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Contribution Details

Type Conference or Workshop Paper
Scope Discipline-based scholarship
Published in Proceedings Yes
Title The Butterfly Effect in Knowledge Graphs: Predicting the Impact of Changes in the Evolving Web of Data
Organization Unit
Authors
  • Romana Pernischova
Presentation Type paper
Item Subtype Original Work
Refereed Yes
Status Published electronically before print/final form (Epub ahead of print)
Language
  • English
Event Title Doctoral Consortium at ISWC 2019
Event Type workshop
Event Location Auckland, NZ
Event Start Date October 26 - 2019
Event End Date October 30 - 2019
Place of Publication CEUR-WS.org
Abstract Text Knowledge graphs (KGs) are at the core of numerous applications and their importance is increasing. Yet, knowledge evolves and so do KGs. PubMed, a search engine that primarily provides access to medical publications, adds an estimated 500'000 new records per year - each having the potential to require updates to a medical KG, like the National Cancer Institute Thesaurus. Depending on the applications that use such a medical KG, some of these updates have possibly wide-ranging impact, while others have only local effects. Estimating the impact of a change ex-ante is highly important, as it might make KG-engineers aware of the consequences of their actions during editing or may be used to highlight the importance of a new fragment of knowledge to be added to the KG for some application. This research description proposes a unified methodology for predicting the impact of changes in evolving KGs and introduces an evaluation framework to assess the quality of these predictions.
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