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Ayllón-Benitez A, Bernabé-Diaz JA, Espinoza-Arias P, Esnaola-Gonzalez I, Beeckman DSA, McCaig B, Hanzlik K, Cools T, Castro Iragorri C, Palacios N. EPPO ontology: a semantic-driven approach for plant and pest codes representation. Front Artif Intell 2023; 6:1131667. [PMID: 37404339 PMCID: PMC10315572 DOI: 10.3389/frai.2023.1131667] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2022] [Accepted: 05/26/2023] [Indexed: 07/06/2023] Open
Abstract
The agricultural industry and regulatory organizations define strategies and build tools and products for plant protection against pests. To identify different plants and their related pests and avoid inconsistencies between such organizations, an agreed and shared classification is necessary. In this regard, the European and Mediterranean Plant Protection Organization (EPPO) has been working on defining and maintaining a harmonized coding system (EPPO codes). EPPO codes are an easy way of referring to a specific organism by means of short 5 or 6 letter codes instead of long scientific names or ambiguous common names. EPPO codes are freely available in different formats through the EPPO Global Database platform and are implemented as a worldwide standard and used among scientists and experts in both industry and regulatory organizations. One of the large companies that adopted such codes is BASF, which uses them mainly in research and development to build their crop protection and seeds products. However, extracting the information is limited by fixed API calls or files that require additional processing steps. Facing these issues makes it difficult to use the available information flexibly, infer new data connections, or enrich it with external data sources. To overcome such limitations, BASF has developed an internal EPPO ontology to represent the list of codes provided by the EPPO Global Database as well as the regulatory categorization and relationship among them. This paper presents the development process of this ontology along with its enrichment process, which allows the reuse of relevant information available in an external knowledge source such as the NCBI Taxon. In addition, this paper describes the use and adoption of the EPPO ontology within the BASF's Agricultural Solutions division and the lessons learned during this work.
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Affiliation(s)
| | | | | | | | | | | | - Kristin Hanzlik
- BASF SE Data Management and Data Governance, Global Research Services APR/HP, Limburgerhof, Germany
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2
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Precision medicine via the integration of phenotype-genotype information in neonatal genome project. FUNDAMENTAL RESEARCH 2022. [DOI: 10.1016/j.fmre.2022.07.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
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3
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Mazandu GK, Hotchkiss J, Nembaware V, Wonkam A, Mulder N. The Sickle Cell Disease Ontology: recent development and expansion of the universal sickle cell knowledge representation. Database (Oxford) 2022; 2022:6562127. [PMID: 35363306 PMCID: PMC9216550 DOI: 10.1093/database/baac014] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2021] [Revised: 02/15/2022] [Accepted: 03/16/2022] [Indexed: 12/17/2022]
Abstract
The Sickle Cell Disease (SCD) Ontology (SCDO, https://scdontology.h3abionet.org/) provides a comprehensive knowledge base of SCD management, systems and standardized human and machine-readable resources that unambiguously describe terminology and concepts about SCD for researchers, patients and clinicians. The SCDO was launched in 2016 and is continuously updated in quantity, as well as in quality, to effectively support the curation of SCD research, patient databasing and clinical informatics applications. SCD knowledge from the scientific literature is used to update existing SCDO terms and create new terms where necessary. Here, we report major updates to the SCDO, from December 2019 until April 2021, for promoting interoperability and facilitating SCD data harmonization, sharing and integration across different studies and for retrospective multi-site research collaborations. SCDO developers continue to collaborate with the SCD community, clinicians and researchers to improve specific ontology areas and expand standardized descriptions to conditions influencing SCD phenotypic expressions and clinical manifestations of the sickling process, e.g. thalassemias. Database URL: https://scdontology.h3abionet.org/
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Affiliation(s)
- Gaston K Mazandu
- Department of Pathology, Division of Human Genetics, University of Cape Town, Health Sciences Campus, Anzio Rd, Observatory 7925, South Africa
| | - Jade Hotchkiss
- Department of Pathology, Division of Human Genetics, University of Cape Town, Health Sciences Campus, Anzio Rd, Observatory 7925, South Africa
| | - Victoria Nembaware
- Department of Pathology, Division of Human Genetics, University of Cape Town, Health Sciences Campus, Anzio Rd, Observatory 7925, South Africa
| | - Ambroise Wonkam
- Department of Pathology, Division of Human Genetics, University of Cape Town, Health Sciences Campus, Anzio Rd, Observatory 7925, South Africa
| | - Nicola Mulder
- Department of Integrative Biomedical Sciences, Computational Biology Division, IDM, CIDRI-Africa WT Centre, University of Cape Town, Health Sciences Campus. Anzio Rd, Observatory 7925, South Africa
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4
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OWL ontology evolution: understanding and unifying the complex changes. KNOWL ENG REV 2022. [DOI: 10.1017/s0269888922000066] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Abstract
Knowledge-based systems and their ontologies evolve due to different reasons. Ontology evolution is the adaptation of an ontology and the propagation of these changes to dependent artifacts such as queries and other ontologies. Besides identifying basic/simple changes, it is imperative to identify complex changes between two versions of the same ontology to make this adaptation possible. There are many definitions of complex changes applied to ontologies in the literature. However, their specifications across works vary both in formalization and textual description. Some works also use different terminologies to refer to a change, while others use the same vocabulary to refer to distinct changes. Therefore, there is a lack of a unified list of complex changes. The main goals of this paper are: (i) present the primary documents that identify complex changes; (ii) provide critical analyses about the set of the complex changes proposed in the literature and the documents mentioning them; (iii) provide a unified list of complex changes mapping different sets of complex changes proposed by several authors; (iv) present a classification for those complex changes; and (v) describe some open directions of the area. The mappings between the complex changes provide a mechanism to relate and compare different proposals. The unified list is thus a reference for the complex changes published in the literature. It may assist the development of tools to identify changes between two versions of the same ontology and enable the adaptation of artifacts that depend on the evolved ontology.
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5
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People, Projects, Organizations, and Products: Designing a Knowledge Graph to Support Multi-Stakeholder Environmental Planning and Design. ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION 2021. [DOI: 10.3390/ijgi10120823] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
As the need for more broad-scale solutions to environmental problems is increasingly recognized, traditional hierarchical, government-led models of coordination are being supplemented by or transformed into more collaborative inter-organizational networks (i.e., collaboratives, coalitions, partnerships). As diffuse networks, such regional environmental planning and design (REPD) efforts often face challenges in sharing and using spatial and other types of information. Recent advances in semantic knowledge management technologies, such as knowledge graphs, have the potential to address these challenges. In this paper, we first describe the information needs of three multi-stakeholder REPD initiatives in the western USA using a list of 80 need-to-know questions and concerns. The top needs expressed were for help in tracking the participants, institutions, and information products relevant to the REDP’s focus. To address these needs, we developed a prototype knowledge graph based on RDF and GeoSPARQL standards. This semantic approach provided a more flexible data structure than traditional relational databases and also functionality to query information across different providers; however, the lack of semantic data expertise, the complexity of existing software solutions, and limited online hosting options are significant barriers to adoption. These same barriers are more acute for geospatial data, which also faces the added challenge of maintaining and synchronizing both semantic and traditional geospatial datastores.
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Nowotarski SH, Davies EL, Robb SMC, Ross EJ, Matentzoglu N, Doddihal V, Mir M, McClain M, Sánchez Alvarado A. Planarian Anatomy Ontology: a resource to connect data within and across experimental platforms. Development 2021; 148:271068. [PMID: 34318308 PMCID: PMC8353266 DOI: 10.1242/dev.196097] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2020] [Accepted: 06/28/2021] [Indexed: 12/23/2022]
Abstract
As the planarian research community expands, the need for an interoperable data organization framework for tool building has become increasingly apparent. Such software would streamline data annotation and enhance cross-platform and cross-species searchability. We created the Planarian Anatomy Ontology (PLANA), an extendable relational framework of defined Schmidtea mediterranea (Smed) anatomical terms used in the field. At publication, PLANA contains over 850 terms describing Smed anatomy from subcellular to system levels across all life cycle stages, in intact animals and regenerating body fragments. Terms from other anatomy ontologies were imported into PLANA to promote interoperability and comparative anatomy studies. To demonstrate the utility of PLANA as a tool for data curation, we created resources for planarian embryogenesis, including a staging series and molecular fate-mapping atlas, and the Planarian Anatomy Gene Expression database, which allows retrieval of a variety of published transcript/gene expression data associated with PLANA terms. As an open-source tool built using FAIR (findable, accessible, interoperable, reproducible) principles, our strategy for continued curation and versioning of PLANA also provides a platform for community-led growth and evolution of this resource. Summary: Description of the construction of an anatomy ontology tool for planaria with examples of its potential use to curate and mine data across multiple experimental platforms.
