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Alam (Eds.), <i>The Semantic Web - 18th International Conference, {ESWC} 2021, Virtual Event, June 6-10, 2021, Proceedings</i> (Vol. 12731, pp. 409–424). Springer. <a href=\"https://doi.org/10.1007/978-3-030-77385-4\\_24\">https://doi.org/10.1007/978-3-030-77385-4\\_24</a>"},"publication":"The Semantic Web - 18th International Conference, {ESWC} 2021, Virtual Event, June 6-10, 2021, Proceedings","department":[{"_id":"574"}],"type":"conference","date_created":"2021-10-01T06:45:57Z"},{"citation":{"mla":"Speck, Ren{\\’{e}}, et al. “Twitter Network Mimicking for Data Storage Benchmarking.” <i>15th {IEEE} International Conference on Semantic Computing, {ICSC} 2021, Laguna Hills, CA, USA, January 27-29, 2021</i>, {IEEE}, 2021, pp. 298–305, doi:<a href=\"https://doi.org/10.1109/ICSC50631.2021.00057\">10.1109/ICSC50631.2021.00057</a>.","bibtex":"@inproceedings{Speck_Moussallem_Ngonga Ngomo_2021, title={Twitter Network Mimicking for Data Storage Benchmarking}, DOI={<a href=\"https://doi.org/10.1109/ICSC50631.2021.00057\">10.1109/ICSC50631.2021.00057</a>}, booktitle={15th {IEEE} International Conference on Semantic Computing, {ICSC} 2021, Laguna Hills, CA, USA, January 27-29, 2021}, publisher={{IEEE}}, author={Speck, Ren{\\’{e}} and Moussallem, Diego and Ngonga Ngomo, Axel-Cyrille}, year={2021}, pages={298–305} }","ama":"Speck R, Moussallem D, Ngonga Ngomo A-C. 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MLCheck- Property-Driven Testing of Machine Learning Models. <i>CoRR</i>, <i>abs/2105.00741</i>."},"date_created":"2021-10-01T06:54:10Z","type":"journal_article","department":[{"_id":"574"}],"year":"2021","title":"MLCheck- Property-Driven Testing of Machine Learning Models","status":"public","author":[{"last_name":"Sharma","first_name":"Arnab","full_name":"Sharma, Arnab","id":"67200"},{"last_name":"Demir","first_name":"Caglar","full_name":"Demir, Caglar","id":"43817"},{"full_name":"Ngonga Ngomo, Axel-Cyrille","last_name":"Ngonga Ngomo","first_name":"Axel-Cyrille","id":"65716"},{"first_name":"Heike","last_name":"Wehrheim","full_name":"Wehrheim, Heike","id":"573"}],"date_updated":"2022-01-06T06:56:55Z","_id":"25213","language":[{"iso":"eng"}],"user_id":"65716","volume":"abs/2105.00741"},{"date_updated":"2022-01-06T06:56:55Z","year":"2021","title":"Open Data Portal Germany (OPAL) Projektergebnisse","status":"public","author":[{"last_name":"Wilke","orcid":"0000-0002-6575-807X","first_name":"Adrian","full_name":"Wilke, Adrian","id":"9101"},{"first_name":"Axel-Cyrille","last_name":"Ngonga Ngomo","full_name":"Ngonga Ngomo, Axel-Cyrille","id":"65716"}],"user_id":"65716","volume":"abs/2105.03161","_id":"25214","language":[{"iso":"eng"}],"publication":"CoRR","citation":{"mla":"Wilke, Adrian, and Axel-Cyrille Ngonga Ngomo. “Open Data Portal Germany (OPAL) Projektergebnisse.” <i>CoRR</i>, vol. abs/2105.03161, 2021.","ama":"Wilke A, Ngonga Ngomo A-C. 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Out-of-Vocabulary Entities in Link Prediction. <i>CoRR</i>, <i>abs/2105.12524</i>.","ieee":"C. Demir and A.-C. Ngonga Ngomo, “Out-of-Vocabulary Entities in Link Prediction,” <i>CoRR</i>, vol. abs/2105.12524, 2021.","short":"C. Demir, A.