TY - GEN
T1 - Fact validation with knowledge graph embeddings
AU - Ammar, Ammar
AU - Çelebi, Remzi
N1 - Funding Information:
Acknowledgments 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
Funding Information:
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
Publisher Copyright:
Copyright © 2019 for this paper by its authors.
PY - 2019/1/1
Y1 - 2019/1/1
N2 - 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.
AB - 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.
KW - Fact checking
KW - Fact validation
KW - Knowledge Graph Embedding
M3 - Conference article in proceeding
VL - 2456
T3 - CEUR Workshop Proceedings
SP - 125
EP - 128
BT - ISWC 2019 Satellites
T2 - 2019 ISWC Satellite Tracks (Posters and Demonstrations, Industry, and Outrageous Ideas)
Y2 - 26 October 2019 through 30 October 2019
ER -