Semantic Standards Research Articles

PCEtoFHIR: Decomposition of Postcoordinated SNOMED CT Expressions for Storage as HL7 FHIR Resources.

To ensure interoperability, both structural and semantic standards must be followed. For exchanging medical data between information systems, the structural standard FHIR (Fast Healthcare Interoperability Resources) has recently gained popularity. Regarding semantic interoperability, the reference terminology SNOMED Clinical Terms (SNOMED CT), as a semantic standard, allows for postcoordination, offering advantages over many other vocabularies. These postcoordinated expressions (PCEs) make SNOMED CT an expressive and flexible interlingua, allowing for precise coding of medical facts. However, this comes at the cost of increased complexity, as well as challenges in storage and processing. Additionally, the boundary between semantic (terminology) and structural (information model) standards becomes blurred, leading to what is known as the TermInfo problem. Although often viewed critically, the TermInfo overlap can also be explored for its potential benefits, such as enabling flexible transformation of parts of PCEs. In this paper, an alternative solution for storing PCEs is presented, which involves combining them with the FHIR data model. Ultimately, all components of a PCE should be expressible solely through precoordinated concepts that are linked to the appropriate elements of the information model. The approach involves storing PCEs decomposed into their components in alignment with FHIR resources. By utilizing the Web Ontology Language (OWL) to generate an OWL ClassExpression, and combining it with an external reasoner and semantic similarity measures, a precoordinated SNOMED CT concept that most accurately describes the PCE is identified as a Superconcept. In addition, the nonmatching attribute relationships between the Superconcept and the PCE are identified as the "Delta." Once SNOMED CT attributes are manually mapped to FHIR elements, FHIRPath expressions can be defined for both the Superconcept and the Delta, allowing the identified precoordinated codes to be stored within FHIR resources. A web application called PCEtoFHIR was developed to implement this approach. In a validation process with 600 randomly selected precoordinated concepts, the formal correctness of the generated OWL ClassExpressions was verified. Additionally, 33 PCEs were used for two separate validation tests. Based on these validations, it was demonstrated that a previously proposed semantic similarity calculation is suitable for determining the Superconcept. Additionally, the 33 PCEs were used to confirm the correct functioning of the entire approach. Furthermore, the FHIR StructureMaps were reviewed and deemed meaningful by FHIR experts. PCEtoFHIR offers services to decompose PCEs for storage within FHIR resources. When creating structure mappings for specific subdomains of SNOMED CT concepts (eg, allergies) to desired FHIR profiles, the use of SNOMED CT Expression Templates has proven highly effective. Domain experts can create templates with appropriate mappings, which can then be easily reused in a constrained manner by end users.

Development and Implementation of Semantic Information Standards for Oncology.

Data exchange in oncological healthcare is hindered by insufficient standardization agreements. An Information Standard comprises agreements facilitating accurate communication of care information with the necessary quality and timeliness. We introduce a structured approach to designing, implementing, and maintaining semantic information standards for oncology, supporting information use across medical scenarios. It consists of an element dataset organized into three tiers, ensuring comprehensive documentation and reliable information exchange. These agreements enhance health data interoperability and system functionality, governed by semantic standardization. Together with communication standards, they empower healthcare professionals with extensive medical records and grant patients control over their health data. Consequently, a high-quality semantic information standard supports both providers and patients, and is adequate during development and manageable during maintenance.

HL7 FHIR in Health Research: A FHIR Specification for Metadata in Clinical, Epidemiological, and Public Health Studies.

This work presents the Fast Healthcare Interoperability Resources (FHIR®) specification of the NFDI4Health Metadata schema based on FHIR Version 4: We created 16 profiles to facilitate the integration of clinical, epidemiological, and public health study data. Despite challenges arising from the extensive MDS as well as missing concepts in semantic standards, it marks a significant advance in applying information technology standards to health research.

From Clinical Information Systems to Personalized Health Knowledge Graphs.

This paper presents a versatile solution to formally represent the contents of electronic health records. It is based on the knowledge graph paradigm, and semantic web standards RDF and OWL. It employs the established semantic standards SNOMED CT and FHIR, which warrant international interoperability. A graph-based form is not only useful to feed different target visualizations, but it can also be subject to AI-powered services such as (fuzzy) retrieval and summarization.

