KGdiff: Tracking the Evolution of Knowledge Graphs
| Autor: | Krys J. Kochut, Abbas Keshavarzi |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
0303 health sciences
education.field_of_study Information retrieval Computer science business.industry RDF Schema Software tool Population 02 engineering and technology computer.file_format 03 medical and health sciences Software Knowledge graph Schema (psychology) 0202 electrical engineering electronic engineering information engineering Graph (abstract data type) 020201 artificial intelligence & image processing RDF education business computer 030304 developmental biology |
| Zdroj: | IRI |
| DOI: | 10.1109/iri49571.2020.00047 |
| Popis: | A Knowledge Graph (KG) is a machine-readable, labeled graph-like representation of human knowledge. As the main goal of KG is to represent data by enriching it with computer-processable semantics, the knowledge graph creation usually involves acquiring data from external resources and datasets. In many domains, especially in biomedicine, the data sources continuously evolve, and KG engineers and domain experts must not only track the changes in KG entities and their interconnections but introduce changes to the KG schema and the graph population software. We present a framework to track the KG evolution both in terms of the schema and individuals. KGdiff is a software tool that incrementally collects the relevant meta-data information from a KG and compares it to a prior version the KG. The KG is represented in OWL/RDF/RDFS and the meta-data is collected using domain-independent queries. We evaluate our method on different RDF/OWL data sets (ontologies). |
| Databáze: | OpenAIRE |
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