This paper addresses machine learning models that embed knowledge graph entities and relationships toward the goal of predicting unseen triples, ...

Apr 2, 2020 · This paper addresses machine learning models that embed knowledge graph entities and relationships toward the goal of predicting unseen ...

Apr 2, 2020 · In crowdsourcing experiments, it is demonstrated that calibrated confidence scores can make knowledge graph embeddings more useful to ...

Apr 2, 2020 · In this paper we take initial steps toward this direction by investigating the calibration of KGE models, or the extent to which they output confidence scores.

We show that presenting calibrated confidence scores alongside predictions significantly improves human accuracy and efficiency in the task, motivating the.

Improving the Utility of Knowledge Graph Embeddings with Calibration. T. Safavi, D. Koutra, and E. Meij. CoRR, (2020 ). Links and resources. BibTeX key ...

This repository contains two public knowledge graph datasets used in our paper Improving the Utility of Knowledge Graph Embeddings with Calibration.

In this paper we take initial steps toward this direction by investigating the calibration of KGE models, or the extent to which they output confidence scores.

"Improving the Utility of Knowledge Graph Embeddings with Calibration", a method to calibrate embedding models to output reliable confidence estimates for ...

Calibration has several benefits. From the systems perspective, natural language processing pipelines that include knowledge graphs can rely on calibrated ...