ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2009
Creating mappings for ontologies in biomedicine: simple methods work.
Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 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
35 citing papers in PubMed.
- Assembly and reasoning over semantic mappings at scale for biomedical data integration.Bioinformatics (Oxford, England) · 2025Article
- Prediction and curation of missing biomedical identifier mappings with Biomappings.Bioinformatics (Oxford, England) · 2023Article
- GWAS Central: an expanding resource for finding and visualising genotype and phenotype data from genome-wide association studies.Nucleic acids research · 2023Article
- Performance assessment of ontology matching systems for FAIR data.Journal of biomedical semantics · 2022Article
- A Simple Standard for Sharing Ontological Mappings (SSSOM).Database : the journal of biological databases and curation · 2022Article
- Auto-CORPus: A Natural Language Processing Tool for Standardizing and Reusing Biomedical Literature.Frontiers in digital health · 2022Article
- Towards the semantic enrichment of Computer Interpretable Guidelines: a method for the identification of relevant ontological terms.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2018Article
- Matching disease and phenotype ontologies in the ontology alignment evaluation initiative.Journal of biomedical semantics · 2017Article
- A Systematic Analysis of Term Reuse and Term Overlap across Biomedical Ontologies.Semantic web · 2017Article
- Article
- A numerical similarity approach for using retired Current Procedural Terminology (CPT) codes for electronic phenotyping in the Scalable Collaborative Infrastructure for a Learning Health System (SCILHS).BMC medical informatics and decision making · 2015Article
- Big Data: the challenge for small research groups in the era of cancer genomics.British journal of cancer · 2015Review
- The role of ontologies in biological and biomedical research: a functional perspective.Briefings in bioinformatics · 2015Article
- The Protégé Project: A Look Back and a Look Forward.AI matters · 2015Article
- Organ system heterogeneity DB: a database for the visualization of phenotypes at the organ system level.Nucleic acids research · 2015Article
- Concept analysis of the patient reported outcomes measurement information system (PROMIS(®)) and the international classification of functioning, disability and health (ICF).Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation · 2014Article
- Molecularly and clinically related drugs and diseases are enriched in phenotypically similar drug-disease pairs.Genome medicine · 2014Article
- Normalization and standardization of electronic health records for high-throughput phenotyping: the SHARPn consortium.Journal of the American Medical Informatics Association : JAMIA · 2013Article
- Speeding up Batch Alignment of Large Ontologies Using MapReduce.Proceedings. IEEE International Conference on Semantic Computing · 2013Article
- NCBO Technology: Powering semantically aware applications.Journal of biomedical semantics · 2013Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Creating mappings between concepts in different ontologies is a critical step in facilitating data integration. In recent years, researchers have developed many elaborate algorithms that use graph structure, background knowledge, machine learning and other techniques to generate mappings between ontologies. We compared the performance of these advanced algorithms on creating mappings for biomedical ontologies with the performance of a simple mapping algorithm that relies on lexical matching. Our evaluation has shown that (1) most of the advanced algorithms are either not publicly available or do not scale to the size of biomedical ontologies today, and (2) for many biomedical ontologies, simple lexical matching methods outperform most of the advanced algorithms in both precision and recall. Our results have practical implications for biomedical researchers who need to create alignments for their ontologies.
Indexed as
Identifiers
20351849PMC2815474What 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.