{"_id":"29007","title":"LIGER – Link Discovery with Partial Recall","keyword":["2020 dice simba sherif hecate ngonga knowgraphs sys:relevantFor:limboproject limboproject sys:relevantFor:infai sys:relevantFor:bis limes limbo opal georgala"],"user_id":"67234","citation":{"bibtex":"@inproceedings{Georgala_Sherif_Ngonga Ngomo_2020, title={LIGER – Link Discovery with Partial Recall}, booktitle={Proceedings of Ontology Matching Workshop 2020}, author={Georgala, Kleanthi and Sherif, Mohamed and Ngonga Ngomo, Axel-Cyrille}, year={2020} }","chicago":"Georgala, Kleanthi, Mohamed Sherif, and Axel-Cyrille Ngonga Ngomo. “LIGER – Link Discovery with Partial Recall.” In <i>Proceedings of Ontology Matching Workshop 2020</i>, 2020.","short":"K. Georgala, M. Sherif, A.-C. Ngonga Ngomo, in: Proceedings of Ontology Matching Workshop 2020, 2020.","ieee":"K. Georgala, M. Sherif, and A.-C. Ngonga Ngomo, “LIGER – Link Discovery with Partial Recall,” 2020.","ama":"Georgala K, Sherif M, Ngonga Ngomo A-C. LIGER – Link Discovery with Partial Recall. In: <i>Proceedings of Ontology Matching Workshop 2020</i>. ; 2020.","mla":"Georgala, Kleanthi, et al. “LIGER – Link Discovery with Partial Recall.” <i>Proceedings of Ontology Matching Workshop 2020</i>, 2020.","apa":"Georgala, K., Sherif, M., &#38; Ngonga Ngomo, A.-C. (2020). LIGER – Link Discovery with Partial Recall. <i>Proceedings of Ontology Matching Workshop 2020</i>."},"date_created":"2021-12-17T09:53:07Z","status":"public","year":"2020","author":[{"first_name":"Kleanthi","full_name":"Georgala, Kleanthi","last_name":"Georgala"},{"id":"67234","last_name":"Sherif","full_name":"Sherif, Mohamed","orcid":"https://orcid.org/0000-0002-9927-2203","first_name":"Mohamed"},{"last_name":"Ngonga Ngomo","id":"65716","full_name":"Ngonga Ngomo, Axel-Cyrille","first_name":"Axel-Cyrille"}],"abstract":[{"text":"Modern data-driven frameworks often have to process large amounts of data periodically. Hence, they often operate under time or space constraints. This also holds for Linked Data-driven frameworks when processing RDF data, in particular, when they perform link discovery tasks. In this work, we present a novel approach for link discovery under constraints pertaining to the expected recall of a link discovery task. Given a link specification, the approach aims to find a subsumed link specification that achieves a lower run time than the input specification while abiding by a predefined constraint on the expected recall it has to achieve. Our approach, dubbed LIGER, combines downward refinement oper- ators with monotonicity assumptions to detect such specifications. We evaluate our approach on seven datasets. Our results suggest that the different implemen- tations of LIGER can detect subsumed specifications that abide by expected recall constraints efficiently, thus leading to significantly shorter overall run times than our baseline.","lang":"eng"}],"date_updated":"2023-08-16T10:27:11Z","type":"conference","publication":"Proceedings of Ontology Matching Workshop 2020","language":[{"iso":"eng"}]}