Towards an Interoperability Landscape for a National Research Data Infrastructure for Personal Health Data

The German initiative “National Research Data Infrastructure for Personal Health Data” (NFDI4Health) focuses on research data management in health research. It aims to foster and develop harmonized informatics standards for public health, epidemiological studies, and clinical trials, facilitating access to relevant data and metadata standards. This publication lists syntactic and semantic data standards of potential use for NFDI4Health and beyond, based on interdisciplinary meetings and workshops, mappings of study questionnaires and the NFDI4Health metadata schema, and literature search. Included are 7 syntactic, 32 semantic and 9 combined syntactic and semantic standards. In addition, 101 ISO Standards from ISO/TC 215 Health Informatics and ISO/TC 276 Biotechnology could be identified as being potentially relevant. The work emphasizes the utilization of standards for epidemiological and health research data ensuring interoperability as well as the compatibility to NFDI4Health, its use cases, and to (inter-)national efforts within these sectors. The goal is to foster collaborative and inter-sectoral work in health research and initiate a debate around the potential of using common standards.

Data sharing in agricultural supply chains: Using semantics to enable sustainable food systems

The agrifood system faces a great many economic, social and environmental challenges. One of the biggest practical challenges has been to achieve greater data sharing throughout the agrifood system and the supply chain, both to inform other stakeholders about a product and equally to incentivise greater environmental sustainability. In this paper, a data sharing architecture is described built on three principles (a) reuse of existing semantic standards; (b) integration with legacy systems; and (c) a distributed architecture where stakeholders control access to their own data. The system has been developed based on the requirements of commercial users and is designed to allow queries across a federated network of agrifood stakeholders. The Ploutos semantic model is built on an integration of existing ontologies. The Ploutos architecture is built on a discovery directory and interoperability enablers, which use graph query patterns to traverse the network and collect the requisite data to be shared. The system is exemplified in the context of a pilot involving commercial stakeholders in the processed fruit sector. The data sharing approach is highly extensible with considerable potential for capturing sustainability related data.

Research on the standardization model of data semantics in the knowledge graph construction of Oil&Gas industry

At present, knowledge graphs in both general and vertical fields rarely consider the problem of data semantic standardization in the process of constructing from a global perspective, which brings trouble and loss to the sharing and interoperability of domain information. Therefore, it is very important to construct a set of standardized and semantically consistent knowledge organization model, and to propose a data semantics standardization methodology on this basis, so as to fundamentally solve this serious problem that hinders data sharing. This paper first proposes a model of data semantic standardization theoretically through the research on knowledge organization, concept triangle and concept logic and other theories, combined with ISO 1087 international standard, and proposes the important idea that the key issue of data semantic standardization is conceptual standardization as the core. Then, based on this theoretical model, a comprehensive study and combing of various vocabulary standards in the international Oil&Gas industry was carried out, and the Oil&Gas industry reference vocabulary standardization model (OIRVSM) used to guide the construction of data semantic standards in the oil and gas industry is proposed. Finally, under the guidance of the W3C best practice framework, combined with the proposed model, an operational algorithm for constructing standardized knowledge graph (ACSKG) in Oil&Gas industry and a construction example are given, which verifies the rationality and correctness of data semantic standardization model and vocabulary standard system model. The two models proposed in this paper are innovative. Although they are proposed in the context of Oil&Gas industry, they also have application and reference significance in other fields.

Effectiveness of Telemedicine and Teledentistry after the COVID-19 Pandemic.

Telemedicine has become increasingly important worldwide over the last two decades. As a new field, it became known especially during the COVID-19 pandemic; this review presents fields of activity with special attention to opportunities and risks. Numerous areas of application offer the possibility for broad use in the medical and dental care landscape in diagnostics, therapy, rehabilitation, and decision advice across a spatial distance. Technical and semantic standards are required, and profiles and guidelines are increasingly defined and organized. Medical/dental consultations have been established in various regions around the world as a response to pandemic challenges and have made video and online emergency consultations possible. Telemedicine applications are already regularly used in medical/dental emergencies, regardless of the pandemic situation, both for transport by train and by plane, from which patients benefit. However, legal hurdles are often still unresolved, but infrastructure challenges both for provider, user hard- and software also complicate deployment. Problems are particularly prevalent in the absence of necessary internet coverage or among socially disadvantaged and vulnerable groups who cannot afford expensive equipment or do not know how to use the technology. Broad access must be enabled, and hardware and software interfaces and updates must be regularly checked and updated. Telemedicine might also improve access to and delivery of oral and general health care support both for rural and urban areas with low costs. Even though dentistry and many medical specialties are still performed clinically by means of practical/manual examination, there are areas of diagnostics where telemedicine applications can provide good support. Therefore, as conclusions, access, and delivery of telemedicine applications in dentistry and medicine should be expanded and improved to provide access to all population groups.

