[{"title":"Neural Class Expression Synthesis","external_id":{"unknown":["https://link.springer.com/chapter/10.1007/978-3-031-33455-9_13"]},"editor":[{"last_name":"Pesquita","full_name":"Pesquita, Catia","first_name":"Catia"},{"last_name":"Jimenez-Ruiz","full_name":"Jimenez-Ruiz, Ernesto","first_name":"Ernesto"},{"last_name":"McCusker","first_name":"Jamie","full_name":"McCusker, Jamie"},{"full_name":"Faria, Daniel","first_name":"Daniel","last_name":"Faria"},{"last_name":"Dragoni","first_name":"Mauro","full_name":"Dragoni, Mauro"},{"full_name":"Dimou, Anastasia","first_name":"Anastasia","last_name":"Dimou"},{"last_name":"Troncy","full_name":"Troncy, Raphael","first_name":"Raphael"},{"last_name":"Hertling","first_name":"Sven","full_name":"Hertling, Sven"}],"publication_identifier":{"unknown":["978-3-031-33455-9"]},"publication_status":"published","project":[{"_id":"410","name":"KnowGraphs: KnowGraphs: Knowledge Graphs at Scale"},{"name":"ENEXA: Efficient Explainable Learning on Knowledge Graphs","grant_number":"101070305","_id":"407"},{"name":"SAIL: SAIL: SustAInable Life-cycle of Intelligent Socio-Technical Systems","grant_number":"NW21-059D","_id":"285"}],"department":[{"_id":"574"},{"_id":"760"}],"doi":"https://doi.org/10.1007/978-3-031-33455-9_13","oa":"1","date_updated":"2023-07-02T18:10:02Z","language":[{"iso":"eng"}],"user_id":"11871","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"}],"volume":13870,"status":"public","date_created":"2022-10-15T19:20:11Z","publisher":"Springer International Publishing","author":[{"full_name":"KOUAGOU, N'Dah Jean","first_name":"N'Dah Jean","id":"87189","last_name":"KOUAGOU"},{"first_name":"Stefan","orcid":"0000-0002-4525-6865","full_name":"Heindorf, Stefan","last_name":"Heindorf","id":"11871"},{"id":"43817","last_name":"Demir","full_name":"Demir, Caglar","first_name":"Caglar"},{"full_name":"Ngonga Ngomo, Axel-Cyrille","first_name":"Axel-Cyrille","id":"65716","last_name":"Ngonga Ngomo"}],"publication":"The Semantic Web - 20th Extended Semantic Web Conference (ESWC 2023)","keyword":["Neural network","Concept learning","Description logics"],"_id":"33734","intvolume":" 13870","conference":{"start_date":"2023-05-28","name":"20th Extended Semantic Web Conference","location":"Hersonissos, Crete, Greece","end_date":"2023-06-01"},"type":"conference","year":"2023","citation":{"chicago":"KOUAGOU, N’Dah Jean, Stefan Heindorf, Caglar Demir, and Axel-Cyrille Ngonga Ngomo. “Neural Class Expression Synthesis.” In The Semantic Web - 20th Extended Semantic Web Conference (ESWC 2023), 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. https://doi.org/10.1007/978-3-031-33455-9_13.","apa":"KOUAGOU, N. J., Heindorf, S., Demir, C., & 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, & S. Hertling (Eds.), The Semantic Web - 20th Extended Semantic Web Conference (ESWC 2023) (Vol. 13870, pp. 209–226). Springer International Publishing. https://doi.org/10.1007/978-3-031-33455-9_13","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. The Semantic Web - 20th Extended Semantic Web Conference (ESWC 2023). Vol 13870. Springer International Publishing; 2023:209-226. doi:https://doi.org/10.1007/978-3-031-33455-9_13","mla":"KOUAGOU, N’Dah Jean, et al. “Neural Class Expression Synthesis.” The Semantic Web - 20th Extended Semantic Web Conference (ESWC 2023), edited by Catia Pesquita et al., vol. 13870, Springer International Publishing, 2023, pp. 209–26, doi:https://doi.org/10.1007/978-3-031-33455-9_13.","bibtex":"@inproceedings{KOUAGOU_Heindorf_Demir_Ngonga Ngomo_2023, title={Neural Class Expression Synthesis}, volume={13870}, DOI={https://doi.org/10.1007/978-3-031-33455-9_13}, 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} }","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.","ieee":"N. J. KOUAGOU, S. Heindorf, C. Demir, and A.-C. Ngonga Ngomo, “Neural Class Expression Synthesis,” in The Semantic Web - 20th Extended Semantic Web Conference (ESWC 2023), Hersonissos, Crete, Greece, 2023, vol. 13870, pp. 209–226, doi: https://doi.org/10.1007/978-3-031-33455-9_13."},"page":"209 - 226","main_file_link":[{"open_access":"1","url":"https://2023.eswc-conferences.org/wp-content/uploads/2023/05/paper_Kouagou_2023_Neural.pdf"}]},{"external_id":{"arxiv":["2301.05109"]},"abstract":[{"text":"Knowledge bases are widely used for information management on the web,\r\nenabling high-impact applications such as web search, question answering, and\r\nnatural language processing. They also serve as the backbone for automatic\r\ndecision systems, e.g. for medical diagnostics and credit scoring. As\r\nstakeholders affected by these decisions would like to understand their\r\nsituation and verify fair decisions, a number of explanation approaches have\r\nbeen proposed using concepts in description logics. However, the learned\r\nconcepts can become long and difficult to fathom for non-experts, even when\r\nverbalized. Moreover, long concepts do not immediately provide a clear path of\r\naction to change one's situation. Counterfactuals answering the question \"How\r\nmust feature values be changed to obtain a different classification?\" have been\r\nproposed as short, human-friendly explanations for tabular data. In this paper,\r\nwe transfer the notion of counterfactuals to description logics and propose the\r\nfirst algorithm for generating counterfactual explanations in the description\r\nlogic $\\mathcal{ELH}$. Counterfactual candidates are generated from concepts\r\nand the candidates with fewest feature changes are selected as counterfactuals.\r\nIn case of multiple counterfactuals, we rank them according to the likeliness\r\nof their feature combinations. For evaluation, we conduct a user survey to\r\ninvestigate which of the generated counterfactual candidates are preferred for\r\nexplanation by participants. In a second study, we explore possible use cases\r\nfor counterfactual explanations.","lang":"eng"}],"title":"Counterfactual Explanations for Concepts in ELH","user_id":"11871","author":[{"id":"93402","last_name":"Sieger","full_name":"Sieger, Leonie Nora","first_name":"Leonie Nora"},{"first_name":"Stefan","full_name":"Heindorf, Stefan","orcid":"0000-0002-4525-6865","last_name":"Heindorf","id":"11871"},{"full_name":"Blübaum, Lukas","first_name":"Lukas","last_name":"Blübaum"},{"full_name":"Ngonga Ngomo, Axel-Cyrille","first_name":"Axel-Cyrille","id":"65716","last_name":"Ngonga Ngomo"}],"publication":"arXiv:2301.05109","department":[{"_id":"574"},{"_id":"760"}],"status":"public","date_created":"2023-01-22T19:36:01Z","date_updated":"2023-07-02T18:10:34Z","_id":"37937","main_file_link":[{"url":"https://arxiv.org/pdf/2301.05109.pdf"}],"type":"preprint","year":"2023","citation":{"mla":"Sieger, Leonie Nora, et al. “Counterfactual Explanations for Concepts in ELH.” ArXiv:2301.05109, 2023.","bibtex":"@article{Sieger_Heindorf_Blübaum_Ngonga Ngomo_2023, title={Counterfactual Explanations for Concepts in ELH}, journal={arXiv:2301.05109}, author={Sieger, Leonie Nora and Heindorf, Stefan and Blübaum, Lukas and Ngonga Ngomo, Axel-Cyrille}, year={2023} }","apa":"Sieger, L. N., Heindorf, S., Blübaum, L., & Ngonga Ngomo, A.-C. (2023). Counterfactual Explanations for Concepts in ELH. In arXiv:2301.05109.","ama":"Sieger LN, Heindorf S, Blübaum L, Ngonga Ngomo A-C. Counterfactual Explanations for Concepts in ELH. arXiv:230105109. Published online 2023.","chicago":"Sieger, Leonie Nora, Stefan Heindorf, Lukas Blübaum, and Axel-Cyrille Ngonga Ngomo. “Counterfactual Explanations for Concepts in ELH.” ArXiv:2301.05109, 2023.","ieee":"L. N. Sieger, S. Heindorf, L. Blübaum, and A.-C. Ngonga Ngomo, “Counterfactual Explanations for Concepts in ELH,” arXiv:2301.05109. 2023.","short":"L.N. Sieger, S. Heindorf, L. Blübaum, A.-C. Ngonga Ngomo, ArXiv:2301.05109 (2023)."