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Affiliation(s)
- Stephanie H Nowotarski
- Stowers Institute for Medical Research, Kansas City, MO 64110, USA.,Howard Hughes Medical Institute, Chevy Chase, MD 20815, USA
| | - Erin L Davies
- Stowers Institute for Medical Research, Kansas City, MO 64110, USA.,Center for Cancer Research, National Cancer Institute, National Institutes of Health, Frederick, MD 21702, USA
| | - Sofia M C Robb
- Stowers Institute for Medical Research, Kansas City, MO 64110, USA
| | - Eric J Ross
- Stowers Institute for Medical Research, Kansas City, MO 64110, USA.,Howard Hughes Medical Institute, Chevy Chase, MD 20815, USA
| | - Nicolas Matentzoglu
- European Bioinformatics Institute (EMBL-EBI), European Molecular Biology Laboratory, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Viraj Doddihal
- Stowers Institute for Medical Research, Kansas City, MO 64110, USA
| | - Mol Mir
- Stowers Institute for Medical Research, Kansas City, MO 64110, USA
| | - Melainia McClain
- Stowers Institute for Medical Research, Kansas City, MO 64110, USA
| | - Alejandro Sánchez Alvarado
- Stowers Institute for Medical Research, Kansas City, MO 64110, USA.,Howard Hughes Medical Institute, Chevy Chase, MD 20815, USA
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Lewis-Smith D, Galer PD, Balagura G, Kearney H, Ganesan S, Cosico M, O'Brien M, Vaidiswaran P, Krause R, Ellis CA, Thomas RH, Robinson PN, Helbig I. Modeling seizures in the Human Phenotype Ontology according to contemporary ILAE concepts makes big phenotypic data tractable. Epilepsia 2021; 62:1293-1305. [PMID: 33949685 PMCID: PMC8272408 DOI: 10.1111/epi.16908] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2020] [Revised: 02/19/2021] [Accepted: 04/01/2021] [Indexed: 01/08/2023]
Abstract
Objective: The clinical features of epilepsy determine how it is defined, which in turn guides management. Therefore, consideration of the fundamental clinical entities that comprise an epilepsy is essential in the study of causes, trajectories, and treatment responses. The Human Phenotype Ontology (HPO) is used widely in clinical and research genetics for concise communication and modeling of clinical features, allowing extracted data to be harmonized using logical inference. We sought to redesign the HPO seizure subontology to improve its consistency with current epileptological concepts, supporting the use of large clinical data sets in high-throughput clinical and research genomics. Methods: We created a new HPO seizure subontology based on the 2017 International League Against Epilepsy (ILAE) Operational Classification of Seizure Types, and integrated concepts of status epilepticus, febrile, reflex, and neonatal seizures at different levels of detail. We compared the HPO seizure subontology prior to, and following, our revision, according to the information that could be inferred about the seizures of 791 individuals from three independent cohorts: 2 previously published and 150 newly recruited individuals. Each cohort’s data were provided in a different format and harmonized using the two versions of the HPO. Results: The new seizure subontology increased the number of descriptive concepts for seizures 5-fold. The number of seizure descriptors that could be annotated to the cohort increased by 40% and the total amount of information about individuals’ seizures increased by 38%. The most important qualitative difference was the relationship of focal to bilateral tonic-clonic seizure to generalized-onset and focal-onset seizures.
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Affiliation(s)
- David Lewis-Smith
- Translational and Clinical Research Institute, Newcastle University, Newcastle-upon-Tyne, UK.,Department of Clinical Neurosciences, Royal Victoria Infirmary, Newcastle-upon-Tyne, UK
| | - Peter D Galer
- Division of Neurology, Children's Hospital of Philadelphia, Philadelphia, PA, USA.,The Epilepsy NeuroGenetics Initiative (ENGIN), Children's Hospital of Philadelphia, Philadelphia, PA, USA.,Department of Biomedical and Health Informatics (DBHi), Children's Hospital of Philadelphia, Philadelphia, PA, USA.,Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA
| | - Ganna Balagura
- Medical Genetics Unit, IRCSS Giannina Gaslini Institute, Genoa, Italy
| | - Hugh Kearney
- FutureNeuro the SFI Research Centre for Chronic and Rare Neurological Diseases, Royal College of Surgeons in Ireland, Dublin, Ireland.,Department of Neurology, Beaumont Hospital, Dublin, Ireland
| | - Shiva Ganesan
- Division of Neurology, Children's Hospital of Philadelphia, Philadelphia, PA, USA.,The Epilepsy NeuroGenetics Initiative (ENGIN), Children's Hospital of Philadelphia, Philadelphia, PA, USA.,Department of Biomedical and Health Informatics (DBHi), Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Mahgenn Cosico
- Division of Neurology, Children's Hospital of Philadelphia, Philadelphia, PA, USA.,The Epilepsy NeuroGenetics Initiative (ENGIN), Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Margaret O'Brien
- Division of Neurology, Children's Hospital of Philadelphia, Philadelphia, PA, USA.,The Epilepsy NeuroGenetics Initiative (ENGIN), Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Priya Vaidiswaran
- Division of Neurology, Children's Hospital of Philadelphia, Philadelphia, PA, USA.,The Epilepsy NeuroGenetics Initiative (ENGIN), Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Roland Krause
- Luxembourg Centre for Systems Biomedicine, Université du Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Colin A Ellis
- The Epilepsy NeuroGenetics Initiative (ENGIN), Children's Hospital of Philadelphia, Philadelphia, PA, USA.,Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA
| | - Rhys H Thomas
- Translational and Clinical Research Institute, Newcastle University, Newcastle-upon-Tyne, UK.,Department of Clinical Neurosciences, Royal Victoria Infirmary, Newcastle-upon-Tyne, UK
| | - Peter N Robinson
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA.,Institute for Systems Genomics, University of Connecticut, Farmington, CT, USA
| | - Ingo Helbig
- Division of Neurology, Children's Hospital of Philadelphia, Philadelphia, PA, USA.,The Epilepsy NeuroGenetics Initiative (ENGIN), Children's Hospital of Philadelphia, Philadelphia, PA, USA.,Department of Biomedical and Health Informatics (DBHi), Children's Hospital of Philadelphia, Philadelphia, PA, USA.,Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA
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8
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Forming Cognitive Maps of Ontologies Using Interactive Visualizations. MULTIMODAL TECHNOLOGIES AND INTERACTION 2021. [DOI: 10.3390/mti5010002] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
Ontology datasets, which encode the expert-defined complex objects mapping the entities, relations, and structures of a domain ontology, are increasingly being integrated into the performance of challenging knowledge-based tasks. Yet, it is hard to use ontology datasets within our tasks without first understanding the ontology which it describes. Using visual representation and interaction design, interactive visualization tools can help us learn and develop our understanding of unfamiliar ontologies. After a review of existing tools which visualize ontology datasets, we find that current design practices struggle to support learning tasks when attempting to build understanding of the ontological spaces within ontology datasets. During encounters with unfamiliar spaces, our cognitive processes align with the theoretical framework of cognitive map formation. Furthermore, designing encounters to promote cognitive map formation can improve our performance during learning tasks. In this paper, we examine related work on cognitive load, cognitive map formation, and the use of interactive visualizations during learning tasks. From these findings, we formalize a set of high-level design criteria for visualizing ontology datasets to promote cognitive map formation during learning tasks. We then perform a review of existing tools which visualize ontology datasets and assess their interface design towards their alignment with the cognitive map framework. We then present PRONTOVISE (PRogressive ONTOlogy VISualization Explorer), an interactive visualization tool which applies the high-level criteria within its design. We perform a task-based usage scenario to illustrate the design of PRONTOVISE. We conclude with a discussion of the implications of PRONTOVISE and its use of the criteria towards the design of interactive visualization tools which help us develop understanding of the ontological space within ontology datasets.
