Auditing SNOMED CT hierarchical relations based on lexical features of concepts in non-lattice subgraphs

Cui, Licong; Bodenreider, Olivier; Shi, Jay; Zhang, Guoqiang

doi:10.1016/j.jbi.2017.12.010

Cited by 23 publications

(24 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These aspects are usually modelled by calculating different hierarchical-based features, where higher values of depth and breadth variance in the class hierarchy tree are associated with better semantic relations [ 19 , 39 ]. Our solution is in line with [ 44 , 45 ], which proposed a lexical study in order to discover the possible inconsistencies. [ 44 ] focused on the semantics of sets that group lexically similar concepts, and [ 45 ] used subgraphs enriched with lexical characteristics obtained along the is-a relationships.…”

Section: Discussionmentioning

confidence: 62%

“…Our solution is in line with [ 44 , 45 ], which proposed a lexical study in order to discover the possible inconsistencies. [ 44 ] focused on the semantics of sets that group lexically similar concepts, and [ 45 ] used subgraphs enriched with lexical characteristics obtained along the is-a relationships. The approach followed in our work was to focus on the LR classes, which are classes whose label is a lexical regularity.…”

Section: Discussionmentioning

confidence: 62%

“…Then, they analysed the semantics of each similarity set in order to propose hierarchical, attribute related, or role-group related inconsistencies. In [ 45 ], the authors introduced a structural-lexical approach for auditing SNOMED CT by using a combination of non-lattice subgraphs of the underlying hierarchical relations and enriched lexical attributes of fully specified concept names. In this approach, new is-a relationships were suggested by means of the study of the lexicon along with the concept hierarchy.…”

Section: State Of the Artmentioning

confidence: 99%

See 2 more Smart Citations

Analysis of readability and structural accuracy in SNOMED CT

Abad-Navarro

Quesada-Martínez

Duque-Ramos

et al. 2020

BMC Med Inform Decis Mak

View full text Add to dashboard Cite

Background The increasing adoption of ontologies in biomedical research and the growing number of ontologies available have made it necessary to assure the quality of these resources. Most of the well-established ontologies, such as the Gene Ontology or SNOMED CT, have their own quality assurance processes. These have demonstrated their usefulness for the maintenance of the resources but are unable to detect all of the modelling flaws in the ontologies. Consequently, the development of efficient and effective quality assurance methods is needed. Methods Here, we propose a series of quantitative metrics based on the processing of the lexical regularities existing in the content of the ontology, to analyse readability and structural accuracy. The readability metrics account for the ratio of labels, descriptions, and synonyms associated with the ontology entities. The structural accuracy metrics evaluate how two ontology modelling best practices are followed: (1) lexically suggest locally define (LSLD), that is, if what is expressed in natural language for humans is available as logical axioms for machines; and (2) systematic naming, which accounts for the amount of label content of the classes in a given taxonomy shared. Results We applied the metrics to different versions of SNOMED CT. Both readability and structural accuracy metrics remained stable in time but could capture some changes in the modelling decisions in SNOMED CT. The value of the LSLD metric increased from 0.27 to 0.31, and the value of the systematic naming metric was around 0.17. We analysed the readability and structural accuracy in the SNOMED CT July 2019 release. The results showed that the fulfilment of the structural accuracy criteria varied among the SNOMED CT hierarchies. The value of the metrics for the hierarchies was in the range of 0–0.92 (LSLD) and 0.08–1 (systematic naming). We also identified the cases that did not meet the best practices. Conclusions We generated useful information about the engineering of the ontology, making the following contributions: (1) a set of readability metrics, (2) the use of lexical regularities to define structural accuracy metrics, and (3) the generation of quality assurance information for SNOMED CT.

show abstract

Section: Discussionmentioning

confidence: 62%

Section: Discussionmentioning

confidence: 62%

Section: State Of the Artmentioning

confidence: 99%

See 1 more Smart Citation

Analysis of readability and structural accuracy in SNOMED CT

Abad-Navarro

Quesada-Martínez

Duque-Ramos

et al. 2020

BMC Med Inform Decis Mak

View full text Add to dashboard Cite

show abstract

“…Liu et al created embeddings for each concept based on its related IS-A relations and used convolutional neural network to discover missing IS-A relations between neoplasm concepts in the NCI Thesaurus [13]. In our previous works [14][15][16][17][18][19][20], we found that non-lattice subgraphs often reveal quality issues including missing IS-A relations. For instance, lexical-based approaches based on non-lattice subgraphs were developed to identify missing IS-A relations in the SNOMED CT [17,18] and NCI Thesaurus [19,20].…”