Supporting Learning Mobility with Student Data Harmonisation - A European Perspective

Digitalisation promotes online education, internationalisation and student mobility. Based on the Bologna process and the European higher education area, learning mobility has been successful under Erasmus and other similar initiatives. However, a key issue for students and universities is that a significant amount of time is spent on the manual labour involved in the process of applying to degree programs overseas. It is therefore essential for higher education institutions to better exploit the potential of technology and Web 2.0 to enable a secure exchange of evidence during application for degree programs and academic courses in foreign Higher Education Institutions, as well as applying for study grants and obtaining recognition for academic and other types of studies. Harmonisation of the student data is a key initial step for enabling such exchange. In this study, an approach to a secured exchange of education evidence is instrumented under the H2020 project Digital Europe for All (DE4A). Existing semantic standards for Web 2.0 applications, core vocabularies for public service data and semantic assets from existing best practices such as W3C, ISA2 core vocabularies, and Europass data model are used to curate data models that allow the exchange of a higher education diploma, secondary education diploma and information of special needs (disability, large family), which is required by students when requesting study grants (waive of tuition fees). The semantic interoperability agreements are established cross-border through these data models called canonical evidences. The canonical evidences are tested with the national data services of three countries, Portugal, Slovenia, and Spain. The final data models are implemented in XML Schema format that could be used by any educational organisation intending to use trusted public service databases within Europe to automatically retrieve information on students’ degrees. The validity of the canonical evidences is tested on two pilot occasions within the DE4A project. The outcome of this study summarises the procedural requirements for evidences when applying for a higher degree program and seeking grants. Furthermore, it resulted in verified canonical evidence data models that fulfil the procedural requirements for applying for studying abroad.

FAIR Digital Objects in Official Statistics

FAIR Digital Objects in Official Statistics

Assessing Progress Toward the Vision of a Comprehensive, Shared Electronic Care Plan: Scoping Review.

BackgroundCare plans are central to effective care delivery for people with multiple chronic conditions. But existing care plans—which typically are difficult to share across care settings and care team members—poorly serve people with multiple chronic conditions, who often receive care from numerous clinicians in multiple care settings. Comprehensive, shared electronic care (e-care) plans are dynamic electronic tools that facilitate care coordination and address the totality of health and social needs across care contexts. They have emerged as a potential way to improve care for individuals with multiple chronic conditions.ObjectiveTo review the landscape of e-care plans and care plan–related initiatives that could allow the creation of a comprehensive, shared e-care plan and inform a joint initiative by the National Institutes of Health and the Agency for Healthcare Research and Quality to develop e-care planning tools for people with multiple chronic conditions.MethodsWe conducted a scoping review, searching literature from 2015 to June 2020 using Scopus, Clinical Key, and PubMed; we also searched the gray literature. To identify initiatives potentially missing from this search, we interviewed expert informants. Relevant data were then identified and extracted in a structured format for data synthesis and analysis using an expanded typology of care plans adapted to our study context. The extracted data included (1) the perspective of the initiatives; (2) their scope, (3) network, and (4) context; (5) their use of open syntax standards; and (6) their use of open semantic standards.ResultsWe identified 7 projects for e-care plans and 3 projects for health care data standards. Each project provided critical infrastructure that could be leveraged to promote the vision of a comprehensive, shared e-care plan. All the e-care plan projects supported both broad goals and specific behaviors; 1 project supported a network of professionals across clinical, community, and home-based networks; 4 projects included social determinants of health. Most projects specified an open syntax standard, but only 3 specified open semantic standards.ConclusionsA comprehensive, shared, interoperable e-care plan has the potential to greatly improve the coordination of care for individuals with multiple chronic conditions across multiple care settings. The need for such a plan is heightened in the wake of the ongoing COVID-19 pandemic. While none of the existing care plan projects meet all the criteria for an optimal e-care plan, they all provide critical infrastructure that can be leveraged as we advance toward the vision of a comprehensive, shared e-care plan. However, critical gaps must be addressed in order to achieve this vision.

A Four-Valued Logical Framework for Reasoning About Fiction

In view of the limitations of classical, free, and modal logics to deal with fictional names, we develop in this paper a four-valued logical framework that we see as a promising strategy for modeling contexts of reasoning in which those names occur. Specifically, we propose to evaluate statements in terms of factual and fictional truth values in such a way that, say, declaring ‘Socrates is a man’ to be true does not come down to the same thing as declaring ‘Sherlock Holmes is a man’ to be so. As a result, our framework is capable of representing reasoning involving fictional characters that avoids evaluating statements according to the same semantic standards. The framework encompasses two logics that differ according to alternative ways one may interpret the relationships among the factual and fictional truth values.