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9
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Köhler S, Gargano M, Matentzoglu N, Carmody LC, Lewis-Smith D, Vasilevsky NA, Danis D, Balagura G, Baynam G, Brower AM, Callahan TJ, Chute CG, Est JL, Galer PD, Ganesan S, Griese M, Haimel M, Pazmandi J, Hanauer M, Harris NL, Hartnett M, Hastreiter M, Hauck F, He Y, Jeske T, Kearney H, Kindle G, Klein C, Knoflach K, Krause R, Lagorce D, McMurry JA, Miller JA, Munoz-Torres M, Peters RL, Rapp CK, Rath AM, Rind SA, Rosenberg A, Segal MM, Seidel MG, Smedley D, Talmy T, Thomas Y, Wiafe SA, Xian J, Yüksel Z, Helbig I, Mungall CJ, Haendel MA, Robinson PN. The Human Phenotype Ontology in 2021. Nucleic Acids Res 2021; 49:D1207-D1217. [PMID: 33264411 PMCID: PMC7778952 DOI: 10.1093/nar/gkaa1043] [Citation(s) in RCA: 501] [Impact Index Per Article: 167.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2020] [Revised: 10/11/2020] [Accepted: 11/16/2020] [Indexed: 12/21/2022] Open
Abstract
The Human Phenotype Ontology (HPO, https://hpo.jax.org) was launched in 2008 to provide a comprehensive logical standard to describe and computationally analyze phenotypic abnormalities found in human disease. The HPO is now a worldwide standard for phenotype exchange. The HPO has grown steadily since its inception due to considerable contributions from clinical experts and researchers from a diverse range of disciplines. Here, we present recent major extensions of the HPO for neurology, nephrology, immunology, pulmonology, newborn screening, and other areas. For example, the seizure subontology now reflects the International League Against Epilepsy (ILAE) guidelines and these enhancements have already shown clinical validity. We present new efforts to harmonize computational definitions of phenotypic abnormalities across the HPO and multiple phenotype ontologies used for animal models of disease. These efforts will benefit software such as Exomiser by improving the accuracy and scope of cross-species phenotype matching. The computational modeling strategy used by the HPO to define disease entities and phenotypic features and distinguish between them is explained in detail.We also report on recent efforts to translate the HPO into indigenous languages. Finally, we summarize recent advances in the use of HPO in electronic health record systems.
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Affiliation(s)
| | - Michael Gargano
- Monarch Initiative
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
| | - Nicolas Matentzoglu
- Monarch Initiative
- Semanticly Ltd, London, UK
- European Bioinformatics Institute (EMBL-EBI)
| | - Leigh C Carmody
- Monarch Initiative
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
| | - David Lewis-Smith
- Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, UK
- Clinical Neurosciences, Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, UK
| | - Nicole A Vasilevsky
- Monarch Initiative
- Oregon Clinical & Translational Research Institute, Oregon Health & Science University
| | | | - Ganna Balagura
- Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics, and Maternal and Child Health, University of Genoa, Genoa, Italy
- Pediatric Neurology and Muscular Diseases Unit, IRCCS ‘G. Gaslini’ Institute, Genoa, Italy
| | - Gareth Baynam
- Western Australian Register of Developmental Anomalies, King Edward memorial Hospital, Perth, Australia
- Telethon Kids Institute and the Division of Paediatrics, Faculty of Helath and Medical Sciences, University of Western Australia, Perth, Australia
| | - Amy M Brower
- American College of Medical Genetics and Genomics (ACMG), Bethesda, MD, USA
| | - Tiffany J Callahan
- Computational Bioscience Program, University of Colorado Anschutz Medical Campus, Colorado, USA
| | | | - Johanna L Est
- Department of Pediatrics, Dr. von Hauner Children's Hospital, University Hospital, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Peter D Galer
- Division of Neurology, Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Biomedical and Health Informatics (DBHi), Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Shiva Ganesan
- Division of Neurology, Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Biomedical and Health Informatics (DBHi), Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Matthias Griese
- Department of Pediatrics, Dr. von Hauner Children's Hospital, University Hospital, Ludwig-Maximilians-Universität München, Munich, Germany
- Ludwig-Maximilians University, German Center for Lung Research (DZL), Munich, Germany
| | - Matthias Haimel
- Ludwig Boltzmann Institute for Rare and Undiagnosed Diseases, Vienna, Austria
- CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria
| | - Julia Pazmandi
- Ludwig Boltzmann Institute for Rare and Undiagnosed Diseases, Vienna, Austria
- CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria
- Institute for Systems Genomics, University of Connecticut, Farmington, CT 06032, USA
| | - Marc Hanauer
- INSERM, US14––Orphanet, Plateforme Maladies Rares, Paris, France
| | - Nomi L Harris
- Monarch Initiative
- Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley CA, USA
| | - Michael J Hartnett
- American College of Medical Genetics and Genomics (ACMG), Bethesda, MD, USA
| | - Maximilian Hastreiter
- Department of Pediatrics, Dr. von Hauner Children's Hospital, University Hospital, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Fabian Hauck
- Department of Pediatrics, Dr. von Hauner Children's Hospital, University Hospital, Ludwig-Maximilians-Universität München, Munich, Germany
- German Centre for Infection Research (DZIF), Munich, Germany
| | - Yongqun He
- Unit for Laboratory Animal Medicine, Department of Microbiology and Immunology, Center for Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Tim Jeske
- Department of Pediatrics, Dr. von Hauner Children's Hospital, University Hospital, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Hugh Kearney
- FutureNeuro, SFI Research Centre for Chronic and Rare Neurological Diseases, Ireland
| | - Gerhard Kindle
- Institute for Immunodeficiency, Center for Chronic Immunodeficiency (CCI). Faculty of Medicine, Medical Center - University of Freiburg, Freiburg, Germany
- Centre for Biobanking FREEZE, Faculty of Medicine, Medical Center - University of Freiburg, Freiburg, Germany
| | - Christoph Klein
- Department of Pediatrics, Dr. von Hauner Children's Hospital, University Hospital, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Katrin Knoflach
- Department of Pediatrics, Dr. von Hauner Children's Hospital, University Hospital, Ludwig-Maximilians-Universität München, Munich, Germany
- Ludwig-Maximilians University, German Center for Lung Research (DZL), Munich, Germany
| | - Roland Krause
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, L-4367 Belvaux, Luxembourg
| | - David Lagorce
- INSERM, US14––Orphanet, Plateforme Maladies Rares, Paris, France
| | - Julie A McMurry
- Monarch Initiative
- Translational and Integrative Sciences Center, Department of Environmental and Molecular Toxicology, Oregon State University, OR, USA
| | - Jillian A Miller
- American College of Medical Genetics and Genomics (ACMG), Bethesda, MD, USA
| | - Monica C Munoz-Torres
- Monarch Initiative
- Translational and Integrative Sciences Center, Department of Environmental and Molecular Toxicology, Oregon State University, OR, USA
| | - Rebecca L Peters
- American College of Medical Genetics and Genomics (ACMG), Bethesda, MD, USA
| | - Christina K Rapp
- Department of Pediatrics, Dr. von Hauner Children's Hospital, University Hospital, Ludwig-Maximilians-Universität München, Munich, Germany
- Ludwig-Maximilians University, German Center for Lung Research (DZL), Munich, Germany
| | - Ana M Rath
- INSERM, US14––Orphanet, Plateforme Maladies Rares, Paris, France
| | - Shahmir A Rind
- WA Register of Developmental Anomalies
- Curtin University, Western Australia, Australia
| | - Avi Z Rosenberg
- Division of Kidney-Urologic Pathology, Johns Hopkins University, Baltimore, MD 21205, USA
| | | | - Markus G Seidel
- Research Unit for Pediatric Hematology and Immunology, Division of Pediatric Hemato-Oncology, Department of Pediatrics and Adolescent Medicine, Medical University of Graz, Graz, Austria
| | - Damian Smedley
- The William Harvey Research Institute, Charterhouse Square Barts and the London School of Medicine and Dentistry Queen Mary University of London, London EC1M 6BQ, UK
| | - Tomer Talmy
- Genomic Research Department, Emedgene Technologies, Tel Aviv, Israel
- Faculty of Medicine, Hebrew University Hadassah Medical School, Jerusalem, Israel
| | - Yarlalu Thomas
- West Australian Register of Developmental Anomalies, East Perth, WA, Australia
| | | | - Julie Xian
- Division of Neurology, Children's Hospital of Philadelphia, Philadelphia, PA, USA
- The Epilepsy NeuroGenetics Initiative (ENGIN), Children's Hospital of Philadelphia, PA, USA
| | - Zafer Yüksel
- Human Genetics, Bioscientia GmbH, Ingelheim, Germany
| | - Ingo Helbig
- Department of Neurology, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA
- The Epilepsy NeuroGenetics Initiative (ENGIN), Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Christopher J Mungall
- Monarch Initiative
- Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley CA, USA
| | - Melissa A Haendel
- Monarch Initiative
- Oregon Clinical & Translational Research Institute, Oregon Health & Science University
- Translational and Integrative Sciences Center, Department of Environmental and Molecular Toxicology, Oregon State University, OR, USA
| | - Peter N Robinson
- Monarch Initiative
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
- Institute for Systems Genomics, University of Connecticut, Farmington, CT 06032, USA
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11
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Alexiou G, Meimaris M, Papastefanatos G, Anagnostopoulos I. LinkZoo. INT J SEMANT WEB INF 2020. [DOI: 10.4018/ijswis.2020070101] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
This article presents LinkZoo, a web-based, linked data enabled tool that supports collaborative management of information resources. LinkZoo addresses the modern needs of information-intensive collaboration environments to publish, manage, and share heterogeneous resources within user-driven contexts. Users create and manage diverse types of resources into common spaces such as files, web documents, people, datasets, and calendar events. They can interlink them, annotate them, and share them with other users, thus enabling collaborative editing, as well as enrich them with links to externally linked data resources. Resources are inherently modeled and published as resource description framework (RDF) and can be explicitly interlinked and dereferenced by external applications. LinkZoo supports creation of dynamic communities that enable web-based collaboration through resource sharing and annotating, exposing objects on the linked data Cloud under controlled vocabularies and permissions. The authors demonstrate the applicability of the tool on a popular collaboration use case scenario for sharing and organizing research resources.
