---
_id: '61202'
abstract:
- lang: eng
  text: The number of datasets on the web of data increases continuously. However,
    the knowledge contained therein cannot be fully utilized without finding links
    between the entities contained in these datasets. Equivalent entities can not
    be identified solely by checking the equivalence of IRIs because of the different
    origins and naming schemes of different data providers. Yet, such equivalences
    can be discovered by computing the similarity of their attributes. In this paper
    we propose GLIDE, an approach that links entities from two different datasets
    by embedding a joint model of these datasets enriched by additional relations
    describing the similarity of literals. The joint model is embedded into a latent
    vector space while paying attention to juxtaposing similar literals. We evaluate
    our approach against state-of-the-art algorithms using real-world datasets commonly
    used in link discovery literature. The results show that GLIDE outperforms all
    baselines on 5 of 7 datasets with perfect or near-perfect accuracy. Our approach
    achieves its best performance on datasets that feature several literals with similarities.
    Our experiments indicate that researchers should not only pay attention to equal
    literals in knowledge graph embedding but should also be aware of the distance
    between similar literals.
author:
- first_name: Alexander
  full_name: Becker, Alexander
  last_name: Becker
- first_name: Axel-Cyrille
  full_name: Ngonga Ngomo, Axel-Cyrille
  id: '65716'
  last_name: Ngonga Ngomo
- first_name: 'Mohamed '
  full_name: 'Sherif, Mohamed '
  last_name: Sherif
citation:
  ama: 'Becker A, Ngonga Ngomo A-C, Sherif M. GLIDE: Knowledge Graph Linking using
    Distance-Aware Embeddings. In: <i>The Semantic Web – ISWC 2025</i>. ; 2025.'
  apa: 'Becker, A., Ngonga Ngomo, A.-C., &#38; Sherif, M. (2025). GLIDE: Knowledge
    Graph Linking using Distance-Aware Embeddings. <i>The Semantic Web – ISWC 2025</i>.
    ISWC 2025.'
  bibtex: '@inproceedings{Becker_Ngonga Ngomo_Sherif_2025, place={Nara, Japan}, title={GLIDE:
    Knowledge Graph Linking using Distance-Aware Embeddings}, booktitle={The Semantic
    Web – ISWC 2025}, author={Becker, Alexander and Ngonga Ngomo, Axel-Cyrille and
    Sherif, Mohamed }, year={2025} }'
  chicago: 'Becker, Alexander, Axel-Cyrille Ngonga Ngomo, and Mohamed  Sherif. “GLIDE:
    Knowledge Graph Linking Using Distance-Aware Embeddings.” In <i>The Semantic Web
    – ISWC 2025</i>. Nara, Japan, 2025.'
  ieee: 'A. Becker, A.-C. Ngonga Ngomo, and M. Sherif, “GLIDE: Knowledge Graph Linking
    using Distance-Aware Embeddings,” presented at the ISWC 2025, 2025.'
  mla: 'Becker, Alexander, et al. “GLIDE: Knowledge Graph Linking Using Distance-Aware
    Embeddings.” <i>The Semantic Web – ISWC 2025</i>, 2025.'
  short: 'A. Becker, A.-C. Ngonga Ngomo, M. Sherif, in: The Semantic Web – ISWC 2025,
    Nara, Japan, 2025.'
conference:
  name: ISWC 2025
date_created: 2025-09-11T10:04:16Z
date_updated: 2025-09-11T10:34:52Z
department:
- _id: '574'
keyword:
- becker sherif enexa sailproject dice simba ngonga whale
language:
- iso: eng
place: Nara, Japan
publication: The Semantic Web – ISWC 2025
status: public
title: 'GLIDE: Knowledge Graph Linking using Distance-Aware Embeddings'
type: conference
user_id: '67234'
year: '2025'
...