"1"}],"oa":"1","date_updated":"2024-09-23T12:30:25Z","publisher":"ACM","author":[{"id":"89326","full_name":"Sapkota, Rupesh","last_name":"Sapkota","first_name":"Rupesh"},{"first_name":"Dominik","full_name":"Köhler, Dominik","last_name":"Köhler"},{"first_name":"Stefan","last_name":"Heindorf","orcid":"0000-0002-4525-6865","id":"11871","full_name":"Heindorf, Stefan"}],"date_created":"2024-09-23T12:30:10Z","status":"public","publication":"Proceedings of the 33rd ACM International Conference on Information and Knowledge Management (CIKM ’24),","type":"conference","language":[{"iso":"eng"}],"_id":"56213","project":[{"grant_number":"NW21-059D","_id":"285","name":"SAIL: SAIL - Nachhaltiger Lebenszyklus von intelligenten soziotechnischen Systemen"}],"department":[{"_id":"760"},{"_id":"574"}],"user_id":"11871"},{"abstract":[{"lang":"eng","text":"Validating assertions before adding them to a knowledge graph is an essential part of its creation and maintenance. Due to the sheer size of knowledge graphs, automatic fact-checking approaches have been developed. These approaches rely on reference knowledge to decide whether a given assertion is correct. Recent hybrid approaches achieve good results by including several knowledge sources. However, it is often impractical to provide a sheer quantity of textual knowledge or generate embedding models to leverage these hybrid approaches. We present FaVEL, an approach that uses algorithm selection and ensemble learning to amalgamate several existing fact-checking approaches that rely solely on a reference knowledge graph and, hence, use fewer resources than current hybrid approaches. For our evaluation, we create updated versions of two existing datasets and a new dataset dubbed FaVEL-DS. Our evaluation compares our approach to 15 fact-checking approaches—including the state-of-the-art approach HybridFC—on 3 datasets. Our results demonstrate that FaVEL outperforms all other approaches significantly by at least 0.04 in terms of the area under the ROC curve. Our source code, datasets, and evaluation results are open-source and can be found at https://github.com/dice-group/favel."}],"file":[{"content_type":"application/pdf","success":1,"relation":"main_file","date_updated":"2024-11-19T14:14:14Z","creator":"uqudus","date_created":"2024-11-19T14:14:14Z","file_size":190661,"access_level":"closed","file_name":"favel.pdf","file_id":"57241"}],"publication":"EKAW 2024","keyword":["fact checking","ensemble learning","transfer learning","knowledge management."],"ddc":["600"],"language":[{"iso":"eng"}],"year":"2024","corporate_editor":["Mehwish Alam"],"quality_controlled":"1","title":"FaVEL: Fact Validation Ensemble Learning","date_created":"2024-11-19T14:12:49Z","editor":[{"first_name":"Marco","full_name":"Rospocher, Marco","last_name":"Rospocher"}],"status":"public","type":"conference","popular_science":"1","file_date_updated":"2024-11-19T14:14:14Z","_id":"57240","project":[{"name":"NEBULA: Nutzerzentrierte KI-basierte Erkennung von Fake-News und Fehlinformationen","_id":"412"},{"_id":"285","name":"SAIL: SAIL - Nachhaltiger Lebenszyklus von intelligenten soziotechnischen Systemen"},{"name":"KnowGraphs: KnowGraphs: Knowledge Graphs at Scale","_id":"410"}],"department":[{"_id":"34"}],"user_id":"83392","citation":{"bibtex":"@inproceedings{Qudus_Röder_Tatkeu Pekarou_Morim da Silva_Ngonga Ngomo_2024, title={FaVEL: Fact Validation Ensemble Learning}, booktitle={EKAW 2024}, author={Qudus, Umair and Röder, Michael and Tatkeu Pekarou, Franck Lionel and Morim da Silva, Ana Alexandra and Ngonga Ngomo, Axel-Cyrille}, editor={Rospocher, Marco and Mehwish Alam}, year={2024} }","short":"U. Qudus, M. Röder, F.L. Tatkeu Pekarou, A.A. Morim da Silva, A.