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Lovestone S. The European medical information framework: A novel ecosystem for sharing healthcare data across Europe. Learn Health Syst 2020; 4:e10214. [PMID: 32313838 PMCID: PMC7156868 DOI: 10.1002/lrh2.10214] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2019] [Revised: 11/27/2019] [Accepted: 11/29/2019] [Indexed: 12/15/2022] Open
Abstract
INTRODUCTION The European medical information framework (EMIF) was an Innovative Medicines Initiative project jointly supported by the European Union and the European Federation of Pharmaceutical Industries and Associations, that generated a common technology and governance framework to identify, assess and (re)use healthcare data, to facilitate real-world data research. The objectives of EMIF included providing a unified platform to support a wide range of studies within two verification programmes-Alzheimer's disease (EMIF-AD), and metabolic consequences of obesity (EMIF-MET). METHODS The EMIF platform was built around two main data-types: electronic health record data and research cohort data, and the platform architecture composed of a set of tools designed to enable data discovery and characterisation. This included the EMIF catalogue, which allowed users to find relevant data sources, including the data-types collected. Data harmonisation via a common data model were central to the project especially for population data sources. EMIF also developed an ethical code of practice to ensure data protection, patient confidentiality and compliance with the European Data Protection Directive, and GDPR. RESULTS Currently 18 population-based disease agnostic and 60 cohort-based Alzheimer's data partners from across 14 countries are contained within the catalogue, and this will continue to expand. The work conducted in EMIF-AD and EMIF-MET includes standardizing cohorts, summarising baseline characteristics of patients, developing diagnostic algorithms, epidemiological studies, identifying and validating novel biomarkers and selecting potential patient samples for pharmacological intervention. CONCLUSIONS EMIF was designed to provide a sustainable model as demonstrated by the sustainability plans for EMIF-AD. Although network-wide studies using EMIF were not conducted during this project to evaluate its sustainability, learning from EMIF will be used in the follow-on IMI-2 project, European Health Data and Evidence Network (EHDEN). Furthermore, EMIF has facilitated collaborations between partners and continues to promote a wider adoption of principles, technology and architecture through some of its continued work.
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Affiliation(s)
- Simon Lovestone
- Neurodegeneration, Janssen R&D, Janssen Pharmaceutica, Beerse, Belgium
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Chen D, Zhang R, Zhao H, Feng J. A Bibliometric Analysis of the Development of ICD-11 in Medical Informatics. JOURNAL OF HEALTHCARE ENGINEERING 2019; 2019:1649363. [PMID: 31949889 PMCID: PMC6944963 DOI: 10.1155/2019/1649363] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/03/2019] [Revised: 11/07/2019] [Accepted: 11/22/2019] [Indexed: 11/18/2022]
Abstract
The International Classification of Diseases (ICD), which is used to group and report health conditions and factors, provides a basis for healthcare statistics. The 11th revision of the ICD (ICD-11) released by the World Health Organization provides stakeholders with novel perspectives on solving the complexity of critical problems in medical informatics. This study conducts a bibliometric analysis of research published over the period of 1989-2018 to examine the development of ICD-related research and its trends. First, over 4000 ICD-related papers spanning the 30-year period are retrieved from the Web of Science database. Then, based on the meta data of the selected papers, time trend analysis is performed to examine the development of different ICD revisions. Finally, the keywords and topics of these papers are analyzed and visualized using VOSViewer and CiteSpace. Our findings indicate that ICD-11-related research has grown rapidly in recent years compared with studies on ICD-9 and ICD-10. Moreover, the most popular research directions of ICD-11 include the topics psychiatry, psychology, information science, library science, and behavioral science. In terms of perspectives, information system-related research is more common than big data- and knowledge discovery-related work. However, the popularity of big data- and knowledge discovery-related developments has grown in recent years. The use of ICD-11 facilitates the development of medical informatics from the perspectives of information systems, big data, and knowledge discovery.
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Affiliation(s)
- Donghua Chen
- Department of Information Management, School of Economics and Management, Beijing Jiaotong University, Beijing 100044, China
| | - Runtong Zhang
- Department of Information Management, School of Economics and Management, Beijing Jiaotong University, Beijing 100044, China
| | - Hongmei Zhao
- Peking University People's Hospital, Beijing 100044, China
| | - Jiayi Feng
- Department of Information Management, School of Economics and Management, Beijing Jiaotong University, Beijing 100044, China
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Kim HH, Park YR, Lee KH, Song YS, Kim JH. Clinical MetaData ontology: a simple classification scheme for data elements of clinical data based on semantics. BMC Med Inform Decis Mak 2019; 19:166. [PMID: 31429750 PMCID: PMC6701018 DOI: 10.1186/s12911-019-0877-x] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2018] [Accepted: 07/24/2019] [Indexed: 11/26/2022] Open
Abstract
Background The increasing use of common data elements (CDEs) in numerous research projects and clinical applications has made it imperative to create an effective classification scheme for the efficient management of these data elements. We applied high-level integrative modeling of entire clinical documents from real-world practice to create the Clinical MetaData Ontology (CMDO) for the appropriate classification and integration of CDEs that are in practical use in current clinical documents. Methods CMDO was developed using the General Formal Ontology method with a manual iterative process comprising five steps: (1) defining the scope of CMDO by conceptualizing its first-level terms based on an analysis of clinical-practice procedures, (2) identifying CMDO concepts for representing clinical data of general CDEs by examining how and what clinical data are generated with flows of clinical care practices, (3) assigning hierarchical relationships for CMDO concepts, (4) developing CMDO properties (e.g., synonyms, preferred terms, and definitions) for each CMDO concept, and (5) evaluating the utility of CMDO. Results We created CMDO comprising 189 concepts under the 4 first-level classes of Description, Event, Finding, and Procedure. CMDO has 256 definitions that cover the 189 CMDO concepts, with 459 synonyms for 139 (74.0%) of the concepts. All of the CDEs extracted from 6 HL7 templates, 25 clinical documents of 5 teaching hospitals, and 1 personal health record specification were successfully annotated by 41 (21.9%), 89 (47.6%), and 13 (7.0%) of the CMDO concepts, respectively. We created a CMDO Browser to facilitate navigation of the CMDO concept hierarchy and a CMDO-enabled CDE Browser for displaying the relationships between CMDO concepts and the CDEs extracted from the clinical documents that are used in current practice. Conclusions CMDO is an ontology and classification scheme for CDEs used in clinical documents. Given the increasing use of CDEs in many studies and real-world clinical documentation, CMDO will be a useful tool for integrating numerous CDEs from different research projects and clinical documents. The CMDO Browser and CMDO-enabled CDE Browser make it easy to search, share, and reuse CDEs, and also effectively integrate and manage CDEs from different studies and clinical documents. Electronic supplementary material The online version of this article (10.1186/s12911-019-0877-x) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Hye Hyeon Kim
- Seoul National University Biomedical Informatics (SNUBI), Seoul National University College of Medicine, Seoul, 03080, Republic of Korea.,Seoul National University Hospital Biomedical Research Institute, Seoul National University Hospital, Seoul, 03080, Republic of Korea
| | - Yu Rang Park
- Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Seoul, 03722, Republic of Korea
| | - Kye Hwa Lee
- Precision Medicine Center, Seoul National University Hospital, Seoul, 03080, Republic of Korea
| | - Young Soo Song
- Department of Pathology, Hanyang University College of Medicine, Seoul, 04763, Republic of Korea.
| | - Ju Han Kim
- Seoul National University Biomedical Informatics (SNUBI), Seoul National University College of Medicine, Seoul, 03080, Republic of Korea. .,Division of Biomedical Informatics, Seoul National University College of Medicine, 103 Daehak-ro Jongno-gu, Seoul, 03080, Republic of Korea.
