Ontology For Interoperability Research Articles

GeoFault: A well-founded fault ontology for interoperability in geological modeling

Geological modeling currently uses various computer-based applications. Data harmonization at the semantic level using ontologies is essential to make these applications interoperable. Since geo-modeling is part of several multidisciplinary projects, interoperability requires semantic harmonization to exchange information between geological applications and integrate other domain knowledge at a general level. Therefore, domain ontologies that describe geological knowledge must be based on a sound ontological background to ensure this knowledge is integrable. Faults are essential for understanding and solving structural problems but are complex to model because the concept of fault includes a group of geological entities with a distinct ontological nature. A fault can correspond to thin, deformed rock volumes or spatial arrangements resulting from the displacement of geological blocks, but at a broader scale, geologists describe faults as surfaces or components of complex fault arrays. Our work intends to harmonize these views by presenting a domain ontology, GeoFault, resting on the Basic Formal Ontology (BFO) and the GeoCore ontology. GeoCore and GeoFault support the parametric description of geological sites as a preliminary step for quantitative and qualitative analysis. We have proposed GeoFault after systematically revising the literature and several knowledge-acquisition sessions with expert structural geologists. The ontology formalizes a vocabulary for fault “sensu stricto,” excluding ductile shear deformations. It covers the regional to outcrop scales, excluding structures at the microscopic, orogenic, and tectonics scales, and it avoids interpretive language associated with geological processes as far as possible. Extending the BFO and GeoCore ontologies allows the fault concept to be related to formal ontological classes in a consistent semantic-rich framework. The ontology artifact is implemented in OWL 2, validated by competency questions with two use cases, and tested using an in-house ontology-driven data entry application. The GeoFault ontology is publicly available and provides a solid framework for clarifying fault knowledge and a foundation for many applications.

A new framework for host-pathogen interaction research.

COVID-19 often manifests with different outcomes in different patients, highlighting the complexity of the host-pathogen interactions involved in manifestations of the disease at the molecular and cellular levels. In this paper, we propose a set of postulates and a framework for systematically understanding complex molecular host-pathogen interaction networks. Specifically, we first propose four host-pathogen interaction (HPI) postulates as the basis for understanding molecular and cellular host-pathogen interactions and their relations to disease outcomes. These four postulates cover the evolutionary dispositions involved in HPIs, the dynamic nature of HPI outcomes, roles that HPI components may occupy leading to such outcomes, and HPI checkpoints that are critical for specific disease outcomes. Based on these postulates, an HPI Postulate and Ontology (HPIPO) framework is proposed to apply interoperable ontologies to systematically model and represent various granular details and knowledge within the scope of the HPI postulates, in a way that will support AI-ready data standardization, sharing, integration, and analysis. As a demonstration, the HPI postulates and the HPIPO framework were applied to study COVID-19 with the Coronavirus Infectious Disease Ontology (CIDO), leading to a novel approach to rational design of drug/vaccine cocktails aimed at interrupting processes occurring at critical host-coronavirus interaction checkpoints. Furthermore, the host-coronavirus protein-protein interactions (PPIs) relevant to COVID-19 were predicted and evaluated based on prior knowledge of curated PPIs and domain-domain interactions, and how such studies can be further explored with the HPI postulates and the HPIPO framework is discussed.

(Digital Presentation) A Battery Interface Ontology for Data Interoperability and Semantic Knowledge Representation

