A semi-automatic semantic method for mapping SNOMED CT concepts to VCM Icons

VCM (Visualization of Concept in Medicine) is an iconic language for representing key medical concepts by icons. However, the use of this language with reference terminologies, such as SNOMED CT, will require the mapping of its icons to the terms of these terminologies. Here, we present and evaluate a semi-automatic semantic method for the mapping of SNOMED CT concepts to VCM icons. Both SNOMED CT and VCM are compositional in nature; SNOMED CT is expressed in description logic and VCM semantics are formalized in an OWL ontology. The proposed method involves the manual mapping of a limited number of underlying concepts from the VCM ontology, followed by automatic generation of the rest of the mapping. We applied this method to the clinical findings of the SNOMED CT CORE subset, and 100 randomly-selected mappings were evaluated by three experts. The results obtained were promising, with 82 of the SNOMED CT concepts correctly linked to VCM icons according to the experts. Most of the errors were easy to fix

How do Ontology Mappings Change in the Life Sciences?

Mappings between related ontologies are increasingly used to support data integration and analysis tasks. Changes in the ontologies also require the adaptation of ontology mappings. So far the evolution of ontology mappings has received little attention albeit ontologies change continuously especially in the life sciences. We therefore analyze how mappings between popular life science ontologies evolve for different match algorithms. We also evaluate which semantic ontology changes primarily affect the mappings. We further investigate alternatives to predict or estimate the degree of future mapping changes based on previous ontology and mapping transitions.Comment: Keywords: mapping evolution, ontology matching, ontology evolutio

A Large Scale Dataset for the Evaluation of Ontology Matching Systems

Recently, the number of ontology matching techniques and systems has increased significantly. This makes the issue of their evaluation and comparison more severe. One of the challenges of the ontology matching evaluation is in building large scale evaluation datasets. In fact, the number of possible correspondences between two ontologies grows quadratically with respect to the numbers of entities in these ontologies. This often makes the manual construction of the evaluation datasets demanding to the point of being infeasible for large scale matching tasks. In this paper we present an ontology matching evaluation dataset composed of thousands of matching tasks, called TaxME2. It was built semi-automatically out of the Google, Yahoo and Looksmart web directories. We evaluated TaxME2 by exploiting the results of almost two dozen of state of the art ontology matching systems. The experiments indicate that the dataset possesses the desired key properties, namely it is error-free, incremental, discriminative, monotonic, and hard for the state of the art ontology matching systems. The paper has been accepted for publication in "The Knowledge Engineering Review", Cambridge Universty Press (ISSN: 0269-8889, EISSN: 1469-8005)

Guest editorial preface of the special issue on Ontology matching

shvaiko2007cEditorial, International journal of semantic web and information systems 3(2):i-ii

Source authenticity in the UMLS – A case study of the Minimal Standard Terminology

AbstractAs the UMLS integrates multiple source vocabularies, the integration process requires that certain adaptation be applied to the source. Our interest is in examining the relationship between the UMLS representation of a source vocabulary and the source vocabulary itself. We investigated the integration of the Minimal Standard Terminology (MST) into the UMLS in order to examine how close its UMLS representation is to the source MST. The MST was conceived as a “minimal” list of terms and structure intended for use within computer systems to facilitate standardized reporting of gastrointestinal endoscopic examinations. Although the MST has an overall schema and implied relationship structure, many of the UMLS integrated MST terms were found to be hierarchically orphaned, and with lateral relationships that do not closely adhere to the source MST. Thus, the MST representation within the UMLS significantly differs from that of the source MST. These representation discrepancies may affect the usability of the MST representation in the UMLS for knowledge acquisition. Furthermore, they pose a problem from the perspective of application developers. While these findings may not necessarily apply to other source terminologies, they highlight the conflict between preservation of authentic concept orientation and the UMLS overall desire to provide fully specified names for all source terms

Uberon, an integrative multi-species anatomy ontology

We present Uberon, an integrated cross-species ontology consisting of over 6,500 classes representing a variety of anatomical entities, organized according to traditional anatomical classification criteria. The ontology represents structures in a species-neutral way and includes extensive associations to existing species-centric anatomical ontologies, allowing integration of model organism and human data. Uberon provides a necessary bridge between anatomical structures in different taxa for cross-species inference. It uses novel methods for representing taxonomic variation, and has proved to be essential for translational phenotype analyses. Uberon is available at http://uberon.or

Mapping the gap: curation of phenotype-driven gene discovery in congenital heart disease research

The goal of translational research is to improve public health by accelerating basic science discovery to human application and clinical practice. The NHLBI Bench-to-Bassinet (B2B) program promotes this goal through its translational research initiative. Together with other collaborators of the B2B program, the University of Pittsburgh mutagenesis screen strives to elucidate the underlying genetic and developmental processes of congenital heart disease (CHD), which is a significant source of morbidity and mortality in the population. The screen investigators have curated over 200 mouse models of CHD on the Jackson Laboratory (JAX) Mouse Genome Database (MGD) through a multi-tiered strategy of phenotypic and genetic analyses. Within the translational research paradigm, this screen has contributed to the improvement of public health and patient care by enabling the identification of 107 pathogenic mutations in 68 unique genes as well as providing 62 models of human disease for future research and development of therapies. Two mutant mouse lines, lines 1702 and 2407, will be thoroughly discussed with regard to their significance to research. However, analysis of the screen curation protocol demonstrated inefficiencies representative of problems across the entirety of the translational research continuum. Within this continuum, data must be translated and readily shared between databases in each domain. Research is currently scattered across disconnected, autonomous databases, which prevents data integration and comprehensive retrieval of information from a single platform. Moreover, data are represented as a combination of discordant ontologies and free-text annotations, which further impede cross-species or cross-domain comparisons and database integration. Although ontology mapping endeavors have achieved some success, the process is flawed with unequivocal alignments or inaccuracies and requires extensive manual validation. Harmonization of ontologies through, ideally, a standardized, relational framework, is necessary to improve the efficacy and utility of translational research. In summary, the future progress of translational research, as exemplified by the University of Pittsburgh B2B program, and its potential in improving public health depends on the acceleration of basic discovery to clinical application through a network of integrated databases supported by a unified ontological system

Ontology Matching: OM-2018: Proceedings of the ISWC Workshop

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Proceedings of The Tenth International Workshop on Ontology Matching (OM-2015)

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