-C. Ngonga Ngomo, CoRR abs/2105.12524 (2021).","chicago":"Demir, Caglar, and Axel-Cyrille Ngonga Ngomo. “Out-of-Vocabulary Entities in Link Prediction.” <i>CoRR</i> abs/2105.12524 (2021).","mla":"Demir, Caglar, and Axel-Cyrille Ngonga Ngomo. “Out-of-Vocabulary Entities in Link Prediction.” <i>CoRR</i>, vol. abs/2105.12524, 2021.","ama":"Demir C, Ngonga Ngomo A-C. 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DRILL- Deep Reinforcement Learning for Refinement Operators in ALC. <i>CoRR</i>. 2021;abs/2106.15373.","chicago":"Demir, Caglar, and Axel-Cyrille Ngonga Ngomo. “DRILL- Deep Reinforcement Learning for Refinement Operators in ALC.” <i>CoRR</i> abs/2106.15373 (2021).","bibtex":"@article{Demir_Ngonga Ngomo_2021, title={DRILL- Deep Reinforcement Learning for Refinement Operators in ALC}, volume={abs/2106.15373}, journal={CoRR}, author={Demir, Caglar and Ngonga Ngomo, Axel-Cyrille}, year={2021} }","mla":"Demir, Caglar, and Axel-Cyrille Ngonga Ngomo. “DRILL- Deep Reinforcement Learning for Refinement Operators in ALC.” <i>CoRR</i>, vol. abs/2106.15373, 2021.","apa":"Demir, C., &#38; Ngonga Ngomo, A.-C. (2021). DRILL- Deep Reinforcement Learning for Refinement Operators in ALC. <i>CoRR</i>, <i>abs/2106.15373</i>.","ieee":"C. Demir and A.-C. 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MLCHECK–Property-Driven Testing of Machine Learning Classifiers. In: <i>Proceedings of the 20th IEEE International Conference on Machine Learning and Applications (ICMLA)</i>. IEEE.","bibtex":"@inproceedings{Sharma_Demir_Ngonga Ngomo_Wehrheim, title={MLCHECK–Property-Driven Testing of Machine Learning Classifiers}, booktitle={Proceedings of the 20th IEEE International Conference on Machine Learning and Applications (ICMLA)}, publisher={IEEE}, author={Sharma, Arnab and Demir, Caglar and Ngonga Ngomo, Axel-Cyrille and Wehrheim, Heike} }","apa":"Sharma, A., Demir, C., Ngonga Ngomo, A.-C., &#38; Wehrheim, H. (n.d.). MLCHECK–Property-Driven Testing of Machine Learning Classifiers. <i>Proceedings of the 20th IEEE International Conference on Machine Learning and Applications (ICMLA)</i>.","ieee":"A. Sharma, C. Demir, A.-C. Ngonga Ngomo, and H. Wehrheim, “MLCHECK–Property-Driven Testing of Machine Learning Classifiers.”","short":"A. Sharma, C. Demir, A.-C. Ngonga Ngomo, H. Wehrheim, in: Proceedings of the 20th IEEE International Conference on Machine Learning and Applications (ICMLA), IEEE, n.d.","chicago":"Sharma, Arnab, Caglar Demir, Axel-Cyrille Ngonga Ngomo, and Heike Wehrheim. “MLCHECK–Property-Driven Testing of Machine Learning Classifiers.” In <i>Proceedings of the 20th IEEE International Conference on Machine Learning and Applications (ICMLA)</i>. IEEE, n.d."},"publication":"Proceedings of the 20th IEEE International Conference on Machine Learning and Applications (ICMLA)","project":[{"name":"SFB 901","_id":"1"},{"name":"SFB 901 - Project Area B","_id":"3"},{"_id":"11","name":"SFB 901 - Subproject B3"},{"name":"SFB 901 - Subproject B2","_id":"10"}],"abstract":[{"lang":"eng","text":"In recent years, we observe an increasing amount of software with machine learning components being deployed. This poses the question of quality assurance for such components: how can we validate whether specified requirements are fulfilled by a machine learned software? Current testing and verification approaches either focus on a single requirement (e.g., fairness) or specialize on a single type of machine learning model (e.g., neural networks).