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.
R. Pernischova (2019) The Butterfly Effect in Knowledge Graphs: Predicting the Impact of Changes in the Evolving Web of Data.In: Doctoral Consortium at ISWC 2019, Auckland, 26 October 2019 - 30 October 2019.
R. Pernischova, D. Dell'Aglio, M. Horridge, M. Baumgartner, A. Bernstein (2019) Toward Predicting Impact of Changes in Evolving Knowledge Graphs.In: ISWC 2019 Posters & Demonstrations, Auckland, 25 October 2019 - 30 October 2019.
Won the Best Poster Award at ISWC 2019