-C. Ngonga Ngomo, in: M. Rospocher, Mehwish Alam (Eds.), EKAW 2024, 2024.","mla":"Qudus, Umair, et al. “FaVEL: Fact Validation Ensemble Learning.” <i>EKAW 2024</i>, edited by Marco Rospocher and Mehwish Alam, 2024.","apa":"Qudus, U., Röder, M., Tatkeu Pekarou, F. L., Morim da Silva, A. A., &#38; Ngonga Ngomo, A.-C. (2024). FaVEL: Fact Validation Ensemble Learning. In M. Rospocher &#38; Mehwish Alam (Eds.), <i>EKAW 2024</i>.","chicago":"Qudus, Umair, Michael Röder, Franck Lionel Tatkeu Pekarou, Ana Alexandra Morim da Silva, and Axel-Cyrille Ngonga Ngomo. “FaVEL: Fact Validation Ensemble Learning.” In <i>EKAW 2024</i>, edited by Marco Rospocher and Mehwish Alam, 2024.","ieee":"U. Qudus, M. Röder, F. L. Tatkeu Pekarou, A. A. Morim da Silva, and A.-C. Ngonga Ngomo, “FaVEL: Fact Validation Ensemble Learning,” in <i>EKAW 2024</i>, Amsterdam, Netherlands, 2024.","ama":"Qudus U, Röder M, Tatkeu Pekarou FL, Morim da Silva AA, Ngonga Ngomo A-C. FaVEL: Fact Validation Ensemble Learning. In: Rospocher M, Mehwish Alam, eds. <i>EKAW 2024</i>. ; 2024."},"has_accepted_license":"1","conference":{"name":"24th International Conference on Knowledge Engineering and Knowledge Management","start_date":"2024-11-26","end_date":"2024-11-28","location":"Amsterdam, Netherlands"},"date_updated":"2025-09-11T09:48:12Z","author":[{"first_name":"Umair","id":"83392","full_name":"Qudus, Umair","orcid":"0000-0001-6714-8729","last_name":"Qudus"},{"orcid":"https://orcid.org/0000-0002-8609-8277","last_name":"Röder","full_name":"Röder, Michael","id":"67199","first_name":"Michael"},{"last_name":"Tatkeu Pekarou","full_name":"Tatkeu Pekarou, Franck Lionel","first_name":"Franck Lionel"},{"first_name":"Ana Alexandra","id":"72108","full_name":"Morim da Silva, Ana Alexandra","last_name":"Morim da Silva"},{"last_name":"Ngonga Ngomo","full_name":"Ngonga Ngomo, Axel-Cyrille","id":"65716","first_name":"Axel-Cyrille"}]},{"title":"Blink: Blank Node Matching Using Embeddings","date_created":"2025-09-11T10:19:47Z","publisher":"Springer Nature Switzerland","year":"2024","language":[{"iso":"eng"}],"abstract":[{"lang":"eng","text":"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."}],"publication":"Lecture Notes in Computer Science","main_file_link":[{"url":"https://papers.dice-research.org/2024/ISWC_BLINK/public.pdf"}],"doi":"10.1007/978-3-031-77844-5_12","conference":{"name":"ISWC 2024: : The 23ed International Semantic Web Conference","start_date":"2024-11-11","end_date":"2024-11-15","location":"Baltimore, USA"},"author":[{"first_name":"Alexander","last_name":"Becker","full_name":"Becker, Alexander"},{"last_name":"Sherif","orcid":"https://orcid.org/0000-0002-9927-2203","full_name":"Sherif, Mohamed","id":"67234","first_name":"Mohamed"},{"id":"65716","full_name":"Ngonga Ngomo, Axel-Cyrille","last_name":"Ngonga