Predicting Completeness in Knowledge Bases

TL;DR

This paper explores methods to assess and predict the completeness of knowledge bases by combining various signals through rule mining, which can improve fact prediction and identify gaps in coverage.

Contribution

It introduces a novel approach to predict knowledge base completeness by integrating multiple signals with rule mining, enhancing gap detection and fact prediction.

Findings

Combining signals improves completeness prediction accuracy.

Completeness predictions assist in identifying missing facts.

The approach is applicable to large-scale knowledge bases.

Abstract

Knowledge bases such as Wikidata, DBpedia, or YAGO contain millions of entities and facts. In some knowledge bases, the correctness of these facts has been evaluated. However, much less is known about their completeness, i.e., the proportion of real facts that the knowledge bases cover. In this work, we investigate different signals to identify the areas where a knowledge base is complete. We show that we can combine these signals in a rule mining approach, which allows us to predict where facts may be missing. We also show that completeness predictions can help other applications such as fact prediction.