Knowledge graph (KG) link prediction is a fundamental task in artificial intelligence, with applications in natural language processing, information retrieval, and biomedicine.
We present CoDEx, a set of knowledge graph completion datasets extracted from Wikidata and Wikipedia that improve upon existing knowledge graph completion benchmarks in scope and level of difficulty.
Ranked #2 on Link Prediction on CoDEx Large
The KG-BIAS 2020 workshop touches on biases and how they surface in knowledge graphs (KGs), biases in the source data that is used to create KGs, methods for measuring or remediating bias in KGs, but also identifying other biases such as how and which languages are represented in automatically constructed KGs or how personal KGs might incur inherent biases.
We first conduct an evaluation under the standard closed-world assumption (CWA), in which predicted triples not already in the knowledge graph are considered false, and show that existing calibration techniques are effective for KGE under this common but narrow assumption.
While advances in computing resources have made processing enormous amounts of data possible, human ability to identify patterns in such data has not scaled accordingly.