2020/05/04 by Elwin Huaman, Huaman, Elwin, Elias Kärle +3
Computer Science · Decision Sciences · Engineering · #Advanced Graph Neural Networks #Asset (computer security) #Computer science #Computer security #Data Quality and Management #Data science #Databases (cs.DB) #Engineering #FOS: Computer and information sciences #Graph #Information Retrieval (cs.IR) #Information retrieval #Knowledge graph #Knowledge management #Task (project management) #Theoretical computer science #Topic Modeling #cs.DB #cs.IR
paper · pdf · doi:10.48550/arxiv.2005.01389
arxiv created 2020/05/04 · openalex publication_date 2020/05/04 · arxiv updated 2020/05/05 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
Knowledge graphs (KGs) have shown to be an important asset of large companies like Google and Microsoft. KGs play an important role in providing structured and semantically rich information, making them available to people and machines, and supplying accurate, correct and reliable knowledge. To do so a critical task is knowledge validation, which measures whether statements from KGs are semantically correct and correspond to the so-called "real" world. In this paper, we provide an overview and review of the state-of-the-art approaches, methods and tools on knowledge validation for KGs, as well as an evaluation of them. As a result, we demonstrate a lack of reproducibility of tools results, give insights, and state our future research direction.