},"language":[{"iso":"eng"}]},{"ddc":["000"],"title":"Accelerating Concept Learning via Sampling","user_id":"11871","department":[{"_id":"760"}],"publication":"CIKM","file_date_updated":"2023-08-19T08:08:39Z","author":[{"full_name":"Baci, Alkid","first_name":"Alkid","last_name":"Baci"},{"last_name":"Heindorf","id":"11871","first_name":"Stefan","full_name":"Heindorf, Stefan","orcid":"0000-0002-4525-6865"}],"file":[{"file_id":"46577","creator":"heindorf","file_size":523067,"relation":"main_file","date_updated":"2023-08-19T08:08:39Z","content_type":"application/pdf","date_created":"2023-08-19T08:08:39Z","file_name":"baci2023_CIKM.pdf","access_level":"open_access"}],"date_created":"2023-08-19T08:02:54Z","has_accepted_license":"1","status":"public","_id":"46575","date_updated":"2023-08-19T08:08:53Z","oa":"1","citation":{"ieee":"A. Baci and S. Heindorf, “Accelerating Concept Learning via Sampling,” 2023.","short":"A. Baci, S. Heindorf, in: CIKM, 2023.","mla":"Baci, Alkid, and Stefan Heindorf. “Accelerating Concept Learning via Sampling.” CIKM, 2023.","bibtex":"@inproceedings{Baci_Heindorf_2023, title={Accelerating Concept Learning via Sampling}, booktitle={CIKM}, author={Baci, Alkid and Heindorf, Stefan}, year={2023} }","apa":"Baci, A., & Heindorf, S. (2023). Accelerating Concept Learning via Sampling. CIKM.","ama":"Baci A, Heindorf S. Accelerating Concept Learning via Sampling. In: CIKM. ; 2023.","chicago":"Baci, Alkid, and Stefan Heindorf. “Accelerating Concept Learning via Sampling.” In CIKM, 2023."},"year":"2023","type":"conference","language":[{"iso":"eng"}]},{"title":"Neural Class Expression Synthesis in ALCHIQ(D)","user_id":"11871","abstract":[{"text":"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.","lang":"eng"}],"place":"Cham","publication_status":"published","publication_identifier":{"isbn":["9783031434204","9783031434211"],"issn":["0302-9743","1611-3349"]},"date_created":"2023-09-25T13:42:01Z","status":"public","department":[{"_id":"760"},{"_id":"574"}],"publication":"Machine Learning and Knowledge Discovery in Databases: Research Track","author":[{"full_name":"Kouagou, N'Dah Jean","first_name":"N'Dah Jean","id":"87189","last_name":"Kouagou"},{"first_name":"Stefan","full_name":"Heindorf, Stefan","orcid":"0000-0002-4525-6865","last_name":"Heindorf","id":"11871"},{"last_name":"Demir","id":"43817","first_name":"Caglar","full_name":"Demir, Caglar"},{"last_name":"Ngonga Ngomo","id":"65716","first_name":"Axel-Cyrille","full_name":"Ngonga Ngomo, Axel-Cyrille"}],"publisher":"Springer Nature Switzerland","doi":"10.1007/978-3-031-43421-1_12","conference":{"end_date":"2023-09-22","location":"Turin","name":"European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases","start_date":"2023-09-18"},"_id":"47421","date_updated":"2023-11-21T09:20:31Z","type":"book_chapter","year":"2023","citation":{"short":"N.J. Kouagou, S. Heindorf, C. Demir, A.-C. Ngonga Ngomo, in: Machine Learning and Knowledge Discovery in Databases: Research Track, Springer Nature Switzerland, Cham, 2023.","ieee":"N. J. Kouagou, S. Heindorf, C. Demir, and A.-C. Ngonga Ngomo, “Neural Class Expression Synthesis in ALCHIQ(D),” in Machine Learning and Knowledge Discovery in Databases: Research Track, Cham: Springer Nature Switzerland, 2023.","chicago":"Kouagou, N’Dah Jean, Stefan Heindorf, Caglar Demir, and Axel-Cyrille Ngonga Ngomo. “Neural Class Expression Synthesis in ALCHIQ(D).” In Machine Learning and Knowledge Discovery in Databases: Research Track. Cham: Springer Nature Switzerland, 2023. https://doi.org/10.1007/978-3-031-43421-1_12.","apa":"Kouagou, N. J., Heindorf, S., Demir, C., & Ngonga Ngomo, A.-C. (2023). Neural Class Expression Synthesis in ALCHIQ(D). In Machine Learning and Knowledge Discovery in Databases: Research Track. European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, Turin. Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-43421-1_12","ama":"Kouagou NJ, Heindorf S, Demir C, Ngonga Ngomo A-C. Neural Class Expression Synthesis in ALCHIQ(D). In: Machine Learning and Knowledge Discovery in Databases: Research Track. Springer Nature Switzerland; 2023. doi:10.1007/978-3-031-43421-1_12","mla":"Kouagou, N’Dah Jean, et al. “Neural Class Expression Synthesis in ALCHIQ(D).” Machine Learning and Knowledge Discovery in Databases: Research Track, Springer Nature Switzerland, 2023, doi:10.1007/978-3-031-43421-1_12.","bibtex":"@inbook{Kouagou_Heindorf_Demir_Ngonga Ngomo_2023, place={Cham}, title={Neural Class Expression Synthesis in ALCHIQ(D)}, DOI={10.1007/978-3-031-43421-1_12}, booktitle={Machine Learning and Knowledge Discovery in Databases: Research Track}, publisher={Springer Nature Switzerland}, author={Kouagou, N’Dah Jean and Heindorf, Stefan and Demir, Caglar and Ngonga Ngomo, Axel-Cyrille}, year={2023} }"},"language":[{"iso":"eng"}]},{"user_id":"14931","title":"Class Expression Learning with Multiple Representations","author":[{"full_name":"Ngonga Ngomo, Axel-Cyrille","first_name":"Axel-Cyrille","id":"65716","last_name":"Ngonga Ngomo"},{"id":"43817","last_name":"Demir","full_name":"Demir, Caglar","first_name":"Caglar"},{"first_name":"N'Dah Jean","full_name":"Kouagou, N'Dah Jean","last_name":"Kouagou","id":"87189"},{"last_name":"Heindorf","id":"11871","first_name":"Stefan","orcid":"0000-0002-4525-6865","full_name":"Heindorf, Stefan"},{"full_name":"Karalis, Nikoloas","first_name":"Nikoloas","last_name":"Karalis"},{"last_name":"Bigerl","id":"72857","first_name":"Alexander","full_name":"Bigerl, Alexander"}],"publisher":"IOS Press","department":[{"_id":"760"},{"_id":"574"}],"publication":"Compendium of Neurosymbolic Artificial Intelligence","status":"public","date_created":"2023-08-08T11:49:51Z","date_updated":"2023-11-21T08:06:20Z","_id":"46460","language":[{"iso":"eng"}],"type":"book_chapter","citation":{"bibtex":"@inbook{Ngonga Ngomo_Demir_Kouagou_Heindorf_Karalis_Bigerl_2023, title={Class Expression Learning with Multiple Representations}, booktitle={Compendium of Neurosymbolic Artificial Intelligence}, publisher={IOS Press}, author={Ngonga Ngomo, Axel-Cyrille and Demir, Caglar and Kouagou, N’Dah Jean and Heindorf, Stefan and Karalis, Nikoloas and Bigerl, Alexander}, year={2023}, pages={272–286} }","mla":"Ngonga Ngomo, Axel-Cyrille, et al. “Class Expression Learning with Multiple Representations.” Compendium of Neurosymbolic Artificial Intelligence, IOS Press, 2023, pp. 272–286.","apa":"Ngonga Ngomo, A.-C., Demir, C., Kouagou, N. J., Heindorf, S., Karalis, N., & Bigerl, A. (2023). Class Expression Learning with Multiple Representations. In Compendium of Neurosymbolic Artificial Intelligence (pp. 272–286). IOS Press.","ama":"Ngonga Ngomo A-C, Demir C, Kouagou NJ, Heindorf S, Karalis N, Bigerl A. Class Expression Learning with Multiple Representations. In: Compendium of Neurosymbolic Artificial Intelligence. IOS Press; 2023:272–286.","chicago":"Ngonga Ngomo, Axel-Cyrille, Caglar Demir, N’Dah Jean Kouagou, Stefan Heindorf, Nikoloas Karalis, and Alexander Bigerl. “Class Expression Learning with Multiple Representations.” In Compendium of Neurosymbolic Artificial Intelligence, 272–286. IOS Press, 2023.","ieee":"A.-C. Ngonga Ngomo, C. Demir, N. J. Kouagou, S. Heindorf, N. Karalis, and A. Bigerl, “Class Expression Learning with Multiple Representations,” in Compendium of Neurosymbolic Artificial Intelligence, IOS Press, 2023, pp. 272–286.","short":"A.-C. Ngonga Ngomo, C. Demir, N.J. Kouagou, S. Heindorf, N. Karalis, A. Bigerl, in: Compendium of Neurosymbolic Artificial Intelligence, IOS Press, 2023, pp. 272–286."},"year":"2023","page":"272–286"},{"conference":{"location":"Torino","name":"European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases"},"_id":"46248","date_updated":"2024-03-06T16:18:53Z","oa":"1","year":"2023","type":"journal_article","citation":{"short":"C. Demir, M. Wiebesiek, R. Lu, A.