Ngomo","first_name":"Axel-Cyrille"}],"date_updated":"2025-09-11T10:34:02Z","citation":{"ama":"Becker A, Sherif M, Ngonga Ngomo A-C. Blink: Blank Node Matching Using Embeddings. In: <i>Lecture Notes in Computer Science</i>. Springer Nature Switzerland; 2024. doi:<a href=\"https://doi.org/10.1007/978-3-031-77844-5_12\">10.1007/978-3-031-77844-5_12</a>","chicago":"Becker, Alexander, Mohamed Sherif, and Axel-Cyrille Ngonga Ngomo. “Blink: Blank Node Matching Using Embeddings.” In <i>Lecture Notes in Computer Science</i>. Cham: Springer Nature Switzerland, 2024. <a href=\"https://doi.org/10.1007/978-3-031-77844-5_12\">https://doi.org/10.1007/978-3-031-77844-5_12</a>.","ieee":"A. Becker, M. Sherif, and A.-C. Ngonga Ngomo, “Blink: Blank Node Matching Using Embeddings,” in <i>Lecture Notes in Computer Science</i>, Cham: Springer Nature Switzerland, 2024.","apa":"Becker, A., Sherif, M., &#38; Ngonga Ngomo, A.-C. (2024). Blink: Blank Node Matching Using Embeddings. In <i>Lecture Notes in Computer Science</i>. ISWC 2024: : The 23ed International Semantic Web Conference, Baltimore, USA. Springer Nature Switzerland. <a href=\"https://doi.org/10.1007/978-3-031-77844-5_12\">https://doi.org/10.1007/978-3-031-77844-5_12</a>","bibtex":"@inbook{Becker_Sherif_Ngonga Ngomo_2024, place={Cham}, title={Blink: Blank Node Matching Using Embeddings}, DOI={<a href=\"https://doi.org/10.1007/978-3-031-77844-5_12\">10.1007/978-3-031-77844-5_12</a>}, booktitle={Lecture Notes in Computer Science}, publisher={Springer Nature Switzerland}, author={Becker, Alexander and Sherif, Mohamed and Ngonga Ngomo, Axel-Cyrille}, year={2024} }","short":"A. Becker, M. Sherif, A.-C. Ngonga Ngomo, in: Lecture Notes in Computer Science, Springer Nature Switzerland, Cham, 2024.","mla":"Becker, Alexander, et al. “Blink: Blank Node Matching Using Embeddings.” <i>Lecture Notes in Computer Science</i>, Springer Nature Switzerland, 2024, doi:<a href=\"https://doi.org/10.1007/978-3-031-77844-5_12\">10.1007/978-3-031-77844-5_12</a>."},"place":"Cham","publication_status":"published","publication_identifier":{"issn":["0302-9743","1611-3349"],"isbn":["9783031778438","9783031778445"]},"user_id":"67234","department":[{"_id":"574"}],"project":[{"_id":"285","name":"SAIL - Nachhaltiger Lebenszyklus von intelligenten soziotechnischen Systemen"}],"_id":"61210","status":"public","type":"book_chapter"},{"citation":{"ama":"Demir C, Wiebesiek M, Lu R, Ngonga Ngomo A-C, Heindorf S. LitCQD: Multi-Hop Reasoning in Incomplete Knowledge Graphs with Numeric Literals. <i>ECML PKDD</i>. Published online 2023.","chicago":"Demir, Caglar, Michel Wiebesiek, Renzhong Lu, Axel-Cyrille Ngonga Ngomo, and Stefan Heindorf. “LitCQD: Multi-Hop Reasoning in Incomplete Knowledge Graphs with Numeric Literals.” <i>ECML PKDD</i>, 2023.","ieee":"C. Demir, M. Wiebesiek, R. Lu, A.-C. Ngonga Ngomo, and S. Heindorf, “LitCQD: Multi-Hop Reasoning in Incomplete Knowledge Graphs with Numeric Literals,” <i>ECML PKDD</i>, 2023.","short":"C. Demir, M. Wiebesiek, R. Lu, A.-C. Ngonga Ngomo, S. Heindorf, ECML PKDD (2023).","bibtex":"@article{Demir_Wiebesiek_Lu_Ngonga Ngomo_Heindorf_2023, title={LitCQD: Multi-Hop Reasoning in Incomplete Knowledge Graphs with Numeric Literals}, journal={ECML PKDD}, author={Demir, Caglar and Wiebesiek, Michel and Lu, Renzhong and Ngonga Ngomo, Axel-Cyrille and Heindorf, Stefan}, year={2023} }","mla":"Demir, Caglar, et al. “LitCQD: Multi-Hop Reasoning in Incomplete Knowledge Graphs with Numeric Literals.” <i>ECML PKDD</i>, 2023.","apa":"Demir, C., Wiebesiek, M., Lu, R., Ngonga Ngomo, A.