Predicting Completeness in 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.

21 pages, 19 references, 1 figure, 5 tables. Complete version of the article accepted at WSDM'17

References in corpus (1)

• PARIS: Probabilistic Alignment of Relations, Instances, and Schema

Cited by in corpus (13)

• Machine Knowledge: Creation and Curation of Comprehensive Knowledge Bases
• What is Normal, What is Strange, and What is Missing in a Knowledge Graph: Unified Characterization via Inductive Summarization
• Negative Statements Considered Useful
• Non-Parametric Class Completeness Estimators for Collaborative Knowledge Graphs -- The Case of Wikidata
• Wikidata from a Research Perspective -- A Systematic Mapping Study of Wikidata
• Discovering Implicational Knowledge in Wikidata
• UnCommonSense: Informative Negative Knowledge about Everyday Concepts
• Degree-Aware Alignment for Entities in Tail
• Embedding Cardinality Constraints in Neural Link Predictors
• Textual Entailment for Effective Triple Validation in Object Prediction
• Knowing the No-match: Entity Alignment with Dangling Cases
• Mining Implicit Entity Preference from User-Item Interaction Data for Knowledge Graph Completion via Adversarial Learning
• REMI: Mining Intuitive Referring Expressions on Knowledge Bases