Header

UZH-Logo

Maintenance Infos

The Butterfly Effect in Knowledge Graphs: Predicting the Impact of Changes in the Evolving Web of Data


Pernischova, Romana (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.

Abstract

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.

Abstract

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.

Statistics

Downloads

55 downloads since deposited on 26 Sep 2019
55 downloads since 12 months
Detailed statistics

Additional indexing

Item Type:Conference or Workshop Item (Paper), refereed, original work
Communities & Collections:03 Faculty of Economics > Department of Informatics
Dewey Decimal Classification:000 Computer science, knowledge & systems
Language:English
Event End Date:30 October 2019
Deposited On:26 Sep 2019 10:56
Last Modified:26 Sep 2019 19:30
Publisher:s.n.
OA Status:Green
Other Identification Number:merlin-id:17843

Download

Green Open Access

Download PDF  'The Butterfly Effect in Knowledge Graphs: Predicting the Impact of Changes in the Evolving Web of Data'.
Preview
Content: Published Version
Filetype: PDF
Size: 369kB