-C. Ngonga Ngomo, S. Heindorf, ECML PKDD (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,” ECML PKDD, 2023.","apa":"Demir, C., Wiebesiek, M., Lu, R., Ngonga Ngomo, A.-C., & Heindorf, S. (2023). LitCQD: Multi-Hop Reasoning in Incomplete Knowledge Graphs with Numeric Literals. ECML PKDD. European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, Torino.","ama":"Demir C, Wiebesiek M, Lu R, Ngonga Ngomo A-C, Heindorf S. LitCQD: Multi-Hop Reasoning in Incomplete Knowledge Graphs with Numeric Literals. ECML PKDD. 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.” ECML PKDD, 2023.","mla":"Demir, Caglar, et al. “LitCQD: Multi-Hop Reasoning in Incomplete Knowledge Graphs with Numeric Literals.” 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} }"},"language":[{"iso":"eng"}],"ddc":["000"],"title":"LitCQD: Multi-Hop Reasoning in Incomplete Knowledge Graphs with Numeric Literals","user_id":"14931","publication":"ECML PKDD","department":[{"_id":"574"},{"_id":"760"}],"file_date_updated":"2023-08-01T09:24:15Z","author":[{"first_name":"Caglar","full_name":"Demir, Caglar","last_name":"Demir","id":"43817"},{"last_name":"Wiebesiek","first_name":"Michel","full_name":"Wiebesiek, Michel"},{"last_name":"Lu","first_name":"Renzhong","full_name":"Lu, Renzhong"},{"id":"65716","last_name":"Ngonga Ngomo","full_name":"Ngonga Ngomo, Axel-Cyrille","first_name":"Axel-Cyrille"},{"last_name":"Heindorf","id":"11871","first_name":"Stefan","orcid":"0000-0002-4525-6865","full_name":"Heindorf, Stefan"}],"file":[{"file_size":562759,"file_id":"46249","creator":"cdemir","date_updated":"2023-08-01T09:24:15Z","content_type":"application/pdf","relation":"main_file","date_created":"2023-08-01T09:24:15Z","file_name":"public.pdf","access_level":"open_access"}],"project":[{"grant_number":"101070305","name":"ENEXA: Efficient Explainable Learning on Knowledge Graphs","_id":"407"},{"_id":"410","name":"KnowGraphs: KnowGraphs: Knowledge Graphs at Scale","grant_number":"860801"},{"grant_number":"NW21-059D","name":"SAIL: SAIL: SustAInable Life-cycle of Intelligent Socio-Technical Systems","_id":"285"}],"date_created":"2023-08-01T09:24:21Z","has_accepted_license":"1","status":"public"},{"status":"public","date_created":"2022-10-15T19:34:41Z","author":[{"first_name":"N'Dah Jean","full_name":"KOUAGOU, N'Dah Jean","last_name":"KOUAGOU","id":"87189"},{"first_name":"Stefan","orcid":"0000-0002-4525-6865","full_name":"Heindorf, Stefan","last_name":"Heindorf","id":"11871"},{"last_name":"Demir","id":"43817","first_name":"Caglar","full_name":"Demir, Caglar"},{"first_name":"Axel-Cyrille","full_name":"Ngonga Ngomo, Axel-Cyrille","last_name":"Ngonga Ngomo","id":"65716"}],"publisher":"Springer International Publishing","publication":"The Semantic Web","user_id":"11871","citation":{"mla":"KOUAGOU, N’Dah Jean, et al. “Learning Concept Lengths Accelerates Concept Learning in ALC.” The Semantic Web, Springer International Publishing, 2022, doi:10.1007/978-3-031-06981-9_14.","bibtex":"@inbook{KOUAGOU_Heindorf_Demir_Ngonga Ngomo_2022, place={Cham}, title={Learning Concept Lengths Accelerates Concept Learning in ALC}, DOI={10.1007/978-3-031-06981-9_14}, booktitle={The Semantic Web}, publisher={Springer International Publishing}, author={KOUAGOU, N’Dah Jean and Heindorf, Stefan and Demir, Caglar and Ngonga Ngomo, Axel-Cyrille}, year={2022} }","chicago":"KOUAGOU, N’Dah Jean, Stefan Heindorf, Caglar Demir, and Axel-Cyrille Ngonga Ngomo. “Learning Concept Lengths Accelerates Concept Learning in ALC.” In The Semantic Web. Cham: Springer International Publishing, 2022. https://doi.org/10.1007/978-3-031-06981-9_14.","ama":"KOUAGOU NJ, Heindorf S, Demir C, Ngonga Ngomo A-C. Learning Concept Lengths Accelerates Concept Learning in ALC. In: The Semantic Web. Springer International Publishing; 2022. doi:10.1007/978-3-031-06981-9_14","apa":"KOUAGOU, N. J., Heindorf, S., Demir, C., & Ngonga Ngomo, A.-C. (2022). Learning Concept Lengths Accelerates Concept Learning in ALC. In The Semantic Web. Springer International Publishing. https://doi.org/10.1007/978-3-031-06981-9_14","ieee":"N. J. KOUAGOU, S. Heindorf, C. Demir, and A.-C. Ngonga Ngomo, “Learning Concept Lengths Accelerates Concept Learning in ALC,” in The Semantic Web, Cham: Springer International Publishing, 2022.","short":"N.J. KOUAGOU, S. Heindorf, C. Demir, A.-C. Ngonga Ngomo, in: The Semantic Web, Springer International Publishing, Cham, 2022."},"type":"book_chapter","year":"2022","main_file_link":[{"url":"https://arxiv.org/abs/2107.04911","open_access":"1"}],"_id":"33740","publication_identifier":{"issn":["0302-9743","1611-3349"],"isbn":["9783031069802","9783031069819"]},"publication_status":"published","department":[{"_id":"574"}],"related_material":{"link":[{"relation":"confirmation","url":"https://link.springer.com/chapter/10.1007/978-3-031-06981-9_14"}]},"title":"Learning Concept Lengths Accelerates Concept Learning in ALC","place":"Cham","language":[{"iso":"eng"}],"oa":"1","doi":"10.1007/978-3-031-06981-9_14","date_updated":"2022-10-15T19:52:08Z"},{"date_created":"2022-01-12T10:22:53Z","status":"public","department":[{"_id":"574"}],"publication":"WWW","publisher":"ACM","author":[{"id":"11871","last_name":"Heindorf","full_name":"Heindorf, Stefan","orcid":"0000-0002-4525-6865","first_name":"Stefan"},{"last_name":"Blübaum","full_name":"Blübaum, Lukas","first_name":"Lukas"},{"last_name":"Düsterhus","first_name":"Nick","full_name":"Düsterhus, Nick"},{"full_name":"Werner, Till","first_name":"Till","last_name":"Werner"},{"last_name":"Golani","first_name":"Varun Nandkumar","full_name":"Golani, Varun Nandkumar"},{"id":"43817","last_name":"Demir","full_name":"Demir, Caglar","first_name":"Caglar"},{"first_name":"Axel-Cyrille","full_name":"Ngonga Ngomo, Axel-Cyrille","last_name":"Ngonga Ngomo","id":"65716"}],"user_id":"11871","title":"EvoLearner: Learning Description Logics with Evolutionary Algorithms","abstract":[{"text":"Classifying nodes in knowledge graphs is an important task, e.g., predicting\r\nmissing types of entities, predicting which molecules cause cancer, or\r\npredicting which drugs are promising treatment candidates. While black-box\r\nmodels often achieve high predictive performance, they are only post-hoc and\r\nlocally explainable and do not allow the learned model to be easily enriched\r\nwith domain knowledge. Towards this end, learning description logic concepts\r\nfrom positive and negative examples has been proposed. However, learning such\r\nconcepts often takes a long time and state-of-the-art approaches provide\r\nlimited support for literal data values, although they are crucial for many\r\napplications. In this paper, we propose EvoLearner - an evolutionary approach\r\nto learn ALCQ(D), which is the attributive language with complement (ALC)\r\npaired with qualified cardinality restrictions (Q) and data properties (D). We\r\ncontribute a novel initialization method for the initial population: starting\r\nfrom positive examples (nodes in the knowledge graph), we perform biased random\r\nwalks and translate them to description logic concepts. Moreover, we improve\r\nsupport for data properties by maximizing information gain when deciding where\r\nto split the data. We show that our approach significantly outperforms the\r\nstate of the art on the benchmarking framework SML-Bench for structured machine\r\nlearning. Our ablation study confirms that this is due to our novel\r\ninitialization method and support for data properties.","lang":"eng"}],"language":[{"iso":"eng"}],"page":"818-828","citation":{"mla":"Heindorf, Stefan, et al. “EvoLearner: Learning Description Logics with Evolutionary Algorithms.” WWW, ACM, 2022, pp. 818–28.","bibtex":"@inproceedings{Heindorf_Blübaum_Düsterhus_Werner_Golani_Demir_Ngonga Ngomo_2022, title={EvoLearner: Learning Description Logics with Evolutionary Algorithms}, booktitle={WWW}, publisher={ACM}, author={Heindorf, Stefan and Blübaum, Lukas and Düsterhus, Nick and Werner, Till and Golani, Varun Nandkumar and Demir, Caglar and Ngonga Ngomo, Axel-Cyrille}, year={2022}, pages={818–828} }","chicago":"Heindorf, Stefan, Lukas Blübaum, Nick Düsterhus, Till Werner, Varun Nandkumar Golani, Caglar Demir, and Axel-Cyrille Ngonga Ngomo. “EvoLearner: Learning Description Logics with Evolutionary Algorithms.” In WWW, 818–28. ACM, 2022.","apa":"Heindorf, S., Blübaum, L., Düsterhus, N., Werner, T., Golani, V. N., Demir, C., & Ngonga Ngomo, A.