Not logged in.

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
  • Romana Pernisch
Presentation Type paper
Item Subtype Original Work
Refereed Yes
Status Published in final form
  • English
Event Title Doctoral Consortium at ISWC 2019
Event Type workshop
Event Location Auckland
Event Start Date October 26 - 2019
Event End Date October 30 - 2019
Place of Publication
Publisher ISWC
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.
Other Identification Number merlin-id:17843
PDF File Download from ZORA
Export BibTeX