\r\nIn this paper, we propose property-driven testing of machine learning models. Our approach MLCheck encompasses (1) a language for property specification, and (2) a technique for systematic test case generation. The specification language is comparable to property-based testing languages. Test case generation employs advanced verification technology for a systematic, property dependent construction of test suites, without additional user supplied generator functions. We evaluate MLCheck using requirements and data sets from three different application areas (software\r\ndiscrimination, learning on knowledge graphs and security). Our evaluation shows that despite its generality MLCheck can even outperform specialised testing approaches while having a comparable runtime"}],"_id":"28350","publisher":"IEEE","language":[{"iso":"eng"}],"user_id":"477","author":[{"last_name":"Sharma","first_name":"Arnab","full_name":"Sharma, Arnab","id":"67200"},{"id":"43817","full_name":"Demir, Caglar","first_name":"Caglar","last_name":"Demir"},{"id":"65716","last_name":"Ngonga Ngomo","first_name":"Axel-Cyrille","full_name":"Ngonga Ngomo, Axel-Cyrille"},{"id":"573","full_name":"Wehrheim, Heike","last_name":"Wehrheim","first_name":"Heike"}],"title":"MLCHECK–Property-Driven Testing of Machine Learning Classifiers","status":"public","year":"2021","publication_status":"accepted","date_updated":"2022-01-06T06:58:02Z"},{"date_created":"2021-09-20T13:01:18Z","type":"journal_article","department":[{"_id":"574"}],"publication":"{IEEE} Access","citation":{"bibtex":"@article{M. Zahera_Jalota_Ahmed Sherif_Ngonga Ngomo_2021, title={I-AID: Identifying Actionable Information From Disaster-Related Tweets}, volume={9}, DOI={<a href=\"https://doi.org/10.1109/ACCESS.2021.3107812\">10.1109/ACCESS.2021.3107812</a>}, journal={{IEEE} Access}, author={M. Zahera, Hamada and Jalota, Rricha and Ahmed Sherif, Mohamed and Ngonga Ngomo, Axel-Cyrille}, year={2021}, pages={118861–118870} }","chicago":"M. Zahera, Hamada, Rricha Jalota, Mohamed Ahmed Sherif, and Axel-Cyrille Ngonga Ngomo. “I-AID: Identifying Actionable Information From Disaster-Related Tweets.” <i>{IEEE} Access</i> 9 (2021): 118861–70. <a href=\"https://doi.org/10.1109/ACCESS.2021.3107812\">https://doi.org/10.1109/ACCESS.2021.3107812</a>.","short":"H. M. Zahera, R. Jalota, M. Ahmed Sherif, A.-C. Ngonga Ngomo, {IEEE} Access 9 (2021) 118861–118870.","ama":"M. Zahera H, Jalota R, Ahmed Sherif M, Ngonga Ngomo A-C. I-AID: Identifying Actionable Information From Disaster-Related Tweets. <i>{IEEE} Access</i>. 2021;9:118861-118870. doi:<a href=\"https://doi.org/10.1109/ACCESS.2021.3107812\">10.1109/ACCESS.2021.3107812</a>","ieee":"H. M. Zahera, R. Jalota, M. Ahmed Sherif, and A.-C. Ngonga Ngomo, “I-AID: Identifying Actionable Information From Disaster-Related Tweets,” <i>{IEEE} Access</i>, vol. 9, pp. 118861–118870, 2021, doi: <a href=\"https://doi.org/10.1109/ACCESS.2021.3107812\">10.1109/ACCESS.2021.3107812</a>.","mla":"M. Zahera, Hamada, et al. “I-AID: Identifying Actionable Information From Disaster-Related Tweets.” <i>{IEEE} Access</i>, vol. 9, 2021, pp. 118861–70, doi:<a href=\"https://doi.org/10.1109/ACCESS.2021.3107812\">10.1109/ACCESS.2021.3107812</a>.","apa":"M. Zahera, H., Jalota, R., Ahmed Sherif, M., &#38; Ngonga Ngomo, A.