Hierarchy in Knowledge Systems

Hierarchies abound to help us organize our world. A hierarchy places items into a general order, where more ‘general’ is also more ‘abstract’. The etymology of hierarchy is grounded in notions of religious and social rank. This article, after a historical review, focuses on knowledge systems, an interloper of the term hierarchy since at least the 1800s. Hierarchies in knowledge systems include taxonomies, classification systems, or thesauri in information science, and systems for representing information and knowledge to computers, notably ontologies and knowledge representation languages. Hierarchies are the logical underpinning of inference and reasoning in these systems, as well as the scaffolding for classification and inheritance. Hierarchies in knowledge systems express subsumption relations that have flexible variants, which we can represent algorithmically, and thus computationally. This article dissects that variability, leading to a proposed typology of hierarchies useful to knowledge systems. The article argues through a perspective informed by Charles Peirce that natural hierarchies are real, can be logically determined, and are the appropriate basis for knowledge systems. Description logics and semantic language standards reflect this perspective, importantly through their open-world logic and vocabularies for generalized subsumption hierarchies. Recent research suggests possible mechanisms for the emergence of natural hierarchies.

Industry Commons: an ecosystem approach to horizontal enablers for sustainable cross-domain industrial innovation (a positioning paper)

This paper introduces the background, concept and definition of the Industry Commons. It initiates a discussion on the positioning of the Industry Commons Ecosystem (ICE) with respect to current research directions in advanced manufacturing and production systems that shape advances in engineering and technology, novel business models and innovation breakthroughs. The potential value of data sharing across industrial domains is estimated at over $100 billion, particularly in view of optimising manufacturing processes. Data sharing across domains however faces a series of well-documented challenges associated with the lack of semantic interoperability and related standards, management of trust and sustainability. Solving bottlenecks in data sharing requires a systemic approach to data management, which can account for all aspects of data use, levels of application, attribution and dynamic exchanges. In this paper we propose a high-level ecosystem approach that integrates societal values with digital affordances of industry’s cognitive-assisted processes, remote interfacing, hybrid applications and large-scale value networks. Early development of an Ontology Commons EcoSystem (OCES) is presented as the key enabling framework for Industry Commons interoperability and a series of enabling frameworks form the basis of future research directions in Trusted Data Sharing and Closed-Loop Lifecycle Management for greater sustainability. Abbreviations: AI – Artificial Intelligence; AIOTI – Alliance of Internet-of-Things Innovation; ALM – Asset Lifecycle Management; ALO – Application-Level Ontology; AP – Application Protocol; API – Application Programming Interface; B2B – Business-to-Business; B2C – Business-to-Customer; CDE – Cross-Domain Ecosystem; CDEI – Cross-Domain Ecosystem Interoperability; CL2M – Closed-Loop Lifecycle Management; CNO – Collaborative Networked Organisations; CPS – Cyber-Physical Systems; CSR – Corporate Social Responsibility; DLO – Domain-Level Ontology; DLT – Distributed Ledger Technology; EM – Enterprise Modelling; FAIR – Findable, Accessible, Interoperable and Reusable; GUI – Graphical User Interface; ICE, Industry Commons Ecosystem; IOF – Industrial Ontology Foundry; IP – Intellectual Property; IPR – Intellectual Property Rights; ISN – Intertwined Supply Network; MIR – Music Information Retrieval; MLO – Middle-Level Ontology; MO – Meta-Ontology; OCES – Ontology Commons EcoSystem; PI – Physical Internet; PLM – Product Lifecycle Management; ROI – Return-on-Investment; SC – Supply Chain; SCM – Supply Chain Management; SOS – System-of-Systems; TDS – Trusted Data Sharing; TLO – Top-Level Ontology; TRO – Top Reference Ontology; TUI – Tangible User Interface.

