Modeling Multi-typed Structurally Viewed Chemicals with the UMLS Refined Semantic Network

Chen, Ling; Morrey, C. Paul; Gu, Huanying; Halper, Michael; Perl, Yehoshua

doi:10.1197/jamia.m2604

Cited by 11 publications

(14 citation statements)

References 14 publications

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“…Various other projects have extended ontologies and terminologies to meet different needs or for other applications. In the biomedical domain, for example, an expansion of terms relevant to muscle biology was incorporated into the Gene Ontology (GO Ontology; Feltrin et al., ); Chen, Morrey, Gu, Halper, and Perl () developed a refined UMLS semantic network to distinguish between chemical complex types and conjugates in the UMLS.…”

Section: Literature Reviewmentioning

confidence: 99%

Extending SemRep to the public health domain

Rosemblat

Resnick

Auston

et al. 2013

J Am Soc Inf Sci Tec

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We describe the use of a domain-independent methodology to extend a natural language processing (NLP) application, SemRep (Rindflesch, Fiszman, & Libbus, 2005), based on the knowledge sources afforded by the Unified Medical Language System (UMLS®) (Humphreys, Lindberg, Schoolman, & Barnett, 1998) to support the area of health promotion within the public health domain. Public health professionals require good information about successful health promotion policies and programs that might be considered for application within their own communities. Our effort seeks to improve access to relevant information for the public health profession, to help those in the field remain an information-savvy workforce. NLP and semantic techniques hold promise to help public health professionals navigate the growing ocean of information by organizing and structuring this knowledge into a focused public health framework paired with a user-friendly visualization application as a way to summarize results of PubMed searches in this field of knowledge.

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Section: Literature Reviewmentioning

confidence: 99%

Extending SemRep to the public health domain

Rosemblat

Resnick

Auston

et al. 2013

J Am Soc Inf Sci Tec

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show abstract

“…We are not suggesting this change for the current SN framework unless a driving need can be established. However, for the alternative network of the “Refined Semantic Network” [20, 21], this problem can be dealt with by adding special types called intersection types , similar to the treatment in [17]. …”

Section: Discussionmentioning

confidence: 99%

“…We previously performed research into the modeling of conjugate and complex types in the framework of the Refined Semantic Network (RSN) [17]. In that research, concepts with a redundant ST assignment were identified as erroneous but the resolution of those errors was not discussed.…”

Section: Introductionmentioning

confidence: 99%

Resolution of redundant semantic type assignments for organic chemicals in the UMLS

Morrey

Chen

Halper

et al. 2011

Artificial Intelligence in Medicine

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Objective The Unified Medical Language System (UMLS) integrates terms from different sources into concepts and supplements these with the assignment of one or more high-level semantic types (STs) from its Semantic Network (SN). For a composite organic chemical concept, multiple assignments of organic chemical STs often serve to enumerate the types of the composite’s underlying chemical constituents. This practice sometimes leads to the introduction of a forbidden redundant ST assignment, where both an ST and one of its descendants are assigned to the same concept. A methodology for resolving redundant ST assignments for organic chemicals, better capturing the essence of such composite chemicals than the typical omission of the more general ST, is presented. Methods and Material The typical SN resolution of a redundant ST assignment is to retain only the more specific ST assignment and omit the more general one. However, with organic chemicals, that is not always the correct strategy. A methodology for properly dealing with the redundancy based on the relative sizes of the chemical components is presented. It is more accurate to use the ST of the larger chemical component for capturing the category of the concept, even if that means using the more general ST. Results A sample of 254 chemical concepts having redundant ST assignments in older UMLS releases was audited to analyze the accuracy of current ST assignments. For 81 (32%) of them, our chemical analysis-based approach yielded a different recommendation from the UMLS (2009AA). New UMLS usage notes capturing rules of this methodology are proffered. Conclusions Redundant ST assignments have typically arisen for organic composite chemical concepts. A methodology for dealing with this kind of erroneous configuration, capturing the proper category for a composite chemical, is presented and demonstrated.

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“…Ambiguity, incorrect classification, and inconsistencies were readily identified in the pure intersections that were examined. New conjugate and complex semantic types are suggested in [104] to better capture the semantics of concepts assigned multiple structurally viewed chemical semantic types. For such concepts the proper semantics expresses a chemical reaction or mixture rather than the typical conjunctive semantics of multiple semantic types.…”

Section: Auditing Methodsmentioning

confidence: 99%

A review of auditing methods applied to the content of controlled biomedical terminologies

Zhu

Fan

Baorto

et al. 2009

Journal of Biomedical Informatics

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Although controlled biomedical terminologies have been with us for centuries, it is only in the last couple of decades that close attention has been paid to the quality of these terminologies. The result of this attention has been the development of auditing methods that apply formal methods to assessing whether terminologies are complete and accurate. We have performed an extensive literature review to identify published descriptions of these methods and have created a framework for characterizing them. The framework considers manual, systematic and heuristic methods that use knowledge (within or external to the terminology) to measure quality factors of different aspects of the terminology content (terms, semantic classification, and semantic relationships). The quality factors examined included concept orientation, consistency, non-redundancy, soundness and comprehensive coverage. We reviewed 130 studies that were retrieved based on keyword search on publications in PubMed, and present our assessment of how they fit into our framework. We also identify which terminologies have been audited with the methods and provide examples to illustrate each part of the framework.

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Modeling Multi-typed Structurally Viewed Chemicals with the UMLS Refined Semantic Network

Cited by 11 publications

References 14 publications

Extending SemRep to the public health domain

Extending SemRep to the public health domain

Resolution of redundant semantic type assignments for organic chemicals in the UMLS

A review of auditing methods applied to the content of controlled biomedical terminologies

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