-C. (2021). I-AID: Identifying Actionable Information From Disaster-Related Tweets. <i>{IEEE} Access</i>, <i>9</i>, 118861–118870. <a href=\"https://doi.org/10.1109/ACCESS.2021.3107812\">https://doi.org/10.1109/ACCESS.2021.3107812</a>"},"page":"118861-118870","language":[{"iso":"eng"}],"_id":"24719","user_id":"65716","doi":"10.1109/ACCESS.2021.3107812","volume":9,"title":"I-AID: Identifying Actionable Information From Disaster-Related Tweets","year":"2021","status":"public","author":[{"last_name":"M. Zahera","first_name":"Hamada","full_name":"M. Zahera, Hamada"},{"first_name":"Rricha","last_name":"Jalota","full_name":"Jalota, Rricha"},{"full_name":"Ahmed Sherif, Mohamed","first_name":"Mohamed","last_name":"Ahmed Sherif"},{"full_name":"Ngonga Ngomo, Axel-Cyrille","first_name":"Axel-Cyrille","last_name":"Ngonga Ngomo","id":"65716"}],"date_updated":"2022-01-06T06:56:32Z","intvolume":"         9"},{"author":[{"last_name":"Hogan","first_name":"Aidan","full_name":"Hogan, Aidan"},{"last_name":"Blomqvist","first_name":"Eva","full_name":"Blomqvist, Eva"},{"full_name":"Cochez, Michael","last_name":"Cochez","first_name":"Michael"},{"full_name":"d'Amato, Claudia","last_name":"d'Amato","first_name":"Claudia"},{"last_name":"de Melo","first_name":"Gerard","full_name":"de Melo, Gerard"},{"first_name":"Claudio","last_name":"Guti{\\'{e}}rrez","full_name":"Guti{\\'{e}}rrez, Claudio"},{"full_name":"Kirrane, Sabrina","last_name":"Kirrane","first_name":"Sabrina"},{"full_name":"Emilio Labra Gayo, Jos{\\'{e}}","last_name":"Emilio Labra Gayo","first_name":"Jos{\\'{e}}"},{"full_name":"Navigli, Roberto","first_name":"Roberto","last_name":"Navigli"},{"full_name":"Neumaier, Sebastian","last_name":"Neumaier","first_name":"Sebastian"},{"full_name":"Ngonga Ngomo, Axel-Cyrille","first_name":"Axel-Cyrille","last_name":"Ngonga Ngomo","id":"65716"},{"full_name":"Polleres, Axel","last_name":"Polleres","first_name":"Axel"},{"first_name":"Sabbir","last_name":"M. Rashid","full_name":"M. Rashid, Sabbir"},{"last_name":"Rula","first_name":"Anisa","full_name":"Rula, Anisa"},{"full_name":"Schmelzeisen, Lukas","first_name":"Lukas","last_name":"Schmelzeisen"},{"full_name":"F. Sequeda, Juan","last_name":"F. Sequeda","first_name":"Juan"},{"full_name":"Staab, Steffen","last_name":"Staab","first_name":"Steffen"},{"full_name":"Zimmermann, Antoine","first_name":"Antoine","last_name":"Zimmermann"}],"title":"Knowledge Graphs","status":"public","year":"2021","intvolume":"        54","date_updated":"2022-01-06T06:56:33Z","language":[{"iso":"eng"}],"_id":"24720","page":"71:1-71:37","volume":54,"doi":"10.1145/3447772","user_id":"65716","citation":{"apa":"Hogan, A., Blomqvist, E., Cochez, M., d’Amato, C., de Melo, G., Guti{\\’{e}}rrez, C., Kirrane, S., Emilio Labra Gayo, J., Navigli, R., Neumaier, S., Ngonga Ngomo, A.-C., Polleres, A., M. Rashid, S., Rula, A., Schmelzeisen, L., F. Sequeda, J., Staab, S., &#38; Zimmermann, A. (2021). Knowledge Graphs. <i>{ACM} Comput. Surv.</i>, <i>54</i>(4), 71:1-71:37. <a href=\"https://doi.org/10.1145/3447772\">https://doi.org/10.1145/3447772</a>","mla":"Hogan, Aidan, et al. “Knowledge Graphs.” <i>{ACM} Comput. Surv.</i>, vol. 54, no. 4, 2021, p. 71:1-71:37, doi:<a href=\"https://doi.org/10.1145/3447772\">10.1145/3447772</a>.","ieee":"A. Hogan <i>et al.</i>, “Knowledge Graphs,” <i>{ACM} Comput. Surv.