ReviewYearbook of medical informatics2016
Knowledge Representation and Management: a Linked Data Perspective.
Review in Yearbook of medical informatics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 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
9 citing papers in PubMed.
- SeEn: Sequential enriched datasets for sequence-aware recommendations.Scientific data · 2022Article
- Text Mining for Building Biomedical Networks Using Cancer as a Case Study.Biomolecules · 2021Review
- Hybrid semantic recommender system for chemical compounds in large-scale datasets.Journal of cheminformatics · 2021Article
- Ontology-based Precision Vaccinology for Deep Mechanism Understanding and Precision Vaccine Development.Current pharmaceutical design · 2021Review
- Assessing Public Metabolomics Metadata, Towards Improving Quality.Journal of integrative bioinformatics · 2017Article
- Knowledge-based biomedical Data Science.EPJ data science · 2017Article
- Impact of translation on named-entity recognition in radiology texts.Database : the journal of biological databases and curation · 2017Article
- Identifying Human Phenotype Terms by Combining Machine Learning and Validation Rules.BioMed research international · 2017Article
- A rule-based named-entity recognition method for knowledge extraction of evidence-based dietary recommendations.PloS one · 2017Article
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
introductionBiomedical research is increasingly becoming a data-intensive science in several areas, where prodigious amounts of data is being generated that has to be stored, integrated, shared and analyzed. In an effort to improve the accessibility of data and knowledge, the Linked Data initiative proposed a well-defined set of recommendations for exposing, sharing and integrating data, information and knowledge, using semantic web technologies.
objectiveThe main goal of this paper is to identify the current status and future trends of knowledge representation and management in Life and Health Sciences, mostly with regard to linked data technologies.
methodsWe selected three prominent linked data studies, namely Bio2RDF, Open PHACTS and EBI RDF platform, and selected 14 studies published after 2014 (inclusive) that cited any of the three studies. We manually analyzed these 14 papers in relation to how they use linked data techniques.
resultsThe analyses show a tendency to use linked data techniques in Life and Health Sciences, and even if some studies do not follow all of the recommendations, many of them already represent and manage their knowledge using RDF and biomedical ontologies.
conclusionThese insights from RDF and biomedical ontologies are having a strong impact on how knowledge is generated from biomedical data, by making data elements increasingly connected and by providing a better description of their semantics. As health institutes become more data centric, we believe that the adoption of linked data techniques will continue to grow and be an effective solution to knowledge representation and management.
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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.