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15
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Abstract
In the setting of software development, knowledge can be both dynamic and situation specific, and the complexity of knowledge usually exceeds the capacity of individuals to solve problems by themselves. Software developers not only require knowledge about the general security concepts but also about the context for which software is being developed. With traditional security knowledge formats, which are usually organized in a security-centric way, it is difficult for knowledge users to retrieve the desired security information to fulfill the requirements of their working context. In order to effectively regulate the operation of security knowledge and be an essential part of practical software development practices, we argue that security knowledge must first incorporate additional features, that is, to first specify which contextual information is to be handled, and then represent the security knowledge in a format that is understandable and acceptable to the individuals. This study introduces a novel ontology approach for modeling security knowledge in a context-sensitive manner where the security knowledge can be retrieved while taking the context of the application in hand into consideration. In this paper, we present our security ontology with the design concepts and the evaluation process.
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16
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Rouces J, de Melo G, Hose K. Addressing structural and linguistic heterogeneity in the Web1. AI COMMUN 2018. [DOI: 10.3233/aic-170745] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Affiliation(s)
- Jacobo Rouces
- Department of Computer Science, Aalborg University, Denmark. E-mails: ,
| | - Gerard de Melo
- Department of Computer Science, Rutgers University–New Brunswick, USA. E-mail:
| | - Katja Hose
- Department of Computer Science, Aalborg University, Denmark. E-mails: ,
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17
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Blfgeh A, Warrender J, Hilkens CMU, Lord P. A document-centric approach for developing the tolAPC ontology. J Biomed Semantics 2017; 8:54. [PMID: 29179777 PMCID: PMC5704585 DOI: 10.1186/s13326-017-0159-4] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2017] [Accepted: 10/15/2017] [Indexed: 11/30/2022] Open
Abstract
Background There are many challenges associated with ontology building, as the process often touches on many different subject areas; it needs knowledge of the problem domain, an understanding of the ontology formalism, software in use and, sometimes, an understanding of the philosophical background. In practice, it is very rare that an ontology can be completed by a single person, as they are unlikely to combine all of these skills. So people with these skills must collaborate. One solution to this is to use face-to-face meetings, but these can be expensive and time-consuming for teams that are not co-located. Remote collaboration is possible, of course, but one difficulty here is that domain specialists use a wide-variety of different “formalisms” to represent and share their data – by the far most common, however, is the “office file” either in the form of a word-processor document or a spreadsheet. Here we describe the development of an ontology of immunological cell types; this was initially developed by domain specialists using an Excel spreadsheet for collaboration. We have transformed this spreadsheet into an ontology using highly-programmatic and pattern-driven ontology development. Critically, the spreadsheet remains part of the source for the ontology; the domain specialists are free to update it, and changes will percolate to the end ontology. Results We have developed a new ontology describing immunological cell lines built by instantiating ontology design patterns written programmatically, using values from a spreadsheet catalogue. Conclusions This method employs a spreadsheet that was developed by domain experts. The spreadsheet is unconstrained in its usage and can be freely updated resulting in a new ontology. This provides a general methodology for ontology development using data generated by domain specialists.
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Affiliation(s)
- Aisha Blfgeh
- School of Computing Science, Newcastle University, Newcastle Upon Tyne, NE1 7RU, UK. .,Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, 21589, Saudi Arabia.
| | - Jennifer Warrender
- School of Computing Science, Newcastle University, Newcastle Upon Tyne, NE1 7RU, UK
| | - Catharien M U Hilkens
- Institute of Cellular Medicine, Newcastle University, Newcastle Upon Tyne, NE1 7RU, UK
| | - Phillip Lord
- School of Computing Science, Newcastle University, Newcastle Upon Tyne, NE1 7RU, UK
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18
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Koutsomitropoulos DA, Solomou GD. A learning object ontology repository to support annotation and discovery of educational resources using semantic thesauri. IFLA JOURNAL-INTERNATIONAL FEDERATION OF LIBRARY ASSOCIATIONS 2017. [DOI: 10.1177/0340035217737559] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Open educational resources are currently becoming increasingly available from a multitude of sources and are consequently annotated in many diverse ways. Interoperability concerns that naturally arise can often be resolved through the semantification of metadata descriptions, while at the same time strengthening the knowledge value of resources. SKOS can be a solid linking point offering a standard vocabulary for thematic descriptions, by referencing semantic thesauri. We propose the enhancement and maintenance of educational resources’ metadata in the form of learning object ontologies and introduce the notion of a learning object ontology repository that can help towards their publication, discovery and reuse. At the same time, linking to thesauri datasets and contextualized sources interrelates learning objects with linked data and exposes them to the Web of Data. We build a set of extensions and workflows on top of contemporary ontology management tools, such as WebProtégé, that can make it suitable as a learning object ontology repository. The proposed approach and implementation can help libraries and universities in discovering, managing and incorporating open educational resources and enhancing current curricula.
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19
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Ziaimatin H, Groza T, Tudorache T, Hunter J. Modelling expertise at different levels of granularity using semantic similarity measures in the context of collaborative knowledge-curation platforms. J Intell Inf Syst 2017; 47:469-490. [PMID: 28077914 DOI: 10.1007/s10844-015-0376-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
Abstract
Collaboration platforms provide a dynamic environment where the content is subject to ongoing evolution through expert contributions. The knowledge embedded in such platforms is not static as it evolves through incremental refinements - or micro-contributions. Such refinements provide vast resources of tacit knowledge and experience. In our previous work, we proposed and evaluated a Semantic and Time-dependent Expertise Profiling (STEP) approach for capturing expertise from micro-contributions. In this paper we extend our investigation to structured micro-contributions that emerge from an ontology engineering environment, such as the one built for developing the International Classification of Diseases (ICD) revision 11. We take advantage of the semantically related nature of these structured micro-contributions to showcase two major aspects: (i) a novel semantic similarity metric, in addition to an approach for creating bottom-up baseline expertise profiles using expertise centroids; and (ii) the application of STEP in this new environment combined with the use of the same semantic similarity measure to both compare STEP against baseline profiles, as well as to investigate the coverage of these baseline profiles by STEP.
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Affiliation(s)
- Hasti Ziaimatin
- School of ITEE, The University of Queensland, Queensland, Australia
| | - Tudor Groza
- School of ITEE, The University of Queensland, Queensland, Australia
| | | | - Jane Hunter
- School of ITEE, The University of Queensland, Queensland, Australia
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20
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Hill DP, D'Eustachio P, Berardini TZ, Mungall CJ, Renedo N, Blake JA. Modeling biochemical pathways in the gene ontology. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2016; 2016:baw126. [PMID: 27589964 PMCID: PMC5009323 DOI: 10.1093/database/baw126] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/24/2016] [Accepted: 08/10/2016] [Indexed: 12/05/2022]
Abstract
The concept of a biological pathway, an ordered sequence of molecular transformations, is used to collect and represent molecular knowledge for a broad span of organismal biology. Representations of biomedical pathways typically are rich but idiosyncratic presentations of organized knowledge about individual pathways. Meanwhile, biomedical ontologies and associated annotation files are powerful tools that organize molecular information in a logically rigorous form to support computational analysis. The Gene Ontology (GO), representing Molecular Functions, Biological Processes and Cellular Components, incorporates many aspects of biological pathways within its ontological representations. Here we present a methodology for extending and refining the classes in the GO for more comprehensive, consistent and integrated representation of pathways, leveraging knowledge embedded in current pathway representations such as those in the Reactome Knowledgebase and MetaCyc. With carbohydrate metabolic pathways as a use case, we discuss how our representation supports the integration of variant pathway classes into a unified ontological structure that can be used for data comparison and analysis.