-C. (2022). EvoLearner: Learning Description Logics with Evolutionary Algorithms. WWW, 818–828.","ama":"Heindorf S, Blübaum L, Düsterhus N, et al. EvoLearner: Learning Description Logics with Evolutionary Algorithms. In: WWW. ACM; 2022:818-828.","ieee":"S. Heindorf et al., “EvoLearner: Learning Description Logics with Evolutionary Algorithms,” in WWW, 2022, pp. 818–828.","short":"S. Heindorf, L. Blübaum, N. Düsterhus, T. Werner, V.N. Golani, C. Demir, A.-C. Ngonga Ngomo, in: WWW, ACM, 2022, pp. 818–828."},"year":"2022","type":"conference","main_file_link":[{"url":"https://arxiv.org/abs/2111.04879","open_access":"1"}],"oa":"1","_id":"29290","date_updated":"2022-10-16T08:49:22Z"},{"type":"conference","citation":{"bibtex":"@inproceedings{Sieger_Hermann_Schomäcker_Heindorf_Meske_Hey_Doğangün_2022, title={User Involvement in Training Smart Home Agents}, DOI={10.1145/3527188.3561914}, booktitle={International Conference on Human-Agent Interaction}, publisher={ACM}, author={Sieger, Leonie Nora and Hermann, Julia and Schomäcker, Astrid and Heindorf, Stefan and Meske, Christian and Hey, Celine-Chiara and Doğangün, Ayşegül}, year={2022} }","mla":"Sieger, Leonie Nora, et al. “User Involvement in Training Smart Home Agents.” International Conference on Human-Agent Interaction, ACM, 2022, doi:10.1145/3527188.3561914.","ama":"Sieger LN, Hermann J, Schomäcker A, et al. User Involvement in Training Smart Home Agents. In: International Conference on Human-Agent Interaction. ACM; 2022. doi:10.1145/3527188.3561914","apa":"Sieger, L. N., Hermann, J., Schomäcker, A., Heindorf, S., Meske, C., Hey, C.-C., & Doğangün, A. (2022). User Involvement in Training Smart Home Agents. International Conference on Human-Agent Interaction. HAI ’22: International Conference on Human-Agent Interaction, Christchurch, New Zealand. https://doi.org/10.1145/3527188.3561914","chicago":"Sieger, Leonie Nora, Julia Hermann, Astrid Schomäcker, Stefan Heindorf, Christian Meske, Celine-Chiara Hey, and Ayşegül Doğangün. “User Involvement in Training Smart Home Agents.” In International Conference on Human-Agent Interaction. ACM, 2022. https://doi.org/10.1145/3527188.3561914.","ieee":"L. N. Sieger et al., “User Involvement in Training Smart Home Agents,” presented at the HAI ’22: International Conference on Human-Agent Interaction, Christchurch, New Zealand, 2022, doi: 10.1145/3527188.3561914.","short":"L.N. Sieger, J. Hermann, A. Schomäcker, S. Heindorf, C. Meske, C.-C. Hey, A. Doğangün, in: International Conference on Human-Agent Interaction, ACM, 2022."},"year":"2022","main_file_link":[{"url":"https://papers.dice-research.org/2022/HAI_SmartHome/User_Involvement_in_Training_Smart_Home_Agents_public.pdf"}],"conference":{"location":"Christchurch, New Zealand","start_date":"2022-12-05","name":"HAI '22: International Conference on Human-Agent Interaction","end_date":"2022-12-08"},"_id":"34674","alternative_title":["Increasing Perceived Control and Understanding"],"date_created":"2022-12-21T09:48:43Z","status":"public","publication":"International Conference on Human-Agent Interaction","keyword":["human-agent interaction","smart homes","supervised learning","participation"],"author":[{"full_name":"Sieger, Leonie Nora","first_name":"Leonie Nora","id":"93402","last_name":"Sieger"},{"last_name":"Hermann","full_name":"Hermann, Julia","first_name":"Julia"},{"last_name":"Schomäcker","full_name":"Schomäcker, Astrid","first_name":"Astrid"},{"first_name":"Stefan","orcid":"0000-0002-4525-6865","full_name":"Heindorf, Stefan","last_name":"Heindorf","id":"11871"},{"full_name":"Meske, Christian","first_name":"Christian","last_name":"Meske"},{"first_name":"Celine-Chiara","full_name":"Hey, Celine-Chiara","last_name":"Hey"},{"first_name":"Ayşegül","full_name":"Doğangün, Ayşegül","last_name":"Doğangün"}],"quality_controlled":"1","publisher":"ACM","user_id":"14931","abstract":[{"text":"Smart home systems contain plenty of features that enhance wellbeing in everyday life through artificial intelligence (AI). However, many users feel insecure because they do not understand the AI’s functionality and do not feel they are in control of it. Combining technical, psychological and philosophical views on AI, we rethink smart homes as interactive systems where users can partake in an intelligent agent’s learning. Parallel to the goals of explainable AI (XAI), we explored the possibility of user involvement in supervised learning of the smart home to have a first approach to improve acceptance, support subjective understanding and increase perceived control. In this work, we conducted two studies: In an online pre-study, we asked participants about their attitude towards teaching AI via a questionnaire. In the main study, we performed a Wizard of Oz laboratory experiment with human participants, where participants spent time in a prototypical smart home and taught activity recognition to the intelligent agent through supervised learning based on the user’s behaviour. We found that involvement in the AI’s learning phase enhanced the users’ feeling of control, perceived understanding and perceived usefulness of AI in general. The participants reported positive attitudes towards training a smart home AI and found the process understandable and controllable. We suggest that involving the user in the learning phase could lead to better personalisation and increased understanding and control by users of intelligent agents for smart home automation.","lang":"eng"}],"language":[{"iso":"eng"}],"doi":"10.1145/3527188.3561914","date_updated":"2023-02-09T14:48:51Z","publication_status":"published","project":[{"name":"TRR 318 - B1: TRR 318 - Subproject B1","_id":"121"}],"department":[{"_id":"574"},{"_id":"760"}],"title":"User Involvement in Training Smart Home Agents"},{"publication":"The Semantic Web: ESWC 2022 Satellite Events","department":[{"_id":"574"}],"author":[{"full_name":"Zahera, Hamada Mohamed Abdelsamee","orcid":"0000-0003-0215-1278","first_name":"Hamada Mohamed Abdelsamee","id":"72768","last_name":"Zahera"},{"full_name":"Heindorf, Stefan","orcid":"0000-0002-4525-6865","first_name":"Stefan","id":"11871","last_name":"Heindorf"},{"last_name":"Balke","first_name":"Stefan","full_name":"Balke, Stefan"},{"full_name":"Haupt, Jonas","first_name":"Jonas","last_name":"Haupt"},{"last_name":"Voigt","full_name":"Voigt, Martin","first_name":"Martin"},{"full_name":"Walter, Carolin","first_name":"Carolin","last_name":"Walter"},{"last_name":"Witter","first_name":"Fabian","full_name":"Witter, Fabian"},{"full_name":"Ngonga Ngomo, Axel-Cyrille","first_name":"Axel-Cyrille","id":"65716","last_name":"Ngonga Ngomo"}],"publisher":"Springer International Publishing","publication_identifier":{"issn":["0302-9743","1611-3349"],"isbn":["9783031116087","9783031116094"]},"publication_status":"published","date_created":"2022-10-15T19:25:42Z","status":"public","place":"Cham","title":"Tab2Onto: Unsupervised Semantification with Knowledge Graph Embeddings","user_id":"72768","main_file_link":[{"url":"https://2022.eswc-conferences.org/wp-content/uploads/2022/05/pd_Zahera_et_al_paper_230.pdf","open_access":"1"}],"year":"2022","citation":{"bibtex":"@inbook{Zahera_Heindorf_Balke_Haupt_Voigt_Walter_Witter_Ngonga Ngomo_2022, place={Cham}, title={Tab2Onto: Unsupervised Semantification with Knowledge Graph