ArticleBMC medical informatics and decision making2020
KGen: a knowledge graph generator from biomedical scientific literature.
Article in BMC medical informatics and decision making, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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.
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
15 citing papers in PubMed.
- DA-BioNER: data augmentation based on few-shot learning and distant supervision for biomedical named entity recognition.Bioinformatics (Oxford, England) · 2026Article
- Polarity-Aware Knowledge Graph Reveals Diet-Microbiome-Health Mechanisms with Relevance to Muscle, Immune and Metabolic Aging.Research square · 2026Article
- medicX-KG: a knowledge graph for pharmacists' drug information needs.Journal of biomedical semantics · 2025Article
- Knowledge graphs in psychiatric research: Potential applications and future perspectives.Acta psychiatrica Scandinavica · 2025Review
- BioKGrapher: Initial evaluation of automated knowledge graph construction from biomedical literature.Computational and structural biotechnology journal · 2024Article
- A novel customizing knowledge graph evaluation method for incorporating user needs.Scientific reports · 2024Article
- Informatic challenges and advances in illuminating the druggable proteome.Drug discovery today · 2024Review
- Construction of a knowledge graph for breast cancer diagnosis based on Chinese electronic medical records: development and usability study.BMC medical informatics and decision making · 2023Article
- AIMedGraph: a comprehensive multi-relational knowledge graph for precision medicine.Database : the journal of biological databases and curation · 2023Article
- Building a knowledge graph to enable precision medicine.Scientific data · 2023Article
- Identifying and Analyzing Topic Clusters in a Nutri-, Food-, and Diet-Proteomic Corpus Using Machine Reading.Nutrients · 2023Review
- The construction of a TCM knowledge graph and application of potential knowledge discovery in diabetic kidney disease by integrating diagnosis and treatment guidelines and real-world clinical data.Frontiers in pharmacology · 2023Article
- Deep Denoising of Raw Biomedical Knowledge Graph From COVID-19 Literature, LitCovid, and Pubtator: Framework Development and Validation.Journal of medical Internet research · 2022Article
- Selected articles from the Fourth International Workshop on Semantics-Powered Data Mining and Analytics (SEPDA 2019).BMC medical informatics and decision making · 2020Article
- Natural language processing in Alzheimer's disease research: Systematic review of methods, data, and efficacy.Alzheimer's & dementia (Amsterdam, Netherlands)Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
backgroundKnowledge is often produced from data generated in scientific investigations. An ever-growing number of scientific studies in several domains result into a massive amount of data, from which obtaining new knowledge requires computational help. For example, Alzheimer's Disease, a life-threatening degenerative disease that is not yet curable. As the scientific community strives to better understand it and find a cure, great amounts of data have been generated, and new knowledge can be produced. A proper representation of such knowledge brings great benefits to researchers, to the scientific community, and consequently, to society.
methodsIn this article, we study and evaluate a semi-automatic method that generates knowledge graphs (KGs) from biomedical texts in the scientific literature. Our solution explores natural language processing techniques with the aim of extracting and representing scientific literature knowledge encoded in KGs. Our method links entities and relations represented in KGs to concepts from existing biomedical ontologies available on the Web. We demonstrate the effectiveness of our method by generating KGs from unstructured texts obtained from a set of abstracts taken from scientific papers on the Alzheimer's Disease. We involve physicians to compare our extracted triples from their manual extraction via their analysis of the abstracts. The evaluation further concerned a qualitative analysis by the physicians of the generated KGs with our software tool.
resultsThe experimental results indicate the quality of the generated KGs. The proposed method extracts a great amount of triples, showing the effectiveness of our rule-based method employed in the identification of relations in texts. In addition, ontology links are successfully obtained, which demonstrates the effectiveness of the ontology linking method proposed in this investigation.
conclusionsWe demonstrate that our proposal is effective on building ontology-linked KGs representing the knowledge obtained from biomedical scientific texts. Such representation can add value to the research in various domains, enabling researchers to compare the occurrence of concepts from different studies. The KGs generated may pave the way to potential proposal of new theories based on data analysis to advance the state of the art in their research domains.
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What OpenQuestion holds
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.