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Affiliation(s)
- David P Hill
- The Jackson Laboratory, Bar Harbor, ME 04609, USA
| | - Peter D'Eustachio
- Department of Biochemistry and Molecular Pharmacology, NYU School of Medicine, New York, NY 10016, USA
| | - Tanya Z Berardini
- Arabidopsis Information Resource, Phoenix Bioinformatics, Redwood City, CA 94063, USA
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21
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Halilaj L, Grangel-González I, Coskun G, Lohmann S, Auer S. Git4Voc: Collaborative Vocabulary Development Based on Git. INTERNATIONAL JOURNAL OF SEMANTIC COMPUTING 2016. [DOI: 10.1142/s1793351x16400067] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Collaborative vocabulary development in the context of data integration is the process of finding consensus between experts with different backgrounds, system understanding and domain knowledge. The complexity of this process increases with the number of people involved, the variety of the systems to be integrated and the dynamics of their domain. In this paper, we advocate that the usage of a powerful version control system is one of the keys to address this problem. Driven by this idea and the success of the version control system Git in the context of software development, we investigate the applicability of Git for collaborative vocabulary development. Even though vocabulary development and software development have much more similarities than differences, there are still important challenges. These need to be considered in the development of a successful versioning and collaboration system for vocabulary development. Therefore, this paper starts by presenting the challenges we are faced with during the collaborative creation of vocabularies and discusses its distinction to software development. Drawing from these findings, we present Git4Voc which comprises guidelines on how Git can be adopted to vocabulary development. Finally, we demonstrate how Git hooks can be implemented to go beyond the plain functionality of Git by realizing vocabulary-specific features like syntactic validation and semantic diffs.
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Affiliation(s)
- Lavdim Halilaj
- Enterprise Information Systems, University of Bonn, Römerstrasse 164, 53117, Bonn, Germany
| | - Irlán Grangel-González
- Enterprise Information Systems, University of Bonn, Römerstrasse 164, 53117, Bonn, Germany
| | - Gökhan Coskun
- Enterprise Information Systems, University of Bonn, Römerstrasse 164, 53117, Bonn, Germany
| | - Steffen Lohmann
- Enterprise Information Systems, University of Bonn, Römerstrasse 164, 53117, Bonn, Germany
| | - Sören Auer
- Enterprise Information Systems, University of Bonn, Römerstrasse 164, 53117, Bonn, Germany
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22
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Ong E, He Y. Community-based Ontology Development, Annotation and Discussion with MediaWiki extension Ontokiwi and Ontokiwi-based Ontobedia. AMIA JOINT SUMMITS ON TRANSLATIONAL SCIENCE PROCEEDINGS. AMIA JOINT SUMMITS ON TRANSLATIONAL SCIENCE 2016; 2016:65-74. [PMID: 27570653 PMCID: PMC5001762] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/03/2022]
Abstract
Hundreds of biological and biomedical ontologies have been developed to support data standardization, integration and analysis. Although ontologies are typically developed for community usage, community efforts in ontology development are limited. To support ontology visualization, distribution, and community-based annotation and development, we have developed Ontokiwi, an ontology extension to the MediaWiki software. Ontokiwi displays hierarchical classes and ontological axioms. Ontology classes and axioms can be edited and added using Ontokiwi form or MediaWiki source editor. Ontokiwi also inherits MediaWiki features such as Wikitext editing and version control. Based on the Ontokiwi/MediaWiki software package, we have developed Ontobedia, which targets to support community-based development and annotations of biological and biomedical ontologies. As demonstrations, we have loaded the Ontology of Adverse Events (OAE) and the Cell Line Ontology (CLO) into Ontobedia. Our studies showed that Ontobedia was able to achieve expected Ontokiwi features.
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Affiliation(s)
- Edison Ong
- University of Michigan Medical School, Ann Arbor, MI
| | - Yongqun He
- University of Michigan Medical School, Ann Arbor, MI
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23
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Ochs C, Geller J, Perl Y, Musen MA. A unified software framework for deriving, visualizing, and exploring abstraction networks for ontologies. J Biomed Inform 2016; 62:90-105. [PMID: 27345947 DOI: 10.1016/j.jbi.2016.06.008] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2016] [Revised: 06/02/2016] [Accepted: 06/22/2016] [Indexed: 11/27/2022]
Abstract
Software tools play a critical role in the development and maintenance of biomedical ontologies. One important task that is difficult without software tools is ontology quality assurance. In previous work, we have introduced different kinds of abstraction networks to provide a theoretical foundation for ontology quality assurance tools. Abstraction networks summarize the structure and content of ontologies. One kind of abstraction network that we have used repeatedly to support ontology quality assurance is the partial-area taxonomy. It summarizes structurally and semantically similar concepts within an ontology. However, the use of partial-area taxonomies was ad hoc and not generalizable. In this paper, we describe the Ontology Abstraction Framework (OAF), a unified framework and software system for deriving, visualizing, and exploring partial-area taxonomy abstraction networks. The OAF includes support for various ontology representations (e.g., OWL and SNOMED CT's relational format). A Protégé plugin for deriving "live partial-area taxonomies" is demonstrated.
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Affiliation(s)
- Christopher Ochs
- Computer Science Department, New Jersey Institute of Technology, Newark, NJ 07102, USA.
| | - James Geller
- Computer Science Department, New Jersey Institute of Technology, Newark, NJ 07102, USA
| | - Yehoshua Perl
- Computer Science Department, New Jersey Institute of Technology, Newark, NJ 07102, USA
| | - Mark A Musen
- Stanford Center for Biomedical Informatics Research, Stanford University, Stanford, CA 94305, USA
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Baumeister J, Striffler A, Brandt M, Neumann M. Collaborative decision support and documentation in chemical safety with KnowSEC. J Cheminform 2016; 8:21. [PMID: 27110289 PMCID: PMC4842272 DOI: 10.1186/s13321-016-0132-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2015] [Accepted: 04/07/2016] [Indexed: 11/25/2022] Open
Abstract
To protect the health of human and environment, the European Union implemented the REACH regulation for chemical substances. REACH is an acronym for Registration, Evaluation, Authorization, and Restriction of Chemicals. Under REACH, the authorities have the task of assessing chemical substances, especially those that might pose a risk to human health or environment. The work under REACH is scientifically, technically and procedurally a complex and knowledge-intensive task that is jointly performed by the European Chemicals Agency and member state authorities in Europe. The assessment of substances under REACH conducted in the German Environment Agency is supported by the knowledge-based system KnowSEC, which is used for the screening, documentation, and decision support when working on chemical substances. The software KnowSEC integrates advanced semantic technologies and strong problem solving methods. It allows for the collaborative work on substances in the context of the European REACH regulation. We discuss the applied methods and process models and we report on experiences with the implementation and use of the system.
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Affiliation(s)
- Joachim Baumeister
- Institute of Computer Science, University of Würzburg, Am Hubland, 97074 Würzburg, Germany
| | | | - Marc Brandt
- Section IV 2.3 Chemicals, The Federal Environment Agency (Umweltbundesamt), Wörlitzer Platz 1, 06844 Dessau-Roßlau, Germany
| | - Michael Neumann
- Section IV 2.3 Chemicals, The Federal Environment Agency (Umweltbundesamt), Wörlitzer Platz 1, 06844 Dessau-Roßlau, Germany
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Krief A. Adventures in Chemistry and in Information Technology: Using Chemistry Skill to Finance Partnership with Computers. CHEM REC 2016; 16:520-80. [PMID: 26849845 DOI: 10.1002/tcr.201500264] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Affiliation(s)
- Alain Krief
- Executive Director IOCD (International Organization for Chemical Sciences in Development), Emeritus Professor University of Namur Chemistry department (COMS & CMI Laboratories), Location: Chemistry Building, 2 rue Joseph Grafé, B-5000, Namur, Belgium, 3rd floor, door 311c.
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Morente-Molinera J, Pérez I, Ureña M, Herrera-Viedma E. Building and managing fuzzy ontologies with heterogeneous linguistic information. Knowl Based Syst 2015. [DOI: 10.1016/j.knosys.2015.07.035] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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Analysis and Prediction of User Editing Patterns in Ontology Development Projects. JOURNAL ON DATA SEMANTICS 2015; 4:117-132. [PMID: 26052350 DOI: 10.1007/s13740-014-0047-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
Abstract
The development of real-world ontologies is a complex undertaking, commonly involving a group of domain experts with different expertise that work together in a collaborative setting. These ontologies are usually large scale and have complex structures. To assist in the authoring process, ontology tools are key at making the editing process as streamlined as possible. Being able to predict confidently what the users are likely to do next as they edit an ontology will enable us to focus and structure the user interface accordingly and to facilitate more efficient interaction and information discovery. In this paper, we use data mining, specifically the association rule mining, to investigate whether we are able to predict the next editing operation that a user will make based on the change history. We simulated and evaluated continuous prediction across time using sliding window model. We used the association rule mining to generate patterns from the ontology change logs in the training window and tested these patterns on logs in the adjacent testing window. We also evaluated the impact of different training and testing window sizes on the prediction accuracies. At last, we evaluated our prediction accuracies across different user groups and different ontologies. Our results indicate that we can indeed predict the next editing operation a user is likely to make. We will use the discovered editing patterns to develop a recommendation module for our editing tools, and to design user interface components that better fit with the user editing behaviors.