AgroFIMS: A Tool to Enable Digital Collection of Standards-Compliant FAIR Data

Agricultural research has been traditionally driven by linear approaches dictated by hypothesis-testing. With the advent of powerful data science capabilities, predictive, empirical approaches are possible that operate over large data pools to discern patterns. Such data pools need to contain well-described, machine-interpretable, and openly available data (represented by high-scoring Findable, Accessible, Interoperable, and Reusable—or FAIR—resources). CGIAR's Platform for Big Data in Agriculture has developed several solutions to help researchers generate open and FAIR outputs, determine their FAIRness in quantitative terms1, and to create high-value data products drawing on these outputs. By accelerating the speed and efficiency of research, these approaches facilitate innovation, allowing the agricultural sector to respond agilely to farmer challenges. In this paper, we describe the Agronomy Field Information Management System or AgroFIMS, a web-based, open-source tool that helps generate data that is “born FAIRer” by addressing data interoperability to enable aggregation and easier value derivation from data. Although license choice to determine accessibility is at the discretion of the user, AgroFIMS provides consistent and rich metadata helping users more easily comply with institutional, founder and publisher FAIR mandates. The tool enables the creation of fieldbooks through a user-friendly interface that allows the entry of metadata tied to the Dublin Core standard schema, and trial details via picklists or autocomplete that are based on semantic standards like the Agronomy Ontology (AgrO). Choices are organized by field operations or measurements of relevance to an agronomist, with specific terms drawn from ontologies. Once the user has stepped through required fields and desired modules to describe their trial management practices and measurement parameters, they can download the fieldbook to use as a standalone Excel-driven file, or employ via free Android-based KDSmart, Fieldbook, or ODK applications for digital data collection. Collected data can be imported back to AgroFIMS for statistical analysis and reports. Development plans for 2021 include new features such ability to clone fieldbooks and the creation of agronomic questionnaires. AgroFIMS will also allow archiving of FAIR data after collection and analysis from a database and to repository platforms for wider sharing.

Expressiveness of an International Semantic Standard for Wound Care: Mapping a Standardized Item Set for Leg Ulcers to the Systematized Nomenclature of Medicine-Clinical Terms.

BackgroundChronic health conditions are on the rise and are putting high economic pressure on health systems, as they require well-coordinated prevention and treatment. Among chronic conditions, chronic wounds such as cardiovascular leg ulcers have a high prevalence. Their treatment is highly interdisciplinary and regularly spans multiple care settings and organizations; this places particularly high demands on interoperable information exchange that can be achieved using international semantic standards, such as Systematized Nomenclature of Medicine–Clinical Terms (SNOMED CT).ObjectiveThis study aims to investigate the expressiveness of SNOMED CT in the domain of wound care, and thereby its clinical usefulness and the potential need for extensions.MethodsA clinically consented and profession-independent wound care item set, the German National Consensus for the Documentation of Leg Wounds (NKDUC), was mapped onto the precoordinated concepts of the international reference terminology SNOMED CT. Before the mapping took place, the NKDUC was transformed into an information model that served to systematically identify relevant items. The mapping process was carried out in accordance with the ISO/TR 12300 formalism. As a result, the reliability, equivalence, and coverage rate were determined for all NKDUC items and sections.ResultsThe developed information model revealed 268 items to be mapped. Conducted by 3 health care professionals, the mapping resulted in moderate reliability (κ=0.512). Regarding the two best equivalence categories (symmetrical equivalence of meaning), the coverage rate of SNOMED CT was 67.2% (180/268) overall and 64.3% (108/168) specifically for wounds. The sections general medical condition (55/66, 83%), wound assessment (18/24, 75%), and wound status (37/57, 65%), showed higher coverage rates compared with the sections therapy (45/73, 62%), wound diagnostics (8/14, 57%), and patient demographics (17/34, 50%).ConclusionsThe results yielded acceptable reliability values for the mapping procedure. The overall coverage rate shows that two-thirds of the items could be mapped symmetrically, which is a substantial portion of the source item set. Some wound care sections, such as general medical conditions and wound assessment, were covered better than other sections (wound status, diagnostics, and therapy). These deficiencies can be mitigated either by postcoordination or by the inclusion of new concepts in SNOMED CT. This study contributes to pushing interoperability in the domain of wound care, thereby responding to the high demand for information exchange in this field. Overall, this study adds another puzzle piece to the general knowledge about SNOMED CT in terms of its clinical usefulness and its need for further extensions.

