Evidence map›Paper›PMID 37072859›Full record

ArticleJournal of biomedical semantics2023

Constructing a knowledge graph for open government data: the case of Nova Scotia disease datasets.

Enayat Rajabi, Rishi Midha, Jairo Francisco de Souza

Abstract read
In one paragraph

Article in Journal of biomedical semantics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Enayat RajabiShannon School of Business, Cape Breton University, Grand Lake Dr., B1M 1A2, Sydney, Canada. enayat_rajabi@cbu.ca.
Rishi MidhaShannon School of Business, Cape Breton University, Grand Lake Dr., B1M 1A2, Sydney, Canada.
Jairo Francisco de SouzaDepartment of Computer Science, Federal University of Juiz de Fora, Juiz de Fora, Brazil.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The majority of available datasets in open government data are statistical. They are widely published by various governments to be used by the public and data consumers. However, most open government data portals do not provide the five-star Linked Data standard datasets. The published datasets are isolated from one another while conceptually connected. This paper constructs a knowledge graph for the disease-related datasets of a Canadian government data portal, Nova Scotia Open Data. We leveraged the Semantic Web technologies to transform the disease-related datasets into Resource Description Framework (RDF) and enriched them with semantic rules. An RDF data model using the RDF Cube vocabulary was designed in this work to develop a graph that adheres to best practices and standards, allowing for expansion, modification and flexible re-use. The study also discusses the lessons learned during the cross-dimensional knowledge graph construction and integration of open statistical datasets from multiple sources.

Indexed as

Pattern Recognition, AutomatedSemanticsNova ScotiaDisease datasetKnowledge graphNova ScotiaOpen statistical data

Identifiers

PMID37072859
PMCPMC10111831

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.