</i>, vol. 54, no. 4, p. 71:1-71:37, 2021, doi: <a href=\"https://doi.org/10.1145/3447772\">10.1145/3447772</a>.","chicago":"Hogan, Aidan, Eva Blomqvist, Michael Cochez, Claudia d’Amato, Gerard de Melo, Claudio Guti{\\’{e}}rrez, Sabrina Kirrane, et al. “Knowledge Graphs.” <i>{ACM} Comput. Surv.</i> 54, no. 4 (2021): 71:1-71:37. <a href=\"https://doi.org/10.1145/3447772\">https://doi.org/10.1145/3447772</a>.","ama":"Hogan A, Blomqvist E, Cochez M, et al. Knowledge Graphs. <i>{ACM} Comput Surv</i>. 2021;54(4):71:1-71:37. doi:<a href=\"https://doi.org/10.1145/3447772\">10.1145/3447772</a>","short":"A. Hogan, E. Blomqvist, M. Cochez, C. d’Amato, G. de Melo, C. Guti{\\’{e}}rrez, S. Kirrane, J. Emilio Labra Gayo, R. Navigli, S. Neumaier, A.-C. Ngonga Ngomo, A. Polleres, S. M. Rashid, A. Rula, L. Schmelzeisen, J. F. Sequeda, S. Staab, A. Zimmermann, {ACM} Comput. Surv. 54 (2021) 71:1-71:37.","bibtex":"@article{Hogan_Blomqvist_Cochez_d’Amato_de Melo_Guti{\\’{e}}rrez_Kirrane_Emilio Labra Gayo_Navigli_Neumaier_et al._2021, title={Knowledge Graphs}, volume={54}, DOI={<a href=\"https://doi.org/10.1145/3447772\">10.1145/3447772</a>}, number={4}, journal={{ACM} Comput. Surv.}, author={Hogan, Aidan and Blomqvist, Eva and Cochez, Michael and d’Amato, Claudia and de Melo, Gerard and Guti{\\’{e}}rrez, Claudio and Kirrane, Sabrina and Emilio Labra Gayo, Jos{\\’{e}} and Navigli, Roberto and Neumaier, Sebastian and et al.}, year={2021}, pages={71:1-71:37} }"},"publication":"{ACM} Comput. Surv.","issue":"4","date_created":"2021-09-20T13:02:51Z","department":[{"_id":"574"}],"type":"journal_article"},{"citation":{"mla":"Firmansyah, Asep Fajar, et al. “GATES: Using Graph Attention Networks for Entity Summarization.” <i>Proceedings of the 11th on Knowledge Capture Conference</i>, ACM, 2021, pp. 73–80, doi:<a href=\"https://doi.org/10.1145/3460210.3493574\">10.1145/3460210.3493574</a>.","bibtex":"@inproceedings{Firmansyah_Moussallem_Ngonga Ngomo_2021, place={Virtual Event, USA}, title={GATES: Using Graph Attention Networks for Entity Summarization}, DOI={<a href=\"https://doi.org/10.1145/3460210.3493574\">10.1145/3460210.3493574</a>}, booktitle={Proceedings of the 11th on Knowledge Capture Conference}, publisher={ACM}, author={Firmansyah, Asep Fajar and Moussallem, Diego and Ngonga Ngomo, Axel-Cyrille}, year={2021}, pages={73–80} }","ama":"Firmansyah AF, Moussallem D, Ngonga Ngomo A-C. GATES: Using Graph Attention Networks for Entity Summarization. In: <i>Proceedings of the 11th on Knowledge Capture Conference</i>. ACM; 2021:73–80. doi:<a href=\"https://doi.org/10.1145/3460210.3493574\">10.1145/3460210.3493574</a>","ieee":"A. F. Firmansyah, D. Moussallem, and A.-C. Ngonga Ngomo, “GATES: Using Graph Attention Networks for Entity Summarization,” in <i>Proceedings of the 11th on Knowledge Capture Conference</i>, 2021, pp. 73–80, doi: <a href=\"https://doi.org/10.1145/3460210.3493574\">10.1145/3460210.3493574</a>.","apa":"Firmansyah, A. F., Moussallem, D., &#38; Ngonga Ngomo, A.-C. (2021). GATES: Using Graph Attention Networks for Entity Summarization. <i>Proceedings of the 11th on Knowledge Capture Conference</i>, 73–80. <a href=\"https://doi.org/10.1145/3460210.3493574\">https://doi.org/10.1145/3460210.3493574</a>","chicago":"Firmansyah, Asep Fajar, Diego Moussallem, and Axel-Cyrille Ngonga Ngomo. “GATES: Using Graph Attention Networks for Entity Summarization.” In <i>Proceedings of the 11th on Knowledge Capture Conference</i>, 73–80. Virtual Event, USA: ACM, 2021. <a href=\"https://doi.org/10.1145/3460210.3493574\">https://doi.org/10.1145/3460210.3493574</a>.","short":"A.F. Firmansyah, D. Moussallem, A.