Abstract
Fact validation in a knowledge graph is a task to determine whether a given fact (subject, predicate, object) should appear in the knowledge graph. In this paper, we have described our approach for the fact validation task in the context of the Semantic Web Challenge 2019. We used embedding features with machine learning to predict facts that were missing from the knowledge graph. The embedding features were generated applying a knowledge graph method known as the RDF2Vec method on the knowledge graph with only positive statements. To improve our machine learning model, we added the test facts that we could validate via the public sources into the positive knowledge graph. We trained a Random Forest classifier on the training data (positive and negative statements) plus the verified test statements and made predictions for test data.
| Original language | English (US) |
|---|---|
| Title of host publication | 2019 ISWC Satellite Tracks (Posters and Demonstrations, Industry, and Outrageous Ideas), ISWC 2019-Satellites |
| Publisher | [email protected] |
| Pages | 125-128 |
| Number of pages | 4 |
| State | Published - Jan 1 2019 |
| Externally published | Yes |
Bibliographical note
KAUST Repository Item: Exported on 2022-06-30Acknowledged KAUST grant number(s): URF/1/3454-01-01
Acknowledgements: This work was supported by funding from King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Award No. URF/1/3454-01-01
This publication acknowledges KAUST support, but has no KAUST affiliated authors.