Section: Related Work On Identifying Missing Is-a Relationsmentioning

confidence: 90%

Detecting missing IS-A relations in the NCI Thesaurus using an enhanced hybrid approach

Zheng

Abeysinghe

Sioutos

et al. 2020

BMC Med Inform Decis Mak

Self Cite

View full text Add to dashboard Cite

Background The National Cancer Institute (NCI) Thesaurus provides reference terminology for NCI and other systems. Previously, we proposed a hybrid prototype utilizing lexical features and role definitions of concepts in non-lattice subgraphs to identify missing IS-A relations in the NCI Thesaurus. However, no domain expert evaluation was provided in our previous work. In this paper, we further enhance the hybrid approach by leveraging a novel lexical feature—roots of noun chunks within concept names. Formal evaluation of our enhanced approach is also performed. Method We first compute all the non-lattice subgraphs in the NCI Thesaurus. We model each concept using its role definitions, words and roots of noun chunks within its concept name and its ancestor’s names. Then we perform subsumption testing for candidate concept pairs in the non-lattice subgraphs to automatically detect potentially missing IS-A relations. Domain experts evaluated the validity of these relations. Results We applied our approach to 19.08d version of the NCI Thesaurus. A total of 55 potentially missing IS-A relations were identified by our approach and reviewed by domain experts. 29 out of 55 were confirmed as valid by domain experts and have been incorporated in the newer versions of the NCI Thesaurus. 7 out of 55 further revealed incorrect existing IS-A relations in the NCI Thesaurus. Conclusions The results showed that our hybrid approach by leveraging lexical features and role definitions is effective in identifying potentially missing IS-A relations in the NCI Thesaurus.

show abstract

“…This method used 4 patterns to cover about 4% of all non-lattice fragments in SNOMED CT, with a solid precision rate (59%) of confirmed errors by domain experts. More recently, a new structural-lexical approach leveraged more existing knowledge in SNOMED CT by enriching the lexical attributes of each concept in non-lattice subgraphs to facilitate the identification of missing is-a relations [17]. This approach covered 7.4% of non-lattice subgraphs with higher precision (82.96%).…”

Section: Discussionmentioning

confidence: 99%

An efficient, large-scale, non-lattice-detection algorithm for exhaustive structural auditing of biomedical ontologies

Zhang

Xing

Cui

2018

Journal of Biomedical Informatics

Self Cite

View full text Add to dashboard Cite

One of the basic challenges in developing structural methods for systematic audition on the quality of biomedical ontologies is the computational cost usually involved in exhaustive sub-graph analysis. We introduce ANT-LCA, a new algorithm for computing all non-trivial lowest common ancestors (LCA) of each pair of concepts in the hierarchical order induced by an ontology. The computation of LCA is a fundamental step for non-lattice approach for ontology quality assurance. Distinct from existing approaches, ANT-LCA only computes LCAs for non-trivial pairs, those having at least one common ancestor. To skip all trivial pairs that may be of no practical interest, ANT-LCA employs a simple but innovative algorithmic strategy combining topological order and dynamic programming to keep track of non-trivial pairs. We provide correctness proofs and demonstrate a substantial reduction in computational time for two largest biomedical ontologies: SNOMED CT and Gene Ontology (GO). ANT-LCA achieved an average computation time of 30 and 3 sec per version for SNOMED CT and GO, respectively, about 2 orders of magnitude faster than the best known approaches. Our algorithm overcomes a fundamental computational barrier in sub-graph based structural analysis of large ontological systems. It enables the implementation of a new breed of structural auditing methods that not only identifies potential problematic areas, but also automatically suggests changes to fix the issues. Such structural auditing methods can lead to more effective tools supporting ontology quality assurance work.

show abstract

Auditing SNOMED CT hierarchical relations based on lexical features of concepts in non-lattice subgraphs

Abstract: Our results demonstrate that analyzing the lexical features of concepts in non-lattice subgraphs is an effective approach for auditing SNOMED CT.

Cited by 23 publications

References 14 publications

Analysis of readability and structural accuracy in SNOMED CT

Analysis of readability and structural accuracy in SNOMED CT

Detecting missing IS-A relations in the NCI Thesaurus using an enhanced hybrid approach

An efficient, large-scale, non-lattice-detection algorithm for exhaustive structural auditing of biomedical ontologies

Contact Info

Product

Resources

About