{"related_material":{"link":[{"url":"http://arxiv.org/abs/2107.04911","relation":"confirmation"}]},"conference":{"location":"Hersonissos, Crete, Greece","end_date":"2022-06-02","name":"Extended Semantic Web Conference (ESWC)","start_date":"2022-05-29"},"publication":"ESWC","abstract":[{"lang":"eng","text":"Concept learning approaches based on refinement operators explore partially ordered solution spaces to compute concepts, which are used as binary classification models for individuals. However, the number of concepts explored by these approaches can grow to the millions for complex learning problems. This often leads to impractical runtimes. We propose to alleviate this problem by predicting the length of target concepts before the exploration of the solution space. By these means, we can prune the search space during concept learning. To achieve this goal, we compare four neural architectures and evaluate them on four benchmarks. Our evaluation results suggest that recurrent neural network architectures perform best at concept length prediction with a macro F-measure ranging from 38% to 92%. We then extend the CELOE algorithm, which learns ALC concepts, with our concept length predictor. Our extension yields the algorithm CLIP. In our experiments, CLIP is at least 7.5 times faster than other state-of-the-art concept learning algorithms for ALC---including CELOE---and achieves significant improvements in the F-measure of the concepts learned on 3 out of 4 datasets. For reproducibility, we provide our implementation in the public GitHub repository at https://github.com/dice-group/LearnALCLengths"}],"title":"Learning Concept Lengths Accelerates Concept Learning in ALC","date_updated":"2023-06-27T10:23:10Z","intvolume":"     13261","language":[{"iso":"eng"}],"page":"236 - 252","department":[{"_id":"34"}],"volume":13261,"publisher":"Springer","type":"conference","keyword":["dice knowgraphs raki daikiri kouagou heindorf demir ngonga"],"citation":{"short":"N.J. Kouagou, S. Heindorf, C. Demir, N.A.-C. Ngomo, in: ESWC, Springer, 2022, pp. 236–252.","bibtex":"@inproceedings{Kouagou_Heindorf_Demir_Ngomo_2022, title={Learning Concept Lengths Accelerates Concept Learning in ALC}, volume={13261}, booktitle={ESWC}, publisher={Springer}, author={Kouagou, N’Dah Jean and Heindorf, Stefan and Demir, Caglar and Ngomo, Ngonga Axel-Cyrille}, year={2022}, pages={236–252} }","ieee":"N. J. Kouagou, S. Heindorf, C. Demir, and N. A.-C. Ngomo, “Learning Concept Lengths Accelerates Concept Learning in ALC,” in <i>ESWC</i>, Hersonissos, Crete, Greece, 2022, vol. 13261, pp. 236–252.","ama":"Kouagou NJ, Heindorf S, Demir C, Ngomo NA-C. Learning Concept Lengths Accelerates Concept Learning in ALC. In: <i>ESWC</i>. Vol 13261. Springer; 2022:236-252.","mla":"Kouagou, N’Dah Jean, et al. “Learning Concept Lengths Accelerates Concept Learning in ALC.” <i>ESWC</i>, vol. 13261, Springer, 2022, pp. 236–52.","chicago":"Kouagou, N’Dah Jean, Stefan Heindorf, Caglar Demir, and Ngonga Axel-Cyrille Ngomo. “Learning Concept Lengths Accelerates Concept Learning in ALC.” In <i>ESWC</i>, 13261:236–52. Springer, 2022.","apa":"Kouagou, N. J., Heindorf, S., Demir, C., &#38; Ngomo, N. A.-C. (2022). Learning Concept Lengths Accelerates Concept Learning in ALC. <i>ESWC</i>, <i>13261</i>, 236–252."},"author":[{"full_name":"Kouagou, N’Dah Jean","last_name":"Kouagou","first_name":"N’Dah Jean"},{"first_name":"Stefan","last_name":"Heindorf","full_name":"Heindorf, Stefan"},{"last_name":"Demir","full_name":"Demir, Caglar","first_name":"Caglar"},{"last_name":"Ngomo","full_name":"Ngomo, Ngonga Axel-Cyrille","first_name":"Ngonga Axel-Cyrille"}],"year":"2022","main_file_link":[{"url":"http://arxiv.org/abs/2107.04911"}],"user_id":"87189","date_created":"2022-05-17T09:55:25Z","_id":"31257","status":"public"}