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.
Rok vydání:	2022
Předmět:	Computer Science - Artificial Intelligence Computer Science - Machine Learning Computer Science - Symbolic Computation
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
Externí odkaz:	http://arxiv.org/abs/2206.02238 Zobrazit plný text záznamu View this record from Arxiv