-C. Ngonga Ngomo, in: Proceedings of the 11th on Knowledge Capture Conference, ACM, Virtual Event, USA, 2021, pp. 73–80."},"publication":"Proceedings of the 11th on Knowledge Capture Conference","department":[{"_id":"574"}],"type":"conference","place":"Virtual Event, USA","date_created":"2022-01-20T08:37:12Z","date_updated":"2022-01-20T09:03:38Z","publication_status":"published","publication_identifier":{"isbn":["978-1-4503-8457-5"]},"author":[{"id":"76787","first_name":"Asep Fajar","last_name":"Firmansyah","full_name":"Firmansyah, Asep Fajar"},{"last_name":"Moussallem","first_name":"Diego","full_name":"Moussallem, Diego","id":"71635"},{"id":"65716","last_name":"Ngonga Ngomo","first_name":"Axel-Cyrille","full_name":"Ngonga Ngomo, Axel-Cyrille"}],"title":"GATES: Using Graph Attention Networks for Entity Summarization","year":"2021","status":"public","doi":"10.1145/3460210.3493574","user_id":"76787","_id":"29486","publisher":"ACM","language":[{"iso":"eng"}],"page":"73–80"},{"main_file_link":[{"url":"https://papers.dice-research.org/2021/IDEAL2021_DriftDetectionEmbeddings/Drift-Detection-in-Text-Data-with-Document-Embeddings-public.pdf","open_access":"1"}],"language":[{"iso":"eng"}],"doi":"10.1007/978-3-030-91608-4_11","year":"2021","title":"Drift Detection in Text Data with Document Embeddings","publication_identifier":{"issn":["0302-9743","1611-3349"],"isbn":["9783030916077","9783030916084"]},"author":[{"full_name":"Feldhans, Robert","last_name":"Feldhans","first_name":"Robert"},{"last_name":"Wilke","first_name":"Adrian","orcid":"0000-0002-6575-807X","full_name":"Wilke, Adrian","id":"9101"},{"full_name":"Heindorf, Stefan","last_name":"Heindorf","first_name":"Stefan","orcid":"0000-0002-4525-6865","id":"11871"},{"last_name":"Shaker","first_name":"Mohammad Hossein","full_name":"Shaker, Mohammad Hossein"},{"last_name":"Hammer","first_name":"Barbara","full_name":"Hammer, Barbara"},{"id":"65716","last_name":"Ngonga Ngomo","first_name":"Axel-Cyrille","full_name":"Ngonga Ngomo, Axel-Cyrille"},{"id":"48129","last_name":"Hüllermeier","first_name":"Eyke","full_name":"Hüllermeier, Eyke"}],"date_updated":"2022-10-15T19:54:20Z","publication_status":"published","date_created":"2022-01-12T10:27:23Z","type":"book_chapter","department":[{"_id":"574"}],"publication":"Intelligent Data Engineering and Automated Learning – IDEAL 2021","related_material":{"link":[{"url":"https://link.springer.com/chapter/10.1007/978-3-030-91608-4_11","relation":"confirmation"}]},"publisher":"Springer International Publishing","_id":"29292","user_id":"11871","status":"public","place":"Cham","oa":"1","citation":{"ama":"Feldhans R, Wilke A, Heindorf S, et al. Drift Detection in Text Data with Document Embeddings. In: <i>Intelligent Data Engineering and Automated Learning – IDEAL 2021</i>. Springer International Publishing; 2021. doi:<a href=\"https://doi.org/10.1007/978-3-030-91608-4_11\">10.1007/978-3-030-91608-4_11</a>","bibtex":"@inbook{Feldhans_Wilke_Heindorf_Shaker_Hammer_Ngonga Ngomo_Hüllermeier_2021, place={Cham}, title={Drift Detection in Text Data with Document Embeddings}, DOI={<a href=\"https://doi.org/10.1007/978-3-030-91608-4_11\">10.1007/978-3-030-91608-4_11</a>}, 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} }","mla":"Feldhans, Robert, et