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What is the schema of your knowledge graph?: leveraging knowledge graph embeddings and clustering for expressive taxonomy learning

Published: 14 June 2020 Publication History

Abstract

Large-scale knowledge graphs have become prevalent on the Web and have demonstrated their usefulness for several tasks. One challenge associated to knowledge graphs is the necessity to keep a knowledge graph schema (which is generally manually defined) that accurately reflects the knowledge graph content. In this paper, we present an approach that extracts an expressive taxonomy based on knowledge graph embeddings, linked data statistics and clustering. Our results show that the learned taxonomy is not only able to retain original classes but also identifies new classes, thus giving an up-to-date view of the knowledge graph.

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  • (2025)Information Circularity Assistance based on extreme dataat - Automatisierungstechnik10.1515/auto-2024-003973:1(3-21)Online publication date: 6-Jan-2025
  • (2024)Fast and Slow Thinking: A Two-Step Schema-Aware Approach for Instance Completion in Knowledge GraphsIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.330413736:3(1113-1129)Online publication date: Mar-2024
  • (2023)Schema First! Learn Versatile Knowledge Graph Embeddings by Capturing Semantics with MASCHInEProceedings of the 12th Knowledge Capture Conference 202310.1145/3587259.3627550(188-196)Online publication date: 5-Dec-2023
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  1. What is the schema of your knowledge graph?: leveraging knowledge graph embeddings and clustering for expressive taxonomy learning

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      cover image ACM Conferences
      SBD '20: Proceedings of The International Workshop on Semantic Big Data
      June 2020
      62 pages
      ISBN:9781450379748
      DOI:10.1145/3391274
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      Published: 14 June 2020

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      Author Tags

      1. expressive taxonomies
      2. hierarchical clustering
      3. knowledge graph embeddings
      4. ontology learning

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      Overall Acceptance Rate 30 of 54 submissions, 56%

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      Cited By

      View all
      • (2025)Information Circularity Assistance based on extreme dataat - Automatisierungstechnik10.1515/auto-2024-003973:1(3-21)Online publication date: 6-Jan-2025
      • (2024)Fast and Slow Thinking: A Two-Step Schema-Aware Approach for Instance Completion in Knowledge GraphsIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.330413736:3(1113-1129)Online publication date: Mar-2024
      • (2023)Schema First! Learn Versatile Knowledge Graph Embeddings by Capturing Semantics with MASCHInEProceedings of the 12th Knowledge Capture Conference 202310.1145/3587259.3627550(188-196)Online publication date: 5-Dec-2023
      • (2023)HELIOS: Hyper-Relational Schema Modeling from Knowledge GraphsProceedings of the 31st ACM International Conference on Multimedia10.1145/3581783.3612184(4053-4064)Online publication date: 26-Oct-2023
      • (2023)Link Prediction in Knowledge Graphs (and its Relation to RDF2vec)Embedding Knowledge Graphs with RDF2vec10.1007/978-3-031-30387-6_6(87-117)Online publication date: 4-Jun-2023
      • (2022)Knowledge graph embedding for data mining vs. knowledge graph embedding for link prediction – two sides of the same coin?Semantic Web10.3233/SW-21289213:3(399-422)Online publication date: 1-Jan-2022
      • (2022)Subgraph matching over graph federationProceedings of the VLDB Endowment10.14778/3494124.349412915:3(437-450)Online publication date: 4-Feb-2022
      • (2022)Applications of Machine Learning in Knowledge Management System: A Comprehensive ReviewJournal of Information & Knowledge Management10.1142/S021964922250017421:02Online publication date: 18-May-2022
      • (2022)Exploring the construction and application of spatial scene knowledge graphs considering topological relationsTransactions in GIS10.1111/tgis.1291126:3(1531-1547)Online publication date: 22-Feb-2022
      • (2022)INK: knowledge graph embeddings for node classificationData Mining and Knowledge Discovery10.1007/s10618-021-00806-z36:2(620-667)Online publication date: 4-Jan-2022
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