-C., &#38; Heindorf, S. (2023). LitCQD: Multi-Hop Reasoning in Incomplete Knowledge Graphs with Numeric Literals. <i>ECML PKDD</i>. European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, Torino."},"year":"2023","has_accepted_license":"1","conference":{"name":"European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases","location":"Torino"},"title":"LitCQD: Multi-Hop Reasoning in Incomplete Knowledge Graphs with Numeric Literals","author":[{"last_name":"Demir","full_name":"Demir, Caglar","id":"43817","first_name":"Caglar"},{"full_name":"Wiebesiek, Michel","last_name":"Wiebesiek","first_name":"Michel"},{"last_name":"Lu","full_name":"Lu, Renzhong","first_name":"Renzhong"},{"first_name":"Axel-Cyrille","last_name":"Ngonga Ngomo","full_name":"Ngonga Ngomo, Axel-Cyrille","id":"65716"},{"last_name":"Heindorf","orcid":"0000-0002-4525-6865","id":"11871","full_name":"Heindorf, Stefan","first_name":"Stefan"}],"date_created":"2023-08-01T09:24:21Z","oa":"1","date_updated":"2024-03-06T16:18:53Z","status":"public","file":[{"relation":"main_file","content_type":"application/pdf","file_id":"46249","file_name":"public.pdf","access_level":"open_access","file_size":562759,"creator":"cdemir","date_created":"2023-08-01T09:24:15Z","date_updated":"2023-08-01T09:24:15Z"}],"publication":"ECML PKDD","type":"journal_article","file_date_updated":"2023-08-01T09:24:15Z","language":[{"iso":"eng"}],"ddc":["000"],"department":[{"_id":"574"},{"_id":"760"}],"user_id":"14931","_id":"46248","project":[{"grant_number":"101070305","_id":"407","name":"ENEXA: Efficient Explainable Learning on Knowledge Graphs"},{"_id":"410","name":"KnowGraphs: KnowGraphs: Knowledge Graphs at Scale","grant_number":"860801"},{"name":"SAIL: SAIL: SustAInable Life-cycle of Intelligent Socio-Technical Systems","_id":"285","grant_number":"NW21-059D"}]},{"user_id":"11871","department":[{"_id":"574"},{"_id":"760"}],"project":[{"name":"KnowGraphs: KnowGraphs: Knowledge Graphs at Scale","_id":"410"},{"name":"ENEXA: Efficient Explainable Learning on Knowledge Graphs","_id":"407","grant_number":"101070305"},{"grant_number":"NW21-059D","_id":"285","name":"SAIL: SAIL: SustAInable Life-cycle of Intelligent Socio-Technical Systems"}],"_id":"33734","status":"public","editor":[{"first_name":"Catia","full_name":"Pesquita, Catia","last_name":"Pesquita"},{"last_name":"Jimenez-Ruiz","full_name":"Jimenez-Ruiz, Ernesto","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"},{"first_name":"Anastasia","last_name":"Dimou","full_name":"Dimou, Anastasia"},{"first_name":"Raphael","last_name":"Troncy","full_name":"Troncy, Raphael"},{"full_name":"Hertling, Sven","last_name":"Hertling","first_name":"Sven"}],"type":"conference","main_file_link":[{"open_access":"1","url":"https://2023.eswc-conferences.org/wp-content/uploads/2023/05/paper_Kouagou_2023_Neural.pdf"}],"conference":{"location":"Hersonissos, Crete, Greece","end_date":"2023-06-01","start_date":"2023-05-28","name":"20th Extended Semantic Web