Management of Dynamic Biomedical Terminologies: Current Status and Future Challenges

Silveira, Marcos Da; Reis, Júlio Cesar dos; Pruski, Cédric

doi:10.15265/iy-2015-002

Cited by 10 publications

(5 citation statements)

References 46 publications

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“…It is sensitive to the level of branching and assumes a consistent correlation between branch length and semantic distance. Thus even semantically similar concepts such as the posturing example seen in Table 5 may not score well, a consideration given the semantic duplication in SNOMED CT [ 21 , 22 ]. We thus asked our annotators to consider the sets of concepts in each disagreement, and judge whether they were semantically equivalent, using their knowledge as medical professionals, rather than the exact structure of the ontology.…”

Section: Discussionmentioning

confidence: 99%

CUILESS2016: a clinical corpus applying compositional normalization of text mentions

et al. 2018

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BackgroundTraditionally text mention normalization corpora have normalized concepts to single ontology identifiers (“pre-coordinated concepts”). Less frequently, normalization corpora have used concepts with multiple identifiers (“post-coordinated concepts”) but the additional identifiers have been restricted to a defined set of relationships to the core concept. This approach limits the ability of the normalization process to express semantic meaning. We generated a freely available corpus using post-coordinated concepts without a defined set of relationships that we term “compositional concepts” to evaluate their use in clinical text.MethodsWe annotated 5397 disorder mentions from the ShARe corpus to SNOMED CT that were previously normalized as “CUI-less” in the “SemEval-2015 Task 14” shared task because they lacked a pre-coordinated mapping. Unlike the previous normalization method, we do not restrict concept mappings to a particular set of the Unified Medical Language System (UMLS) semantic types and allow normalization to occur to multiple UMLS Concept Unique Identifiers (CUIs). We computed annotator agreement and assessed semantic coverage with this method.ResultsWe generated the largest clinical text normalization corpus to date with mappings to multiple identifiers and made it freely available. All but 8 of the 5397 disorder mentions were normalized using this methodology. Annotator agreement ranged from 52.4% using the strictest metric (exact matching) to 78.2% using a hierarchical agreement that measures the overlap of shared ancestral nodes.ConclusionOur results provide evidence that compositional concepts can increase semantic coverage in clinical text. To our knowledge we provide the first freely available corpus of compositional concept annotation in clinical text.Electronic supplementary materialThe online version of this article (doi:10.1186/s13326-017-0173-6) contains supplementary material, which is available to authorized users.

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Section: Discussionmentioning

confidence: 99%

CUILESS2016: a clinical corpus applying compositional normalization of text mentions

et al. 2018

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“…Ontology evolution is therefore a research field that has gained more and more interest over the past years through a joint effort of the biomedical and Semantic Web communities. Since this subject has been recently surveyed [22] , [24] , [25] , we focus on pointing out some interesting novel investigations, that are or will be particularly important to improve the adaptation process for ontology-based mappings and applications. This covers ontology change detection, the visualization of ontology evolution and ontology change prediction and tracking.…”

Section: Ontology Evolutionmentioning

confidence: 99%

“…In this review, we will first introduce the problem of ontology and mapping evolution ( Section 2 ) and then give an overview of recently proposed evolution methods for the biomedical domain and discuss open challenges: Methods for ontology evolution have been surveyed in several contexts before (e.g. [22] , [23] , [24] , [25] ). Here we will focus on recent approaches that we see relevant for semi-automatic adaptation of ontology-based mappings and applications in the life sciences.…”

Section: Introductionmentioning

confidence: 99%

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Evolution of biomedical ontologies and mappings: Overview of recent approaches

Groß

Pruski

Rahm

2016

Computational and Structural Biotechnology Journal

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Biomedical ontologies are heavily used to annotate data, and different ontologies are often interlinked by ontology mappings. These ontology-based mappings and annotations are used in many applications and analysis tasks. Since biomedical ontologies are continuously updated dependent artifacts can become outdated and need to undergo evolution as well. Hence there is a need for largely automated approaches to keep ontology-based mappings up-to-date in the presence of evolving ontologies. In this article, we survey current approaches and novel directions in the context of ontology and mapping evolution. We will discuss requirements for mapping adaptation and provide a comprehensive overview on existing approaches. We will further identify open challenges and outline ideas for future developments.

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“…Implementing a fully manual documentation process is not realistic, since the changes need to be understandable by humans and computers. Therefore, there is a need to efficiently and automatically document the changes in the ontologies as well as to determine the impact of these changes in dependent resources such as semantic mappings and annotations [ 6 ]. This chain of maintenance process is necessary to ensure interoperability between systems and understandability of documents.…”

Section: Introductionmentioning

confidence: 99%

Analysis and implementation of the DynDiff tool when comparing versions of ontology

Diaz Benavides,

Cardoso,

Da Silveira

et al. 2023

J Biomed Semant

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Background Ontologies play a key role in the management of medical knowledge because they have the properties to support a wide range of knowledge-intensive tasks. The dynamic nature of knowledge requires frequent changes to the ontologies to keep them up-to-date. The challenge is to understand and manage these changes and their impact on depending systems well in order to handle the growing volume of data annotated with ontologies and the limited documentation describing the changes. Methods We present a method to detect and characterize the changes occurring between different versions of an ontology together with an ontology of changes entitled DynDiffOnto, designed according to Semantic Web best practices and FAIR principles. We further describe the implementation of the method and the evaluation of the tool with different ontologies from the biomedical domain (i.e. ICD9-CM, MeSH, NCIt, SNOMEDCT, GO, IOBC and CIDO), showing its performance in terms of time execution and capacity to classify ontological changes, compared with other state-of-the-art approaches. Results The experiments show a top-level performance of DynDiff for large ontologies and a good performance for smaller ones, with respect to execution time and capability to identify complex changes. In this paper, we further highlight the impact of ontology matchers on the diff computation and the possibility to parameterize the matcher in DynDiff, enabling the possibility of benefits from state-of-the-art matchers. Conclusion DynDiff is an efficient tool to compute differences between ontology versions and classify these differences according to DynDiffOnto concepts. This work also contributes to a better understanding of ontological changes through DynDiffOnto, which was designed to express the semantics of the changes between versions of an ontology and can be used to document the evolution of an ontology.

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Management of Dynamic Biomedical Terminologies: Current Status and Future Challenges

Cited by 10 publications

References 46 publications

CUILESS2016: a clinical corpus applying compositional normalization of text mentions

CUILESS2016: a clinical corpus applying compositional normalization of text mentions

Evolution of biomedical ontologies and mappings: Overview of recent approaches

Analysis and implementation of the DynDiff tool when comparing versions of ontology

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