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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 |
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| Presentation Type | paper |
| Item Subtype | Original Work |
| Refereed | Yes |
| Status | Published in final form |
| Language |
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| 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 | CEUR-WS.org |
| 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 |
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