Conference"},"doi":"https://doi.org/10.1007/978-3-031-33455-9_13","author":[{"first_name":"N'Dah Jean","last_name":"KOUAGOU","full_name":"KOUAGOU, N'Dah Jean","id":"87189"},{"full_name":"Heindorf, Stefan","id":"11871","orcid":"0000-0002-4525-6865","last_name":"Heindorf","first_name":"Stefan"},{"first_name":"Caglar","last_name":"Demir","id":"43817","full_name":"Demir, Caglar"},{"first_name":"Axel-Cyrille","last_name":"Ngonga Ngomo","full_name":"Ngonga Ngomo, Axel-Cyrille","id":"65716"}],"volume":13870,"date_updated":"2023-07-02T18:10:02Z","oa":"1","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>","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>.","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>.","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>","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.","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>.","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} }"},"page":"209 - 226","intvolume":"     13870","publication_status":"published","publication_identifier":{"unknown":["978-3-031-33455-9"]},"language":[{"iso":"eng"}],"keyword":["Neural network","Concept learning","Description logics"],"external_id":{"unknown":["https://link.springer.com/chapter/10.1007/978-3-031-33455-9_13"]},"abstract":[{"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","lang":"eng"}],"publication":"The Semantic Web - 20th Extended Semantic Web Conference (ESWC 2023)","title":"Neural Class Expression Synthesis","date_created":"2022-10-15T19:20:11Z","publisher":"Springer International Publishing","year":"2023"},{"oa":"1","date_updated":"2023-08-01T09:22:40Z","author":[{"last_name":"Demir","full_name":"Demir, Caglar","id":"43817","first_name":"Caglar"},{"first_name":"Axel-Cyrille","full_name":"Ngonga Ngomo, Axel-Cyrille","last_name":"Ngonga Ngomo"}],"date_created":"2023-08-01T09:12:06Z","title":"Clifford Embeddings – A Generalized Approach for Embedding in Normed Algebras","conference":{"location":"Torino","name":"European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases"},"has_accepted_license":"1","year":"2023","citation":{"short":"C. Demir, A.-C. Ngonga Ngomo, ECML-PKDD (2023).","bibtex":"@article{Demir_Ngonga Ngomo_2023, title={Clifford Embeddings – A Generalized Approach for Embedding in Normed Algebras}, journal={ECML-PKDD}, author={Demir, Caglar and Ngonga Ngomo, Axel-Cyrille}, year={2023} }","mla":"Demir, Caglar, and Axel-Cyrille Ngonga Ngomo. “Clifford Embeddings – A Generalized Approach for Embedding in Normed Algebras.” <i>ECML-PKDD</i>, 2023.","apa":"Demir, C., &#38; Ngonga Ngomo, A.-C. (2023). Clifford Embeddings – A Generalized Approach for Embedding in Normed Algebras. <i>ECML-PKDD</i>. European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, Torino.","ama":"Demir C, Ngonga Ngomo A-C. Clifford Embeddings – A Generalized Approach for Embedding in Normed Algebras. <i>ECML-PKDD</i>. Published online 2023.","chicago":"Demir, Caglar, and Axel-Cyrille Ngonga Ngomo. “Clifford Embeddings – A Generalized Approach for Embedding in Normed Algebras.” <i>ECML-PKDD</i>, 2023.","ieee":"C. Demir and A.-C. Ngonga Ngomo, “Clifford Embeddings – A Generalized Approach for Embedding in Normed Algebras,” <i>ECML-PKDD</i>, 2023."