OntoMerger: An Ontology Integration Library for Deduplicating and Connecting Knowledge Graph Nodes
Autor: | Geleta, David, Nikolov, Andriy, ODonoghue, Mark, Rozemberczki, Benedek, Gogleva, Anna, Tamma, Valentina, Payne, Terry R. |
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Rok vydání: | 2022 |
Předmět: | |
Druh dokumentu: | Working Paper |
Popis: | Duplication of nodes is a common problem encountered when building knowledge graphs (KGs) from heterogeneous datasets, where it is crucial to be able to merge nodes having the same meaning. OntoMerger is a Python ontology integration library whose functionality is to deduplicate KG nodes. Our approach takes a set of KG nodes, mappings and disconnected hierarchies and generates a set of merged nodes together with a connected hierarchy. In addition, the library provides analytic and data testing functionalities that can be used to fine-tune the inputs, further reducing duplication, and to increase connectivity of the output graph. OntoMerger can be applied to a wide variety of ontologies and KGs. In this paper we introduce OntoMerger and illustrate its functionality on a real-world biomedical KG. Comment: Code available under: https://github.com/AstraZeneca/onto_merger |
Databáze: | arXiv |
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