Embeddings}, DOI={10.1007/978-3-031-11609-4_9}, booktitle={The Semantic Web: ESWC 2022 Satellite Events}, publisher={Springer International Publishing}, author={Zahera, Hamada Mohamed Abdelsamee and Heindorf, Stefan and Balke, Stefan and Haupt, Jonas and Voigt, Martin and Walter, Carolin and Witter, Fabian and Ngonga Ngomo, Axel-Cyrille}, year={2022} }","mla":"Zahera, Hamada Mohamed Abdelsamee, et al. “Tab2Onto: Unsupervised Semantification with Knowledge Graph Embeddings.” The Semantic Web: ESWC 2022 Satellite Events, Springer International Publishing, 2022, doi:10.1007/978-3-031-11609-4_9.","chicago":"Zahera, Hamada Mohamed Abdelsamee, Stefan Heindorf, Stefan Balke, Jonas Haupt, Martin Voigt, Carolin Walter, Fabian Witter, and Axel-Cyrille Ngonga Ngomo. “Tab2Onto: Unsupervised Semantification with Knowledge Graph Embeddings.” In The Semantic Web: ESWC 2022 Satellite Events. Cham: Springer International Publishing, 2022. https://doi.org/10.1007/978-3-031-11609-4_9.","ama":"Zahera HMA, Heindorf S, Balke S, et al. Tab2Onto: Unsupervised Semantification with Knowledge Graph Embeddings. In: The Semantic Web: ESWC 2022 Satellite Events. Springer International Publishing; 2022. doi:10.1007/978-3-031-11609-4_9","apa":"Zahera, H. M. A., Heindorf, S., Balke, S., Haupt, J., Voigt, M., Walter, C., Witter, F., & Ngonga Ngomo, A.-C. (2022). Tab2Onto: Unsupervised Semantification with Knowledge Graph Embeddings. In The Semantic Web: ESWC 2022 Satellite Events. Springer International Publishing. https://doi.org/10.1007/978-3-031-11609-4_9","ieee":"H. M. A. Zahera et al., “Tab2Onto: Unsupervised Semantification with Knowledge Graph Embeddings,” in The Semantic Web: ESWC 2022 Satellite Events, Cham: Springer International Publishing, 2022.","short":"H.M.A. Zahera, S. Heindorf, S. Balke, J. Haupt, M. Voigt, C. Walter, F. Witter, A.-C. Ngonga Ngomo, in: The Semantic Web: ESWC 2022 Satellite Events, Springer International Publishing, Cham, 2022."},"type":"book_chapter","language":[{"iso":"eng"}],"_id":"33738","date_updated":"2023-06-23T09:20:20Z","doi":"10.1007/978-3-031-11609-4_9","oa":"1"},{"language":[{"iso":"eng"}],"year":"2022","type":"conference","citation":{"mla":"Bondarenko, Alexander, et al. “CausalQA: A Benchmark for Causal Question Answering.” Proceedings of the 29th International Conference on Computational Linguistics, International Committee on Computational Linguistics, 2022, pp. 3296–3308.","bibtex":"@inproceedings{Bondarenko_Wolska_Heindorf_Blübaum_Ngonga Ngomo_Stein_Braslavski_Hagen_Potthast_2022, place={Gyeongju, Republic of Korea}, title={CausalQA: A Benchmark for Causal Question Answering}, booktitle={Proceedings of the 29th International Conference on Computational Linguistics}, publisher={International Committee on Computational Linguistics}, author={Bondarenko, Alexander and Wolska, Magdalena and Heindorf, Stefan and Blübaum, Lukas and Ngonga Ngomo, Axel-Cyrille and Stein, Benno and Braslavski, Pavel and Hagen, Matthias and Potthast, Martin}, year={2022}, pages={3296–3308} }","chicago":"Bondarenko, Alexander, Magdalena Wolska, Stefan Heindorf, Lukas Blübaum, Axel-Cyrille Ngonga Ngomo, Benno Stein, Pavel Braslavski, Matthias Hagen, and Martin Potthast. “CausalQA: A Benchmark for Causal Question Answering.” In Proceedings of the 29th International Conference on Computational Linguistics, 3296–3308. Gyeongju, Republic of Korea: International Committee on Computational Linguistics, 2022.","ama":"Bondarenko A, Wolska M, Heindorf S, et al. CausalQA: A Benchmark for Causal Question Answering. In: Proceedings of the 29th International Conference on Computational Linguistics. International Committee on Computational Linguistics; 2022:3296–3308.","apa":"Bondarenko, A., Wolska, M., Heindorf, S., Blübaum, L., Ngonga Ngomo, A.-C., Stein, B., Braslavski, P., Hagen, M., & Potthast, M. (2022). CausalQA: A Benchmark for Causal Question Answering. Proceedings of the 29th International Conference on Computational Linguistics, 3296–3308.","ieee":"A. Bondarenko et al., “CausalQA: A Benchmark for Causal Question Answering,” in Proceedings of the 29th International Conference on Computational Linguistics, 2022, pp. 3296–3308.","short":"A. Bondarenko, M. Wolska, S. Heindorf, L. Blübaum, A.-C. Ngonga Ngomo, B. Stein, P. Braslavski, M. Hagen, M. Potthast, in: Proceedings of the 29th International Conference on Computational Linguistics, International Committee on Computational Linguistics, Gyeongju, Republic of Korea, 2022, pp. 3296–3308."},"page":"3296–3308","main_file_link":[{"url":"https://aclanthology.org/2022.coling-1.291.pdf","open_access":"1"}],"oa":"1","date_updated":"2023-07-02T18:14:01Z","_id":"33739","status":"public","date_created":"2022-10-15T19:33:10Z","project":[{"name":"PC2: Computing Resources Provided by the Paderborn Center for Parallel Computing","_id":"52"}],"author":[{"last_name":"Bondarenko","first_name":"Alexander","full_name":"Bondarenko, Alexander"},{"full_name":"Wolska, Magdalena","first_name":"Magdalena","last_name":"Wolska"},{"orcid":"0000-0002-4525-6865","full_name":"Heindorf, Stefan","first_name":"Stefan","id":"11871","last_name":"Heindorf"},{"first_name":"Lukas","full_name":"Blübaum, Lukas","last_name":"Blübaum"},{"last_name":"Ngonga Ngomo","id":"65716","first_name":"Axel-Cyrille","full_name":"Ngonga Ngomo, Axel-Cyrille"},{"last_name":"Stein","first_name":"Benno","full_name":"Stein, Benno"},{"first_name":"Pavel","full_name":"Braslavski, Pavel","last_name":"Braslavski"},{"last_name":"Hagen","full_name":"Hagen, Matthias","first_name":"Matthias"},{"last_name":"Potthast","full_name":"Potthast, Martin","first_name":"Martin"}],"publisher":"International Committee on Computational Linguistics","department":[{"_id":"574"},{"_id":"760"}],"publication":"Proceedings of the 29th International Conference on Computational Linguistics","user_id":"11871","title":"CausalQA: A Benchmark for Causal Question Answering","place":"Gyeongju, Republic of Korea","abstract":[{"lang":"eng","text":"At least 5% of questions submitted to search engines ask about cause-effect relationships in some way. To support the development of tailored approaches that can answer such questions, we construct Webis-CausalQA-22, a benchmark corpus of 1.1 million causal questions with answers. We distinguish different types of causal questions using a novel typology derived from a data-driven, manual analysis of questions from ten large question answering (QA) datasets. Using high-precision lexical rules, we extract causal questions of each type from these datasets to create our corpus. As an initial baseline, the state-of-the-art QA model UnifiedQA achieves a ROUGE-L F1 score of 0.48 on our new benchmark."}]},{"user_id":"67234","title":"CovidPubGraph: A FAIR Knowledge Graph of COVID-19 Publications","status":"public","date_created":"2022-02-15T16:59:29Z","author":[{"first_name":"Svetlana ","full_name":"Pestryakova, Svetlana ","last_name":"Pestryakova"},{"last_name":"Vollmers","full_name":"Vollmers, Daniel","first_name":"Daniel"},{"last_name":"Sherif","id":"67234","first_name":"Mohamed","full_name":"Sherif, Mohamed","orcid":"https://orcid.org/0000-0002-9927-2203"},{"id":"11871","last_name":"Heindorf","full_name":"Heindorf, Stefan","orcid":"0000-0002-4525-6865","first_name":"Stefan"},{"last_name":"Saleem","full_name":"Saleem, Muhammad ","first_name":"Muhammad "},{"first_name":"Diego","full_name":"Moussallem, Diego","last_name":"Moussallem","id":"71635"},{"full_name":"Ngonga Ngomo, Axel-Cyrille","first_name":"Axel-Cyrille","id":"65716","last_name":"Ngonga Ngomo"}],"publication":"Scientific Data","department":[{"_id":"574"}],"oa":"1","_id":"29851","date_updated":"2023-08-16T10:01:49Z","language":[{"iso":"eng"}],"year":"2022","type":"journal_article","citation":{"bibtex":"@article{Pestryakova_Vollmers_Sherif_Heindorf_Saleem_Moussallem_Ngonga Ngomo_2022, title={CovidPubGraph: A FAIR Knowledge Graph of COVID-19 Publications}, journal={Scientific Data}, author={Pestryakova, Svetlana and Vollmers, Daniel and Sherif, Mohamed and Heindorf, Stefan and Saleem, Muhammad and Moussallem, Diego and Ngonga Ngomo, Axel-Cyrille}, year={2022} }","mla":"Pestryakova, Svetlana, et al. “CovidPubGraph: A FAIR Knowledge Graph of COVID-19 Publications.” Scientific Data, 2022.","apa":"Pestryakova, S., Vollmers, D., Sherif, M., Heindorf, S., Saleem, M., Moussallem, D., & Ngonga Ngomo, A.