},"_id":"46243","project":[{"grant_number":"101070305","name":"ENEXA: Efficient Explainable Learning on Knowledge Graphs","_id":"407"},{"_id":"410","name":"KnowGraphs: KnowGraphs: Knowledge Graphs at Scale"},{"name":"SAIL: SAIL: SustAInable Life-cycle of Intelligent Socio-Technical Systems","_id":"285","grant_number":"NW21-059D"}],"user_id":"43817","ddc":["000"],"file_date_updated":"2023-08-01T09:11:59Z","language":[{"iso":"eng"}],"publication":"ECML-PKDD","type":"journal_article","status":"public","file":[{"content_type":"application/pdf","relation":"main_file","date_created":"2023-08-01T09:11:59Z","creator":"cdemir","date_updated":"2023-08-01T09:11:59Z","access_level":"open_access","file_id":"46244","file_name":"public.pdf","file_size":408352}]},{"user_id":"43817","department":[{"_id":"574"}],"project":[{"_id":"407","name":"ENEXA: Efficient Explainable Learning on Knowledge Graphs","grant_number":"101070305"},{"name":"KnowGraphs: KnowGraphs: Knowledge Graphs at Scale","_id":"410"},{"grant_number":"NW21-059D","name":"SAIL: SAIL: SustAInable Life-cycle of Intelligent Socio-Technical Systems","_id":"285"}],"_id":"46251","language":[{"iso":"eng"}],"file_date_updated":"2023-08-01T09:30:35Z","ddc":["000"],"type":"journal_article","publication":"International Joint Conference on Artificial Intelligence","file":[{"file_name":"public.pdf","file_id":"46252","access_level":"open_access","file_size":340865,"creator":"cdemir","date_created":"2023-08-01T09:30:35Z","date_updated":"2023-08-01T09:30:35Z","relation":"main_file","content_type":"application/pdf"}],"status":"public","date_created":"2023-08-01T09:30:37Z","author":[{"id":"43817","full_name":"Demir, Caglar","last_name":"Demir","first_name":"Caglar"},{"first_name":"Axel-Cyrille","full_name":"Ngonga Ngomo, Axel-Cyrille","last_name":"Ngonga Ngomo"}],"date_updated":"2023-08-01T09:44:30Z","oa":"1","conference":{"location":"Macau","name":"International Joint Conference on Artificial Intelligence IJCAI 2023"},"title":"Neuro-Symbolic Class Expression Learning","has_accepted_license":"1","citation":{"mla":"Demir, Caglar, and Axel-Cyrille Ngonga Ngomo. “Neuro-Symbolic Class Expression Learning.” <i>International Joint Conference on Artificial Intelligence</i>, 2023.","short":"C. Demir, A.-C. Ngonga Ngomo, International Joint Conference on Artificial Intelligence (2023).","bibtex":"@article{Demir_Ngonga Ngomo_2023, title={Neuro-Symbolic Class Expression Learning}, journal={International Joint Conference on Artificial Intelligence}, author={Demir, Caglar and Ngonga Ngomo, Axel-Cyrille}, year={2023} }","apa":"Demir, C., &#38; Ngonga Ngomo, A.-C. (2023). Neuro-Symbolic Class Expression Learning. <i>International Joint Conference on Artificial Intelligence</i>. International Joint Conference on Artificial Intelligence IJCAI 2023, Macau.","ama":"Demir C, Ngonga Ngomo A-C. Neuro-Symbolic Class Expression Learning. <i>International Joint Conference on Artificial Intelligence</i>. Published online 2023.","chicago":"Demir, Caglar, and Axel-Cyrille Ngonga Ngomo. “Neuro-Symbolic Class Expression Learning.” <i>International Joint Conference on Artificial Intelligence</i>, 2023.","ieee":"C. Demir and A.-C. Ngonga Ngomo, “Neuro-Symbolic Class Expression Learning,” <i>International Joint Conference on Artificial Intelligence</i>, 2023."},"year":"2023"}]