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Horridge M, Parsia B, Noy NF, Musenm MA. Reasoning based quality assurance of medical ontologies: a case study. AMIA ... ANNUAL SYMPOSIUM PROCEEDINGS. AMIA SYMPOSIUM 2014; 2014:671-80. [PMID: 25954373 PMCID: PMC4420015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 06/04/2023]
Abstract
The World Health Organisation is using OWL as a key technology to develop ICD-11 - the next version of the well-known International Classification of Diseases. Besides providing better opportunities for data integration and linkages to other well-known ontologies such as SNOMED-CT, one of the main promises of using OWL is that it will enable various forms of automated error checking. In this paper we investigate how automated OWL reasoning, along with a Justification Finding Service can be used as a Quality Assurance technique for the development of large and complex ontologies such as ICD-11. Using the International Classification of Traditional Medicine (ICTM) - Chapter 24 of ICD-11 - as a case study, and an expert panel of knowledge engineers, we reveal the kinds of problems that can occur, how they can be detected, and how they can be fixed. Specifically, we found that a logically inconsistent version of the ICTM ontology could be repaired using justifications (minimal entailing subsets of an ontology). Although over 600 justifications for the inconsistency were initially computed, we found that there were three main manageable patterns or categories of justifications involving TBox and ABox axioms. These categories represented meaningful domain errors to an expert panel of ICTM project knowledge engineers, who were able to use them to successfully determine the axioms that needed to be revised in order to fix the problem. All members of the expert panel agreed that the approach was useful for debugging and ensuring the quality of ICTM.
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Walk S, Singer P, Strohmaier M, Tudorache T, Musen MA, Noy NF. Discovering beaten paths in collaborative ontology-engineering projects using Markov chains. J Biomed Inform 2014; 51:254-71. [PMID: 24953242 PMCID: PMC4194274 DOI: 10.1016/j.jbi.2014.06.004] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2014] [Revised: 06/04/2014] [Accepted: 06/07/2014] [Indexed: 11/26/2022]
Abstract
Biomedical taxonomies, thesauri and ontologies in the form of the International Classification of Diseases as a taxonomy or the National Cancer Institute Thesaurus as an OWL-based ontology, play a critical role in acquiring, representing and processing information about human health. With increasing adoption and relevance, biomedical ontologies have also significantly increased in size. For example, the 11th revision of the International Classification of Diseases, which is currently under active development by the World Health Organization contains nearly 50,000 classes representing a vast variety of different diseases and causes of death. This evolution in terms of size was accompanied by an evolution in the way ontologies are engineered. Because no single individual has the expertise to develop such large-scale ontologies, ontology-engineering projects have evolved from small-scale efforts involving just a few domain experts to large-scale projects that require effective collaboration between dozens or even hundreds of experts, practitioners and other stakeholders. Understanding the way these different stakeholders collaborate will enable us to improve editing environments that support such collaborations. In this paper, we uncover how large ontology-engineering projects, such as the International Classification of Diseases in its 11th revision, unfold by analyzing usage logs of five different biomedical ontology-engineering projects of varying sizes and scopes using Markov chains. We discover intriguing interaction patterns (e.g., which properties users frequently change after specific given ones) that suggest that large collaborative ontology-engineering projects are governed by a few general principles that determine and drive development. From our analysis, we identify commonalities and differences between different projects that have implications for project managers, ontology editors, developers and contributors working on collaborative ontology-engineering projects and tools in the biomedical domain.
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Affiliation(s)
- Simon Walk
- Institute for Information Systems and Computer Media, Graz University of Technology, Austria.
| | - Philipp Singer
- GESIS - Leibniz-Institute for the Social Sciences, Cologne, Germany
| | - Markus Strohmaier
- GESIS - Leibniz-Institute for the Social Sciences, Cologne, Germany; Dept. of Computer Science, University of Koblenz-Landau, Germany
| | - Tania Tudorache
- Stanford Center for Biomedical Informatics Research, Stanford University, USA
| | - Mark A Musen
- Stanford Center for Biomedical Informatics Research, Stanford University, USA
| | - Natalya F Noy
- Stanford Center for Biomedical Informatics Research, Stanford University, USA
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Lozano-Rubí R, Pastor X, Lozano E. OWLing Clinical Data Repositories With the Ontology Web Language. JMIR Med Inform 2014; 2:e14. [PMID: 25599697 PMCID: PMC4288111 DOI: 10.2196/medinform.3023] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2013] [Revised: 02/24/2014] [Accepted: 04/28/2014] [Indexed: 11/23/2022] Open
Abstract
Background The health sciences are based upon information. Clinical information is usually stored and managed by physicians with precarious tools, such as spreadsheets. The biomedical domain is more complex than other domains that have adopted information and communication technologies as pervasive business tools. Moreover, medicine continuously changes its corpus of knowledge because of new discoveries and the rearrangements in the relationships among concepts. This scenario makes it especially difficult to offer good tools to answer the professional needs of researchers and constitutes a barrier that needs innovation to discover useful solutions. Objective The objective was to design and implement a framework for the development of clinical data repositories, capable of facing the continuous change in the biomedicine domain and minimizing the technical knowledge required from final users. Methods We combined knowledge management tools and methodologies with relational technology. We present an ontology-based approach that is flexible and efficient for dealing with complexity and change, integrated with a solid relational storage and a Web graphical user interface. Results Onto Clinical Research Forms (OntoCRF) is a framework for the definition, modeling, and instantiation of data repositories. It does not need any database design or programming. All required information to define a new project is explicitly stated in ontologies. Moreover, the user interface is built automatically on the fly as Web pages, whereas data are stored in a generic repository. This allows for immediate deployment and population of the database as well as instant online availability of any modification. Conclusions OntoCRF is a complete framework to build data repositories with a solid relational storage. Driven by ontologies, OntoCRF is more flexible and efficient to deal with complexity and change than traditional systems and does not require very skilled technical people facilitating the engineering of clinical software systems.
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Affiliation(s)
- Raimundo Lozano-Rubí
- Hospital Clínic, Unit of Medical Informatics, University of Barcelona, Barcelona, Spain.
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Younesi E, Ansari S, Guendel M, Ahmadi S, Coggins C, Hoeng J, Hofmann-Apitius M, Peitsch MC. CSEO - the Cigarette Smoke Exposure Ontology. J Biomed Semantics 2014; 5:31. [PMID: 25093069 PMCID: PMC4120729 DOI: 10.1186/2041-1480-5-31] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2013] [Accepted: 07/03/2014] [Indexed: 01/12/2023] Open
Abstract
BACKGROUND In the past years, significant progress has been made to develop and use experimental settings for extensive data collection on tobacco smoke exposure and tobacco smoke exposure-associated diseases. Due to the growing number of such data, there is a need for domain-specific standard ontologies to facilitate the integration of tobacco exposure data. RESULTS The CSEO (version 1.0) is composed of 20091 concepts. The ontology in its current form is able to capture a wide range of cigarette smoke exposure concepts within the knowledge domain of exposure science with a reasonable sensitivity and specificity. Moreover, it showed a promising performance when used to answer domain expert questions. The CSEO complies with standard upper-level ontologies and is freely accessible to the scientific community through a dedicated wiki at https://publicwiki-01.fraunhofer.de/CSEO-Wiki/index.php/Main_Page. CONCLUSIONS The CSEO has potential to become a widely used standard within the academic and industrial community. Mainly because of the emerging need of systems toxicology to controlled vocabularies and also the lack of suitable ontologies for this domain, the CSEO prepares the ground for integrative systems-based research in the exposure science.