al. “Drift Detection in Text Data with Document Embeddings.” <i>Intelligent Data Engineering and Automated Learning – IDEAL 2021</i>, Springer International Publishing, 2021, doi:<a href=\"https://doi.org/10.1007/978-3-030-91608-4_11\">10.1007/978-3-030-91608-4_11</a>.","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.","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 <i>Intelligent Data Engineering and Automated Learning – IDEAL 2021</i>. Cham: Springer International Publishing, 2021. <a href=\"https://doi.org/10.1007/978-3-030-91608-4_11\">https://doi.org/10.1007/978-3-030-91608-4_11</a>.","apa":"Feldhans, R., Wilke, A., Heindorf, S., Shaker, M. H., Hammer, B., Ngonga Ngomo, A.-C., &#38; Hüllermeier, E. (2021). Drift Detection in Text Data with Document Embeddings. In <i>Intelligent Data Engineering and Automated Learning – IDEAL 2021</i>. Springer International Publishing. <a href=\"https://doi.org/10.1007/978-3-030-91608-4_11\">https://doi.org/10.1007/978-3-030-91608-4_11</a>","ieee":"R. Feldhans <i>et al.</i>, “Drift Detection in Text Data with Document Embeddings,” in <i>Intelligent Data Engineering and Automated Learning – IDEAL 2021</i>, Cham: Springer International Publishing, 2021."}},{"abstract":[{"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.","lang":"eng"}],"publication":"The 13th Asian Conference on Machine Learning, ACML 2021","citation":{"mla":"Demir, Caglar, et al. “Convolutional Hypercomplex Embeddings for Link Prediction.” <i>The 13th Asian Conference on Machine Learning, ACML 2021</i>, 2021.","ama":"Demir C, Moussallem D, Heindorf S, Ngonga Ngomo A-C. Convolutional Hypercomplex Embeddings for Link Prediction. In: <i>The 13th Asian Conference on Machine Learning, ACML 2021</i>. ; 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., &#38; Ngonga Ngomo, A.-C. (2021). Convolutional Hypercomplex Embeddings for Link Prediction. <i>The 13th Asian Conference on Machine Learning, ACML 2021</i>.","ieee":"C. Demir, D. Moussallem, S. Heindorf, and A.-C. Ngonga Ngomo, “Convolutional Hypercomplex Embeddings for Link Prediction,” 2021.","chicago":"Demir, Caglar, Diego Moussallem, Stefan Heindorf, and Axel-Cyrille Ngonga Ngomo. “Convolutional Hypercomplex Embeddings for Link Prediction.” In <i>The 13th Asian Conference on Machine Learning, ACML 2021</i>, 2021.","short":"C. Demir, D. Moussallem, S. Heindorf, A.-C. Ngonga Ngomo, in: The 13th Asian Conference on Machine Learning, ACML 2021, 2021."},"type":"conference","department":[{"_id":"574"}],"oa":"1","external_id":{"arxiv":["2106.15230"]},"date_created":"2022-01-12T10:21:10Z","date_updated":"2022-10-17T15:06:40Z","year":"2021","status":"public","title":"Convolutional Hypercomplex Embeddings for Link Prediction","author":[{"id":"43817","full_name":"Demir, Caglar","last_name":"Demir","first_name":"Caglar"},{"id":"71635","full_name":"Moussallem, Diego","last_name":"Moussallem","first_name":"Diego"},{"id":"11871","last_name":"Heindorf","first_name":"Stefan","orcid":"0000-0002-4525-6865","full_name":"Heindorf, Stefan"},{"first_name":"Axel-Cyrille","last_name":"Ngonga Ngomo","full_name":"Ngonga Ngomo, Axel-Cyrille","id":"65716"}],"user_id":"11871","main_file_link":[{"open_access":"1","url":"https://papers.dice-research.org/2021/ACML2021_HyperConv/public.pdf"}],"_id":"29287","language":[{"iso":"eng"}]}]