-C. (2022). CovidPubGraph: A FAIR Knowledge Graph of COVID-19 Publications. Scientific Data.","ama":"Pestryakova S, Vollmers D, Sherif M, et al. CovidPubGraph: A FAIR Knowledge Graph of COVID-19 Publications. Scientific Data. Published online 2022.","chicago":"Pestryakova, Svetlana , Daniel Vollmers, Mohamed Sherif, Stefan Heindorf, Muhammad Saleem, Diego Moussallem, and Axel-Cyrille Ngonga Ngomo. “CovidPubGraph: A FAIR Knowledge Graph of COVID-19 Publications.” Scientific Data, 2022.","ieee":"S. Pestryakova et al., “CovidPubGraph: A FAIR Knowledge Graph of COVID-19 Publications,” Scientific Data, 2022.","short":"S. Pestryakova, D. Vollmers, M. Sherif, S. Heindorf, M. Saleem, D. Moussallem, A.-C. Ngonga Ngomo, Scientific Data (2022)."},"main_file_link":[{"open_access":"1","url":"https://papers.dice-research.org/2022/NSDJ_CovidPubGraph/public.pdf"}]},{"user_id":"44648","related_material":{"link":[{"relation":"confirmation","url":"https://ieeexplore.ieee.org/document/9921520"}]},"title":"AI-Based Assistance System for Manufacturing","abstract":[{"text":"Manufacturing companies are challenged to make the increasingly complex work processes equally manageable for all employees to prevent an impending loss of competence. In this contribution, an intelligent assistance system is proposed enabling employees to help themselves in the workplace and provide them with competence-related support. This results in increasing the short- and long-term efficiency of problem solving in companies.","lang":"eng"}],"status":"public","project":[{"_id":"409","grant_number":"02L19C115","name":"KIAM: KIAM: Kompetenzzentrum KI in der Arbeitswelt des industriellen Mittelstands in OstWestfalenLippe"}],"date_created":"2022-10-28T11:43:49Z","author":[{"last_name":"Deppe","first_name":"Sahar","full_name":"Deppe, Sahar"},{"full_name":"Brandt, Lukas","first_name":"Lukas","last_name":"Brandt"},{"full_name":"Brünninghaus, Marc","first_name":"Marc","last_name":"Brünninghaus"},{"full_name":"Papenkordt, Jörg","first_name":"Jörg","id":"44648","last_name":"Papenkordt"},{"last_name":"Heindorf","id":"11871","first_name":"Stefan","orcid":"0000-0002-4525-6865","full_name":"Heindorf, Stefan"},{"last_name":"Tschirner-Vinke","first_name":"Gudrun","full_name":"Tschirner-Vinke, Gudrun"}],"department":[{"_id":"178"},{"_id":"574"},{"_id":"184"}],"keyword":["Assistance system","Knowledge graph","Information retrieval","Neural networks","AR"],"doi":"10.1109/ETFA52439.2022.9921520","date_updated":"2023-11-23T08:07:51Z","_id":"33957","conference":{"end_date":"2022-09-09","location":"Stuttgart","start_date":"2022-09-06","name":"ETFA"},"language":[{"iso":"eng"}],"year":"2022","citation":{"ieee":"S. Deppe, L. Brandt, M. Brünninghaus, J. Papenkordt, S. Heindorf, and G. Tschirner-Vinke, “AI-Based Assistance System for Manufacturing.” 2022, doi: 10.1109/ETFA52439.2022.9921520.","short":"S. Deppe, L. Brandt, M. Brünninghaus, J. Papenkordt, S. Heindorf, G. Tschirner-Vinke, (2022).","bibtex":"@article{Deppe_Brandt_Brünninghaus_Papenkordt_Heindorf_Tschirner-Vinke_2022, series={2022 IEEE 27th International Conference on Emerging Technologies and Factory Automation (ETFA)}, title={AI-Based Assistance System for Manufacturing}, DOI={10.1109/ETFA52439.2022.9921520}, author={Deppe, Sahar and Brandt, Lukas and Brünninghaus, Marc and Papenkordt, Jörg and Heindorf, Stefan and Tschirner-Vinke, Gudrun}, year={2022}, collection={2022 IEEE 27th International Conference on Emerging Technologies and Factory Automation (ETFA)} }","mla":"Deppe, Sahar, et al. AI-Based Assistance System for Manufacturing. 2022, doi:10.1109/ETFA52439.2022.9921520.","chicago":"Deppe, Sahar, Lukas Brandt, Marc Brünninghaus, Jörg Papenkordt, Stefan Heindorf, and Gudrun Tschirner-Vinke. “AI-Based Assistance System for Manufacturing.” 2022 IEEE 27th International Conference on Emerging Technologies and Factory Automation (ETFA), 2022. https://doi.org/10.1109/ETFA52439.2022.9921520.","apa":"Deppe, S., Brandt, L., Brünninghaus, M., Papenkordt, J., Heindorf, S., & Tschirner-Vinke, G. (2022). AI-Based Assistance System for Manufacturing. ETFA, Stuttgart. https://doi.org/10.1109/ETFA52439.2022.9921520","ama":"Deppe S, Brandt L, Brünninghaus M, Papenkordt J, Heindorf S, Tschirner-Vinke G. AI-Based Assistance System for Manufacturing. Published online 2022. doi:10.1109/ETFA52439.2022.9921520"},"type":"conference","series_title":"2022 IEEE 27th International Conference on Emerging Technologies and Factory Automation (ETFA)"},{"oa":"1","doi":"10.1145/3460210.3493563","date_updated":"2022-10-15T19:40:49Z","_id":"29291","language":[{"iso":"eng"}],"citation":{"chicago":"Zahera, Hamada Mohamed Abdelsamee, Stefan Heindorf, and Axel-Cyrille Ngonga Ngomo. “ASSET: A Semi-Supervised Approach for Entity Typing in Knowledge Graphs.” In Proceedings of the 11th on Knowledge Capture Conference. ACM, 2021. https://doi.org/10.1145/3460210.3493563.","apa":"Zahera, H. M. A., Heindorf, S., & Ngonga Ngomo, A.-C. (2021). ASSET: A Semi-supervised Approach for Entity Typing in Knowledge Graphs. Proceedings of the 11th on Knowledge Capture Conference. https://doi.org/10.1145/3460210.3493563","ama":"Zahera HMA, Heindorf S, Ngonga Ngomo A-C. ASSET: A Semi-supervised Approach for Entity Typing in Knowledge Graphs. In: Proceedings of the 11th on Knowledge Capture Conference. ACM; 2021. doi:10.1145/3460210.3493563","bibtex":"@inproceedings{Zahera_Heindorf_Ngonga Ngomo_2021, title={ASSET: A Semi-supervised Approach for Entity Typing in Knowledge Graphs}, DOI={10.1145/3460210.3493563}, booktitle={Proceedings of the 11th on Knowledge Capture Conference}, publisher={ACM}, author={Zahera, Hamada Mohamed Abdelsamee and Heindorf, Stefan and Ngonga Ngomo, Axel-Cyrille}, year={2021} }","mla":"Zahera, Hamada Mohamed Abdelsamee, et al. “ASSET: A Semi-Supervised Approach for Entity Typing in Knowledge Graphs.” Proceedings of the 11th on Knowledge Capture Conference, ACM, 2021, doi:10.1145/3460210.3493563.","short":"H.M.A. Zahera, S. Heindorf, A.-C. Ngonga Ngomo, in: Proceedings of the 11th on Knowledge Capture Conference, ACM, 2021.","ieee":"H. M. A. Zahera, S. Heindorf, and A.-C. Ngonga Ngomo, “ASSET: A Semi-supervised Approach for Entity Typing in Knowledge Graphs,” 2021, doi: 10.1145/3460210.3493563."},"type":"conference","year":"2021","main_file_link":[{"open_access":"1","url":"https://papers.dice-research.org/2021/KCAP2021_ASSET/public.pdf"}],"user_id":"11871","title":"ASSET: A Semi-supervised Approach for Entity Typing in Knowledge Graphs","date_created":"2022-01-12T10:27:02Z","status":"public","publication_status":"published","publication":"Proceedings of the 11th on Knowledge Capture Conference","department":[{"_id":"574"}],"publisher":"ACM","author":[{"last_name":"Zahera","id":"72768","first_name":"Hamada Mohamed Abdelsamee","full_name":"Zahera, Hamada Mohamed Abdelsamee"},{"last_name":"Heindorf","id":"11871","first_name":"Stefan","full_name":"Heindorf, Stefan","orcid":"0000-0002-4525-6865"},{"first_name":"Axel-Cyrille","full_name":"Ngonga Ngomo, Axel-Cyrille","last_name":"Ngonga Ngomo"}]},{"user_id":"11871","status":"public","date_created":"2022-01-12T10:27:23Z","author":[{"full_name":"Feldhans, Robert","first_name":"Robert","last_name":"Feldhans"},{"last_name":"Wilke","id":"9101","first_name":"Adrian","orcid":"0000-0002-6575-807X","full_name":"Wilke, Adrian"},{"first_name":"Stefan","full_name":"Heindorf, Stefan","orcid":"0000-0002-4525-6865","last_name":"Heindorf","id":"11871"},{"full_name":"Shaker, Mohammad Hossein","first_name":"Mohammad Hossein","last_name":"Shaker"},{"full_name":"Hammer, Barbara","first_name":"Barbara","last_name":"Hammer"},{"full_name":"Ngonga Ngomo, Axel-Cyrille","first_name":"Axel-Cyrille","id":"65716","last_name":"Ngonga Ngomo"},{"id":"48129","last_name":"Hüllermeier","full_name":"Hüllermeier, Eyke","first_name":"Eyke"}],"publisher":"Springer International Publishing","publication":"Intelligent Data Engineering and Automated Learning – IDEAL 2021","_id":"29292","type":"book_chapter","year":"2021","citation":{"ieee":"R. Feldhans et al., “Drift Detection in Text Data with Document Embeddings,” in Intelligent Data Engineering and Automated Learning – IDEAL 2021, Cham: Springer International Publishing, 2021.","short":"R. Feldhans, A. Wilke, S. Heindorf, M.H. Shaker, B. Hammer, A.