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Affiliation(s)
- Erfan Younesi
- Fraunhofer Institute for Algorithms and Scientific Computing SCAI, Schloss Birlinghoven, 53754 Sankt Augustin, Germany
| | - Sam Ansari
- Philip Morris International R&D, Philip Morris Products S.A., Quai Jeanrenaud 5, 2000 Neuchâtel, Switzerland
| | - Michaela Guendel
- Fraunhofer Institute for Algorithms and Scientific Computing SCAI, Schloss Birlinghoven, 53754 Sankt Augustin, Germany
| | - Shiva Ahmadi
- Fraunhofer Institute for Algorithms and Scientific Computing SCAI, Schloss Birlinghoven, 53754 Sankt Augustin, Germany
| | - Chris Coggins
- Carson Watts Consulting, 1266 Carson Watts Rd, King, NC 27021-7453, USA
| | - Julia Hoeng
- Philip Morris International R&D, Philip Morris Products S.A., Quai Jeanrenaud 5, 2000 Neuchâtel, Switzerland
| | - Martin Hofmann-Apitius
- Fraunhofer Institute for Algorithms and Scientific Computing SCAI, Schloss Birlinghoven, 53754 Sankt Augustin, Germany
| | - Manuel C Peitsch
- Philip Morris International R&D, Philip Morris Products S.A., Quai Jeanrenaud 5, 2000 Neuchâtel, Switzerland
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Walk S, Pöschko J, Strohmaier M, Andrews K, Tudorache T, Noy NF, Nyulas C, Musen MA. PragmatiX: An Interactive Tool for Visualizing the Creation Process Behind Collaboratively Engineered Ontologies. INT J SEMANT WEB INF 2014; 9:45-78. [PMID: 24465189 DOI: 10.4018/jswis.2013010103] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
With the emergence of tools for collaborative ontology engineering, more and more data about the creation process behind collaborative construction of ontologies is becoming available. Today, collaborative ontology engineering tools such as Collaborative Protégé offer rich and structured logs of changes, thereby opening up new challenges and opportunities to study and analyze the creation of collaboratively constructed ontologies. While there exists a plethora of visualization tools for ontologies, they have primarily been built to visualize aspects of the final product (the ontology) and not the collaborative processes behind construction (e.g. the changes made by contributors over time). To the best of our knowledge, there exists no ontology visualization tool today that focuses primarily on visualizing the history behind collaboratively constructed ontologies. Since the ontology engineering processes can influence the quality of the final ontology, we believe that visualizing process data represents an important stepping-stone towards better understanding of managing the collaborative construction of ontologies in the future. In this application paper, we present a tool - PragmatiX - which taps into structured change logs provided by tools such as Collaborative Protégé to visualize various pragmatic aspects of collaborative ontology engineering. The tool is aimed at managers and leaders of collaborative ontology engineering projects to help them in monitoring progress, in exploring issues and problems, and in tracking quality-related issues such as overrides and coordination among contributors. The paper makes the following contributions: (i) we present PragmatiX, a tool for visualizing the creation process behind collaboratively constructed ontologies (ii) we illustrate the functionality and generality of the tool by applying it to structured logs of changes of two large collaborative ontology-engineering projects and (iii) we conduct a heuristic evaluation of the tool with domain experts to uncover early design challenges and opportunities for improvement. Finally, we hope that this work sparks a new line of research on visualization tools for collaborative ontology engineering projects.
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Affiliation(s)
- Simon Walk
- Knowledge Management Institute, Graz University of Technology, Inffeldgasse 21a/II, 8010 Graz
| | - Jan Pöschko
- Knowledge Management Institute, Graz University of Technology, Inffeldgasse 21a/II, 8010 Graz
| | - Markus Strohmaier
- Knowledge Management Institute, Graz University of Technology, Inffeldgasse 21a/II, 8010 Graz
| | - Keith Andrews
- Institute for Information Systems and Computer Media, Graz University of Technology, Inffeldgasse 16c, 8010 Graz
| | - Tania Tudorache
- Stanford Center for Biomedical Informatics Research, Stanford University, 1265 Welch Road, Stanford, CA 94305-5479, USA
| | - Natalya F Noy
- Stanford Center for Biomedical Informatics Research, Stanford University, 1265 Welch Road, Stanford, CA 94305-5479, USA
| | - Csongor Nyulas
- Stanford Center for Biomedical Informatics Research, Stanford University, 1265 Welch Road, Stanford, CA 94305-5479, USA
| | - Mark A Musen
- Stanford Center for Biomedical Informatics Research, Stanford University, 1265 Welch Road, Stanford, CA 94305-5479, USA
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Abstract
Novel social media collaboration platforms, such as games with a purpose and mechanised labour marketplaces, are increasingly used for enlisting large populations of non-experts in crowdsourced knowledge acquisition processes. Climate Quiz uses this paradigm for acquiring environmental domain knowledge from non-experts. The game’s usage statistics and the quality of the produced data show that Climate Quiz has managed to attract a large number of players but noisy input data and task complexity led to low player engagement and suboptimal task throughput and data quality. To address these limitations, the authors propose embedding the game into a hybrid-genre workflow, which supplements the game with a set of tasks outsourced to micro-workers, thus leveraging the complementary nature of games with a purpose and mechanised labour platforms. Experimental evaluations suggest that such workflows are feasible and have positive effects on the game’s enjoyment level and the quality of its output.
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Affiliation(s)
- Marta Sabou
- Department of New Media Technology, MODUL University Vienna, Vienna, Austria
| | - Arno Scharl
- Department of New Media Technology, MODUL University Vienna, Vienna, Austria
| | - Michael Föls
- Research Institute for Computational Methods, Vienna University of Economics and Business, Vienna, Austria
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Strohmaier M, Walk S, Pöschko J, Lamprecht D, Tudorache T, Nyulas C, Musen MA, Noy NF. How Ontologies are Made: Studying the Hidden Social Dynamics Behind Collaborative Ontology Engineering Projects. WEB SEMANTICS (ONLINE) 2013; 20:10.1016/j.websem.2013.04.001. [PMID: 24311994 PMCID: PMC3845806 DOI: 10.1016/j.websem.2013.04.001] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/28/2023]
Abstract
Traditionally, evaluation methods in the field of semantic technologies have focused on the end result of ontology engineering efforts, mainly, on evaluating ontologies and their corresponding qualities and characteristics. This focus has led to the development of a whole arsenal of ontology-evaluation techniques that investigate the quality of ontologies as a product. In this paper, we aim to shed light on the process of ontology engineering construction by introducing and applying a set of measures to analyze hidden social dynamics. We argue that especially for ontologies which are constructed collaboratively, understanding the social processes that have led to its construction is critical not only in understanding but consequently also in evaluating the ontology. With the work presented in this paper, we aim to expose the texture of collaborative ontology engineering processes that is otherwise left invisible. Using historical change-log data, we unveil qualitative differences and commonalities between different collaborative ontology engineering projects. Explaining and understanding these differences will help us to better comprehend the role and importance of social factors in collaborative ontology engineering projects. We hope that our analysis will spur a new line of evaluation techniques that view ontologies not as the static result of deliberations among domain experts, but as a dynamic, collaborative and iterative process that needs to be understood, evaluated and managed in itself. We believe that advances in this direction would help our community to expand the existing arsenal of ontology evaluation techniques towards more holistic approaches.
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Affiliation(s)
- Markus Strohmaier
- Knowledge Management Institute, Graz University of Technology, Austria ; Stanford Center for Biomedical Informatics Research, Stanford University, USA
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Peroni S, Shotton D, Vitali F. Tools for the Automatic Generation of Ontology Documentation. INT J SEMANT WEB INF 2013. [DOI: 10.4018/jswis.2013010102] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Ontologies are knowledge constructs essential for creation of the Web of Data. Good documentation is required to permit people to understand ontologies and thus employ them correctly, but this is costly to create by tradition authorship methods, and is thus inefficient to create in this way until an ontology has matured into a stable structure. The authors describe three tools, LODE, Parrot and the OWLDoc-based Ontology Browser, that can be used automatically to create documentation from a well-formed OWL ontology at any stage of its development. They contrast their properties and then report on the authors’ evaluation of their effectiveness and usability, determined by two task-based user testing sessions.
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Affiliation(s)
- Silvio Peroni
- Department of Computer Science and Engineering, University of Bologna, Bologna, Italy
| | - David Shotton
- Department of Zoology, University of Oxford, Oxford, UK
| | - Fabio Vitali
- Department of Computer Science and Engineering, University of Bologna, Bologna, Italy
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Indented Tree or Graph? A Usability Study of Ontology Visualization Techniques in the Context of Class Mapping Evaluation. ADVANCED INFORMATION SYSTEMS ENGINEERING 2013. [DOI: 10.1007/978-3-642-41335-3_8] [Citation(s) in RCA: 29] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
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