Fallatah, O., Zhang, Z. and Hopfgartner, F. orcid.org/0000-0003-0380-6088 (2022) A hybrid approach for large knowledge graphs matching. In: Proceedings of the 16th International Workshop on Ontology Matching (OM 2021). The Sixteenth International Workshop on Ontology Matching (OM-2021), 25 Oct 2021, Virtual Conference. CEUR Workshop Proceedings . CEUR-WS
Abstract
Matching large and heterogeneous Knowledge Graphs (KGs) has been a challenge in the Semantic Web research community. This work highlights a number of limitations with current matching methods, such as: (1) they are highly dependent on string-based similarity measures, and (2) they are primarily built to handle well-formed ontologies. These features make them unsuitable for large, (semi-) automatically constructed KGs with hundreds of classes and millions of instances. Such KGs share a remarkable number of complementary facts, often described using different vocabulary. Inspired by the role of instances in large-scale KGs, we propose a hybrid matching approach. Our method composes an instance-based matcher that casts the schema matching process as a two-way text classification task by exploiting instances of KG classes, and a string-based matcher. Our method is domain-independent and is able to handle KG classes with unbalanced population. Our evaluation on a real-world KG dataset shows that our method obtains the highest recall and F1 over all OAEI 2020 participants.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2021 The Authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0) https://creativecommons.org/licenses/by/4.0/. |
Keywords: | Knowledge Graphs; Machine Learning; Schema Matching |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Social Sciences (Sheffield) > Information School (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 30 Nov 2021 11:49 |
Last Modified: | 05 Jan 2022 07:21 |
Published Version: | http://ceur-ws.org/Vol-3063/ |
Status: | Published |
Publisher: | CEUR-WS |
Series Name: | CEUR Workshop Proceedings |
Refereed: | Yes |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:180979 |