-C. Ngonga Ngomo, E. Hüllermeier, in: Intelligent Data Engineering and Automated Learning – IDEAL 2021, Springer International Publishing, Cham, 2021.","mla":"Feldhans, Robert, et al. “Drift Detection in Text Data with Document Embeddings.” Intelligent Data Engineering and Automated Learning – IDEAL 2021, Springer International Publishing, 2021, doi:10.1007/978-3-030-91608-4_11.","bibtex":"@inbook{Feldhans_Wilke_Heindorf_Shaker_Hammer_Ngonga Ngomo_Hüllermeier_2021, place={Cham}, title={Drift Detection in Text Data with Document Embeddings}, DOI={10.1007/978-3-030-91608-4_11}, booktitle={Intelligent Data Engineering and Automated Learning – IDEAL 2021}, publisher={Springer International Publishing}, author={Feldhans, Robert and Wilke, Adrian and Heindorf, Stefan and Shaker, Mohammad Hossein and Hammer, Barbara and Ngonga Ngomo, Axel-Cyrille and Hüllermeier, Eyke}, year={2021} }","chicago":"Feldhans, Robert, Adrian Wilke, Stefan Heindorf, Mohammad Hossein Shaker, Barbara Hammer, Axel-Cyrille Ngonga Ngomo, and Eyke Hüllermeier. “Drift Detection in Text Data with Document Embeddings.” In Intelligent Data Engineering and Automated Learning – IDEAL 2021. Cham: Springer International Publishing, 2021. https://doi.org/10.1007/978-3-030-91608-4_11.","ama":"Feldhans R, Wilke A, Heindorf S, et al. Drift Detection in Text Data with Document Embeddings. In: Intelligent Data Engineering and Automated Learning – IDEAL 2021. Springer International Publishing; 2021. doi:10.1007/978-3-030-91608-4_11","apa":"Feldhans, R., Wilke, A., Heindorf, S., Shaker, M. H., Hammer, B., Ngonga Ngomo, A.-C., & Hüllermeier, E. (2021). Drift Detection in Text Data with Document Embeddings. In Intelligent Data Engineering and Automated Learning – IDEAL 2021. Springer International Publishing. https://doi.org/10.1007/978-3-030-91608-4_11"},"main_file_link":[{"open_access":"1","url":"https://papers.dice-research.org/2021/IDEAL2021_DriftDetectionEmbeddings/Drift-Detection-in-Text-Data-with-Document-Embeddings-public.pdf"}],"title":"Drift Detection in Text Data with Document Embeddings","related_material":{"link":[{"relation":"confirmation","url":"https://link.springer.com/chapter/10.1007/978-3-030-91608-4_11"}]},"place":"Cham","publication_identifier":{"isbn":["9783030916077","9783030916084"],"issn":["0302-9743","1611-3349"]},"publication_status":"published","department":[{"_id":"574"}],"doi":"10.1007/978-3-030-91608-4_11","oa":"1","date_updated":"2022-10-15T19:54:20Z","language":[{"iso":"eng"}]},{"status":"public","date_created":"2022-01-12T10:21:10Z","author":[{"last_name":"Demir","id":"43817","first_name":"Caglar","full_name":"Demir, Caglar"},{"id":"71635","last_name":"Moussallem","full_name":"Moussallem, Diego","first_name":"Diego"},{"last_name":"Heindorf","id":"11871","first_name":"Stefan","full_name":"Heindorf, Stefan","orcid":"0000-0002-4525-6865"},{"full_name":"Ngonga Ngomo, Axel-Cyrille","first_name":"Axel-Cyrille","id":"65716","last_name":"Ngonga Ngomo"}],"department":[{"_id":"574"}],"publication":"The 13th Asian Conference on Machine Learning, ACML 2021","user_id":"11871","title":"Convolutional Hypercomplex Embeddings for Link Prediction","abstract":[{"lang":"eng","text":"Knowledge graph embedding research has mainly focused on the two smallest\r\nnormed division algebras, $\\mathbb{R}$ and $\\mathbb{C}$. Recent results suggest\r\nthat trilinear products of quaternion-valued embeddings can be a more effective\r\nmeans to tackle link prediction. In addition, models based on convolutions on\r\nreal-valued embeddings often yield state-of-the-art results for link\r\nprediction. In this paper, we investigate a composition of convolution\r\noperations with hypercomplex multiplications. We propose the four approaches\r\nQMult, OMult, ConvQ and ConvO to tackle the link prediction problem. QMult and\r\nOMult can be considered as quaternion and octonion extensions of previous\r\nstate-of-the-art approaches, including DistMult and ComplEx. ConvQ and ConvO\r\nbuild upon QMult and OMult by including convolution operations in a way\r\ninspired by the residual learning framework. We evaluated our approaches on\r\nseven link prediction datasets including WN18RR, FB15K-237 and YAGO3-10.\r\nExperimental results suggest that the benefits of learning hypercomplex-valued\r\nvector representations become more apparent as the size and complexity of the\r\nknowledge graph grows. ConvO outperforms state-of-the-art approaches on\r\nFB15K-237 in MRR, Hit@1 and Hit@3, while QMult, OMult, ConvQ and ConvO\r\noutperform state-of-the-approaches on YAGO3-10 in all metrics. Results also\r\nsuggest that link prediction performances can be further improved via\r\nprediction averaging. To foster reproducible research, we provide an\r\nopen-source implementation of approaches, including training and evaluation\r\nscripts as well as pretrained models."}],"external_id":{"arxiv":["2106.15230"]},"language":[{"iso":"eng"}],"citation":{"ieee":"C. Demir, D. Moussallem, S. Heindorf, and A.-C. Ngonga Ngomo, “Convolutional Hypercomplex Embeddings for Link Prediction,” 2021.","short":"C. Demir, D. Moussallem, S. Heindorf, A.-C. Ngonga Ngomo, in: The 13th Asian Conference on Machine Learning, ACML 2021, 2021.","mla":"Demir, Caglar, et al. “Convolutional Hypercomplex Embeddings for Link Prediction.” The 13th Asian Conference on Machine Learning, ACML 2021, 2021.","bibtex":"@inproceedings{Demir_Moussallem_Heindorf_Ngonga Ngomo_2021, title={Convolutional Hypercomplex Embeddings for Link Prediction}, booktitle={The 13th Asian Conference on Machine Learning, ACML 2021}, author={Demir, Caglar and Moussallem, Diego and Heindorf, Stefan and Ngonga Ngomo, Axel-Cyrille}, year={2021} }","apa":"Demir, C., Moussallem, D., Heindorf, S., & Ngonga Ngomo, A.-C. (2021). Convolutional Hypercomplex Embeddings for Link Prediction. The 13th Asian Conference on Machine Learning, ACML 2021.","ama":"Demir C, Moussallem D, Heindorf S, Ngonga Ngomo A-C. Convolutional Hypercomplex Embeddings for Link Prediction. In: The 13th Asian Conference on Machine Learning, ACML 2021. ; 2021.","chicago":"Demir, Caglar, Diego Moussallem, Stefan Heindorf, and Axel-Cyrille Ngonga Ngomo. “Convolutional Hypercomplex Embeddings for Link Prediction.” In The 13th Asian Conference on Machine Learning, ACML 2021, 2021."