ArticleAMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science2013
Dissecting the Ambiguity of FMA Concept Names Using Taxonomy and Partonomy Structural Information.
Article in AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 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
4 citing papers in PubMed.
- Automatic Structuring of Ontology Terms Based on Lexical Granularity and Machine Learning: Algorithm Development and Validation.JMIR medical informatics · 2020Article
- Assessing the practice of biomedical ontology evaluation: Gaps and opportunities.Journal of biomedical informatics · 2018Review
- MaPLE: A MapReduce Pipeline for Lattice-based Evaluation and Its Application to SNOMED CT.Proceedings : ... IEEE International Conference on Big Data. IEEE International Conference on Big Data · 2014Article
- An analysis of FMA using structural self-bisimilarity.Journal of biomedical informatics · 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
The complex inner structures of concept names in the Foundational Model of Anatomy (FMA) remain an obstacle for further analyzing the ontology using lexical methods. A very common problem is the ambiguity lying in names with the sometimes multiple occurrences of the preposition "of." In this paper, we propose an automatic method to help disambiguating FMA terms by leveraging the taxonomy and partonomy information. If a sub-phrase of a concept name also appears in its parents, it is likely to occur as a sub-tree in its parse tree, hence should be parsed as such. We classified all the concept names with a single occurrence of the preposition "of" by the appearances of their sub-phrases in the parent names using three test suites. Results show that more than 90% of them can be provided with useful information to assist their correct parsing.
Identifiers
24303256PMC3845762What 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.