---
_id: '29009'
abstract:
- lang: eng
  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.
author:
- first_name: Kleanthi
  full_name: Georgala, Kleanthi
  last_name: Georgala
- first_name: Michael
  full_name: Röder, Michael
  last_name: Röder
- first_name: Mohamed
  full_name: Sherif, Mohamed
  id: '67234'
  last_name: Sherif
  orcid: https://orcid.org/0000-0002-9927-2203
- first_name: Axel-Cyrille
  full_name: Ngonga Ngomo, Axel-Cyrille
  id: '65716'
  last_name: Ngonga Ngomo
citation:
  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.'
  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>.'
  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} }'
  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.'
  ieee: 'K. Georgala, M. Röder, M. Sherif, and A.-C. Ngonga Ngomo, “Applying edge-counting
    semantic similarities to Link Discovery: Scalability and Accuracy,” 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.'
  short: 'K. Georgala, M. Röder, M. Sherif, A.-C. Ngonga Ngomo, in: Proceedings of
    Ontology Matching Workshop 2020, 2020.'
date_created: 2021-12-17T09:53:49Z
date_updated: 2023-08-16T09:34:31Z
keyword:
- 2020 dice simba sherif hecate ngonga knowgraphs sys:relevantFor:limboproject limboproject
  sys:relevantFor:infai sys:relevantFor:bis limes limbo opal roeder georgala
language:
- iso: eng
publication: Proceedings of Ontology Matching Workshop 2020
status: public
title: 'Applying edge-counting semantic similarities to Link Discovery: Scalability
  and Accuracy'
type: conference
user_id: '67234'
year: '2020'
...