},"type":"conference","year":"2021","main_file_link":[{"open_access":"1","url":"https://papers.dice-research.org/2021/ACML2021_HyperConv/public.pdf"}],"oa":"1","_id":"29287","date_updated":"2022-10-17T15:06:40Z"},{"user_id":"11871","title":"Generating Physically Sound Training Data for Image Recognition of Additively Manufactured Parts","publication":"2021 IEEE Winter Conference on Applications of Computer Vision (WACV)","department":[{"_id":"66"},{"_id":"574"}],"author":[{"first_name":"Tobias","orcid":"0000-0001-8958-9330","full_name":"Nickchen, Tobias","last_name":"Nickchen","id":"27340"},{"first_name":"Stefan","orcid":"0000-0002-4525-6865","full_name":"Heindorf, Stefan","last_name":"Heindorf","id":"11871"},{"last_name":"Engels","id":"107","first_name":"Gregor","full_name":"Engels, Gregor"}],"publisher":"IEEE","date_created":"2022-01-12T10:31:42Z","project":[{"name":"PC2: Computing Resources Provided by the Paderborn Center for Parallel Computing","_id":"52"}],"status":"public","publication_status":"published","_id":"29294","date_updated":"2022-10-17T15:07:38Z","oa":"1","doi":"10.1109/wacv48630.2021.00204","main_file_link":[{"url":"https://openaccess.thecvf.com/content/WACV2021/papers/Nickchen_Generating_Physically_Sound_Training_Data_for_Image_Recognition_of_Additively_WACV_2021_paper.pdf","open_access":"1"}],"language":[{"iso":"eng"}],"type":"conference","citation":{"ieee":"T. Nickchen, S. Heindorf, and G. Engels, “Generating Physically Sound Training Data for Image Recognition of Additively Manufactured Parts,” 2021, doi: 10.1109/wacv48630.2021.00204.","short":"T. Nickchen, S. Heindorf, G. Engels, in: 2021 IEEE Winter Conference on Applications of Computer Vision (WACV), IEEE, 2021.","bibtex":"@inproceedings{Nickchen_Heindorf_Engels_2021, title={Generating Physically Sound Training Data for Image Recognition of Additively Manufactured Parts}, DOI={10.1109/wacv48630.2021.00204}, booktitle={2021 IEEE Winter Conference on Applications of Computer Vision (WACV)}, publisher={IEEE}, author={Nickchen, Tobias and Heindorf, Stefan and Engels, Gregor}, year={2021} }","mla":"Nickchen, Tobias, et al. “Generating Physically Sound Training Data for Image Recognition of Additively Manufactured Parts.” 2021 IEEE Winter Conference on Applications of Computer Vision (WACV), IEEE, 2021, doi:10.1109/wacv48630.2021.00204.","ama":"Nickchen T, Heindorf S, Engels G. Generating Physically Sound Training Data for Image Recognition of Additively Manufactured Parts. In: 2021 IEEE Winter Conference on Applications of Computer Vision (WACV). IEEE; 2021. doi:10.1109/wacv48630.2021.00204","apa":"Nickchen, T., Heindorf, S., & Engels, G. (2021). Generating Physically Sound Training Data for Image Recognition of Additively Manufactured Parts. 2021 IEEE Winter Conference on Applications of Computer Vision (WACV). https://doi.org/10.1109/wacv48630.2021.00204","chicago":"Nickchen, Tobias, Stefan Heindorf, and Gregor Engels. “Generating Physically Sound Training Data for Image Recognition of Additively Manufactured Parts.” In 2021 IEEE Winter Conference on Applications of Computer Vision (WACV). IEEE, 2021. https://doi.org/10.1109/wacv48630.2021.00204."},"year":"2021"},{"citation":{"chicago":"Heindorf, Stefan. “Automatically Generating Instructions from Tutorials for Search and User Navigation,” 2021.","apa":"Heindorf, S. (2021). Automatically generating instructions from tutorials for search and user navigation.","ama":"Heindorf S. Automatically generating instructions from tutorials for search and user navigation. Published online 2021.","mla":"Heindorf, Stefan. Automatically Generating Instructions from Tutorials for Search and User Navigation. 2021.","bibtex":"@article{Heindorf_2021, title={Automatically generating instructions from tutorials for search and user navigation}, author={Heindorf, Stefan}, year={2021} }","short":"S. Heindorf, (2021).","ieee":"S. Heindorf, “Automatically generating instructions from tutorials for search and user navigation.” 2021."},"year":"2021","type":"patent","main_file_link":[{"open_access":"1","url":"https://patentimages.storage.googleapis.com/f6/21/79/657191c3dfe93b/US10936684.pdf"}],"oa":"1","publication_date":"2021-03-02","_id":"33733","date_updated":"2022-10-17T15:21:49Z","status":"public","ipc":"US15/885,363 ","date_created":"2022-10-15T19:19:10Z","author":[{"last_name":"Heindorf","id":"11871","first_name":"Stefan","orcid":"0000-0002-4525-6865","full_name":"Heindorf, Stefan"}],"user_id":"11871","title":"Automatically generating instructions from tutorials for search and user navigation","ipn":"10936684"},{"status":"public","date_created":"2020-10-20T13:11:14Z","project":[{"name":"Computing Resources Provided by the Paderborn Center for Parallel Computing","_id":"52"}],"author":[{"last_name":"Heindorf","id":"11871","first_name":"Stefan","full_name":"Heindorf, Stefan","orcid":"0000-0002-4525-6865"},{"last_name":"Scholten","full_name":"Scholten, Yan","first_name":"Yan"},{"first_name":"Henning","full_name":"Wachsmuth, Henning","last_name":"Wachsmuth","id":"3900"},{"full_name":"Ngonga Ngomo, Axel-Cyrille","first_name":"Axel-Cyrille","id":"65716","last_name":"Ngonga Ngomo"},{"full_name":"Potthast, Martin","first_name":"Martin","last_name":"Potthast"}],"department":[{"_id":"574"}],"publication":"Proceedings of the 28th ACM International Conference on Information and Knowledge Management (CIKM 2020)","title":"CauseNet: Towards a Causality Graph Extracted from the Web","user_id":"11871","citation":{"bibtex":"@inproceedings{Heindorf_Scholten_Wachsmuth_Ngonga Ngomo_Potthast_2020, title={CauseNet: Towards a Causality Graph Extracted from the Web}, DOI={10.1145/3340531.3412763}, booktitle={Proceedings of the 28th ACM International Conference on Information and Knowledge Management (CIKM 2020)}, author={Heindorf, Stefan and Scholten, Yan and Wachsmuth, Henning and Ngonga Ngomo, Axel-Cyrille and Potthast, Martin}, year={2020}, pages={3023–3030} }","mla":"Heindorf, Stefan, et al. “CauseNet: Towards a Causality Graph Extracted from the Web.” Proceedings of the 28th ACM International Conference on Information and Knowledge Management (CIKM 2020), 2020, pp. 3023–30, doi:10.1145/3340531.3412763.","apa":"Heindorf, S., Scholten, Y., Wachsmuth, H., Ngonga Ngomo, A.-C., & Potthast, M. (2020). CauseNet: Towards a Causality Graph Extracted from the Web. Proceedings of the 28th ACM International Conference on Information and Knowledge Management (CIKM 2020), 3023–3030. https://doi.org/10.1145/3340531.3412763","ama":"Heindorf S, Scholten Y, Wachsmuth H, Ngonga Ngomo A-C, Potthast M. CauseNet: Towards a Causality Graph Extracted from the Web. In: Proceedings of the 28th ACM International Conference on Information and Knowledge Management (CIKM 2020). ; 2020:3023-3030. doi:10.1145/3340531.3412763","chicago":"Heindorf, Stefan, Yan Scholten, Henning Wachsmuth, Axel-Cyrille Ngonga Ngomo, and Martin Potthast. “CauseNet: Towards a Causality Graph Extracted from the Web.” In Proceedings of the 28th ACM International Conference on Information and Knowledge Management (CIKM 2020), 3023–30, 2020. https://doi.org/10.1145/3340531.3412763.","ieee":"S. Heindorf, Y. Scholten, H. Wachsmuth, A.-C. Ngonga Ngomo, and M. Potthast, “CauseNet: Towards a Causality Graph Extracted from the Web,” in Proceedings of the 28th ACM International Conference on Information and Knowledge Management (CIKM 2020), 2020, pp. 3023–3030, doi: 10.1145/3340531.3412763.","short":"S. Heindorf, Y. Scholten, H. Wachsmuth, A.-C. Ngonga Ngomo, M. 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Heindorf, Y. Scholten, G. Engels, M. Potthast, in: WWW, ACM, 2019, pp. 670–680.","ieee":"S. Heindorf, Y. Scholten, G. Engels, and M. Potthast, “Debiasing Vandalism Detection Models at Wikidata,” in WWW, San Francisco, USA, 2019, pp. 670–680.","apa":"Heindorf, S., Scholten, Y., Engels, G., & Potthast, M. (2019). Debiasing Vandalism Detection Models at Wikidata. In WWW (pp. 670–680). San Francisco, USA: ACM. https://doi.org/10.1145/3308558.3313507","ama":"Heindorf S, Scholten Y, Engels G, Potthast M. Debiasing Vandalism Detection Models at Wikidata. In: WWW. ACM; 2019:670-680. doi:10.1145/3308558.3313507","chicago":"Heindorf, Stefan, Yan Scholten, Gregor Engels, and Martin Potthast. “Debiasing Vandalism Detection Models at Wikidata.” In WWW, 670–80. 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