{"abstract":[{"text":"With the growth in number and variety of RDF datasets comes an in- creasing need for both scalable and accurate solutions to support link discovery at instance level within and across these datasets. In contrast to ontology matching, most linking frameworks rely solely on string similarities to this end. The limited use of semantic similarities when linking instances is partly due to the current literature stating that they (1) do not improve the F-measure of instance linking approaches and (2) are impractical to use because they lack time efficiency. We revisit the combination of string and semantic similarities for linking instances. Contrary to the literature, our results suggest that this combination can improve the F-measure achieved by instance linking systems when the combination of the measures is performed by a machine learning approach. To achieve this in- sight, we had to address the scalability of semantic similarities. We hence present a framework for the rapid computation of semantic similarities based on edge counting. This runtime improvement allowed us to run an evaluation of 5 bench- mark datasets. Our results suggest that combining string and semantic similarities can improve the F-measure by up to 6% absolute.","lang":"eng"}],"citation":{"short":"K. Georgala, M. Röder, M. Sherif, A.-C. Ngonga Ngomo, in: Proceedings of Ontology Matching Workshop 2020, 2020.","chicago":"Georgala, Kleanthi, Michael Röder, Mohamed Sherif, and Axel-Cyrille Ngonga Ngomo. “Applying Edge-Counting Semantic Similarities to Link Discovery: Scalability and Accuracy.” In <i>Proceedings of Ontology Matching Workshop 2020</i>, 2020.","bibtex":"@inproceedings{Georgala_Röder_Sherif_Ngonga Ngomo_2020, title={Applying edge-counting semantic similarities to Link Discovery: Scalability and Accuracy}, booktitle={Proceedings of Ontology Matching Workshop 2020}, author={Georgala, Kleanthi and Röder, Michael and Sherif, Mohamed and Ngonga Ngomo, Axel-Cyrille}, year={2020} }","ama":"Georgala K, Röder M, Sherif M, Ngonga Ngomo A-C. Applying edge-counting semantic similarities to Link Discovery: Scalability and Accuracy. In: <i>Proceedings of Ontology Matching Workshop 2020</i>. ; 2020.","mla":"Georgala, Kleanthi, et al. “Applying Edge-Counting Semantic Similarities to Link Discovery: Scalability and Accuracy.” <i>Proceedings of Ontology Matching Workshop 2020</i>, 2020.","ieee":"K. Georgala, M. Röder, M. Sherif, and A.-C. Ngonga Ngomo, “Applying edge-counting semantic similarities to Link Discovery: Scalability and Accuracy,” 2020.","apa":"Georgala, K., Röder, M., Sherif, M., &#38; Ngonga Ngomo, A.-C. (2020). Applying edge-counting semantic similarities to Link Discovery: Scalability and Accuracy. <i>Proceedings of Ontology Matching Workshop 2020</i>."},"user_id":"67234","keyword":["2020 dice simba sherif hecate ngonga knowgraphs sys:relevantFor:limboproject limboproject sys:relevantFor:infai sys:relevantFor:bis limes limbo opal roeder georgala"],"publication":"Proceedings of Ontology Matching Workshop 2020","title":"Applying edge-counting semantic similarities to Link Discovery: Scalability and Accuracy","date_created":"2021-12-17T09:53:49Z","type":"conference","_id":"29009","language":[{"iso":"eng"}],"year":"2020","author":[{"first_name":"Kleanthi","full_name":"Georgala, Kleanthi","last_name":"Georgala"},{"full_name":"Röder, Michael","last_name":"Röder","first_name":"Michael"},{"orcid":"https://orcid.org/0000-0002-9927-2203","id":"67234","first_name":"Mohamed","full_name":"Sherif, Mohamed","last_name":"Sherif"},{"first_name":"Axel-Cyrille","id":"65716","full_name":"Ngonga Ngomo, Axel-Cyrille","last_name":"Ngonga Ngomo"}],"status":"public","date_updated":"2023-08-16T09:34:31Z"}