ArticleFrontiers in big data2021
Proving the Correctness of Knowledge Graph Update: A Scenario From Surveillance of Adverse Childhood Experiences.
Article in Frontiers in big data, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Exploring the link between adverse childhood experiences and cancer development - insights and intervention recommendations from a scoping review.Npj mental health research · 2025Article
- A solution and practice for combining multi-source heterogeneous data to construct enterprise knowledge graph.Frontiers in big data · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Knowledge graphs are a modern way to store information. However, the knowledge they contain is not static. Instances of various classes may be added or deleted and the semantic relationship between elements might evolve as well. When such changes take place, a knowledge graph might become inconsistent and the knowledge it conveys meaningless. In order to ensure the consistency and coherency of dynamic knowledge graphs, we propose a method to model the transformations that a knowledge graph goes through and to prove that the new transformations do not yield inconsistencies. To do so, we express the knowledge graphs as logically decorated graphs, then we describe the transformations as algorithmic graph transformations and we use a Hoare-like verification process to prove correctness. To demonstrate the proposed method in action, we use examples from Adverse Childhood Experiences (ACEs), which is a public health crisis.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.