Much native data, little longitudinal information: a model of information for the continuity of care, from prenatal assistance to the emergency in maternities / Muitos dados nativos, pouca informação longitudinal: um modelo de informação para a continuidade do atendimento, desde a assistência pré-natal até a emergência em maternidades

Background : Information systems allow for the availability of clinical records in computer databases. Obstetric caring demands a continuous process of information sharing between health professionals. However, the lack of communication between points of assistance has allowed for an accumulation of local data without the benefits of data interoperability. Objective : The study’s objective is to develop an information model with essential obstetric data to reuse information from primary sources through to tertiary level of care, and to foster the continuity of information. Methods : An exploratory research involved focus group discussions with obstetricians as domain specialists and computer scientists. Fictitious cases of obstetric emergencies were discussed to select meaningful information from prenatal care for the specialists’ decision making. Ninety electronic medical records (EMR) from a university obstetric emergency unit were used to validate the information model. A second-round evaluation with the obstetrician met the specialists' requirements, the information model was also finalized after adjustments to semantic standards and the structure of the clinical data. Results: The study found a recurring set of clinical information that was common in both the domain specialist requests and the EMR retrospective analysis. However, the lack of informational standards, use of acronyms and abbreviations characterized real histories in the obstetric EMR. The minimum antenatal dataset entries was structured into nine sections, and fifty-six data entries. Conclusion : The development of an information model for the continuity of care records, based on the standard of interoperability and usefulness for clinicians, has the potential to overcome the informality of EMR.

Addressing Semantics Standards for Cloud Portability and Interoperability in Multi Cloud Environment

The increasing demand for cloud computing has shifted business toward a huge demand for cloud services, which offer platform, software, and infrastructure for the day-to-day use of cloud consumers. Numerous new cloud service providers have been introduced to the market with unique features that assist service developers collaborate and migrate services among multiple cloud service providers to address the varying requirements of cloud consumers. Many interfaces and proprietary application programming interfaces (API) are available for migration and collaboration services among cloud providers, but lack standardization efforts. The target of the research work was to summarize the issues involved in semantic cloud portability and interoperability in the multi-cloud environment and define the standardization effort imminently needed for migrating and collaborating services in the multi-cloud environment.

Formalization and Semantic Integration of Heterogeneous Omics Annotations for Exploratory Searches

Aim: To facilitate researchers and practitioners for unveiling the mysterious functional aspects of human cellular system through performing exploratory searching on semantically integrated heterogeneous and geographically dispersed omics annotations. Background: Improving health standards of life is one of the motives which continuously instigates researchers and practitioners to strive for uncovering the mysterious aspects of human cellular system. Inferring new knowledge from known facts always requires reasonably large amount of data in well-structured, integrated and unified form. Due to the advent of especially high throughput and sensor technologies, biological data is growing heterogeneously and geographically at astronomical rate. Several data integration systems have been deployed to cope with the issues of data heterogeneity and global dispersion. Systems based on semantic data integration models are more flexible and expandable than syntax-based ones but still lack aspect-based data integration, persistence and querying. Furthermore, these systems do not fully support to warehouse biological entities in the form of semantic associations as naturally possessed by the human cell. Objective: To develop aspect-oriented formal data integration model for semantically integrating heterogeneous and geographically dispersed omics annotations for providing exploratory querying on integrated data. Method: We propose an aspect-oriented formal data integration model which uses web semantics standards to formally specify its each construct. Proposed model supports aspect-oriented representation of biological entities while addressing the issues of data heterogeneity and global dispersion. It associates and warehouses biological entities in the way they relate with Result: To show the significance of proposed model, we developed a data warehouse and information retrieval system based on proposed model compliant multi-layered and multi-modular software architecture. Results show that our model supports well for gathering, associating, integrating, persisting and querying each entity with respect to its all possible aspects within or across the various associated omics layers. Conclusion: Formal specifications better facilitate for addressing data integration issues by providing formal means for understanding omics data based on meaning instead of syntax

Transforming the study of organisms: Phenomic data models and knowledge bases.

The rapidly decreasing cost of gene sequencing has resulted in a deluge of genomic data from across the tree of life; however, outside a few model organism databases, genomic data are limited in their scientific impact because they are not accompanied by computable phenomic data. The majority of phenomic data are contained in countless small, heterogeneous phenotypic data sets that are very difficult or impossible to integrate at scale because of variable formats, lack of digitization, and linguistic problems. One powerful solution is to represent phenotypic data using data models with precise, computable semantics, but adoption of semantic standards for representing phenotypic data has been slow, especially in biodiversity and ecology. Some phenotypic and trait data are available in a semantic language from knowledge bases, but these are often not interoperable. In this review, we will compare and contrast existing ontology and data models, focusing on nonhuman phenotypes and traits. We discuss barriers to integration of phenotypic data and make recommendations for developing an operationally useful, semantically interoperable phenotypic data ecosystem.