Data drives battery development. Despite the unprecedented volume of data being generated across both industry and research today, the battery data landscape remains a patchwork of heterogeneous sources with inconsistent metadata needed to correctly interpret its meaning. A new approach is needed to ensure that reported battery data is interoperable, reusable, and machine-readable. Furthermore, there is a growing need to share knowledge about batteries among groups from different backgrounds and organizations (e.g., experimentalists, modellers, engineers, technicians, etc.).The Battery Interface Ontology (BattINFO) is designed to meet these needs [1]. An ontology is a data model that represents domain knowledge as a map of concepts and the relations between them. This allows data to be mapped to common vocabulary terms, such that any two pieces of data mapped to the same term are also mapped to each other. Additionally, the network of concepts and relations allow expert knowledge to be expressed as graphs in a machine-readable form, which can then be queried to identify links between individual pieces of data and quickly share knowledge across organizations.BattINFO is a free, open-source domain ontology for electrochemistry and batteries that facilitates battery data interoperability and enables machine-readable semantic knowledge representation. A conceptual overview is shown in Figure 1. BattINFO was developed within the scope of the EU H2020 project BIG-MAP to facilitate data interoperability between more than 30 battery research institutes and companies in Europe. This contribution introduces BattINFO, demonstrates specific use cases for how it can be applied to accelerate battery data handling and development, and presents plans for future development and integration into the global battery data landscape.[1] S. Clark et al., “Toward a Unified Description of Battery Data,” Adv. Energy Mater., vol. 2102702, 2021. Figure 1

Modular Ontology Design: A State-of-Art of Diseases Ontology Modeling and Possible Issue

The evolution of ontological engineering leaded authors to use some techniques of software engineering to design ontologies. Are obtained from these techniques the monolithic or modularized Ontologies. When is difficult to reuse some concepts of monolithic ontologies, modularized Ontologies facilitate ontology management, understandability and reuse. This paper aims to survey on ontology modularization techniques and their contribution in biomedical ontologies design. Modularization reposed on appropriated techniques and some challenges related to ontology reused, scalable querying, collaborative authoring, and distributed reasoning. For most of disease ontologies, more especially ontologies which reused IDO, these challenges are not considered, and most of them are implemented with OWL language and the novel mode to construct ontology’s purpose is to facilitate reuse and interoperability of ontologies ensured by modularization.

Construction safety ontology development and alignment with industry foundation classes (IFC)

A pronounced gap often exists between expected and actual safety performance in the construction industry. The multifaceted causes of this performance gap are resulting from the misalignment between design assumptions and actual construction processes that take place on-site. In general, critical factors are rooted in the lack of interoperability around the building and work-environment information due to its heterogeneous nature. To overcome the interoperability challenge in safety management, this paper represents the development of an ontological model consisting of terms and relationships between these terms, creating a conceptual information model for construction safety management and linking that ontology to IfcOWL. The developed ontology, named Safety and Health Exchange (SHE), comprises eight concepts and their relationships required to identify and manage safety risks in the design and planning stages. The main concepts of the developed ontology are identified based on reviewing accident cases from 165 Reporting of Injuries, Diseases and Dangerous Occurrences Regulations (RIDDOR) and 31 Press Releases from the database of the Health and Safety Executive (HSE) in the United Kingdom. Consequently, a semantic mapping between the developed ontology and IfcOWL (the most popular ontology and schema for interoperability in the AEC sector) is proposed. Then several SPARQL queries were developed and implemented to evaluate the semantic consistency of the developed ontology and the cross-mapping. The proposed ontology and cross-mapping gained recognition for its innovation in utilising OpenBIM and won the BuildingSMART professional research award 2020. This work could facilitate developing a knowledge-based system in the BIM environment to assist designers in addressing health and safety issues during the design and planning phases in the construction sector.

Dynamic adaption in cyber-physical production systems based on ontologies

The paradigmatic shift of production systems towards Cyber-Physical Production Systems (CPPSs) requires the development of flexible and decentralized approaches. In this way, such systems enable manufacturers to respond quickly and accurately to changing requirements. However, domain-specific applications require the use of suitable conceptualizations. The issue at hand, when using various conceptualizations is the interoperability of different ontologies. To achieve flexibility and adaptability in CPPSs though requires overcoming interoperability issues within CPPSs. This paper presents an approach to increase flexibility and adaptability in CPPSs while addressing the interoperability issue. In this work, OWL ontologies conceptualize domain knowledge. The Intelligent Manufacturing Knowledge Ontology Repository (IMKOR) connects the domain knowledge in different ontologies. Testing if adaptions in one ontology within the IMKOR provide knowledge to the whole IMKOR. The tests showed, positive results and the repository makes the knowledge available to the whole CPPS. Furthermore, an increase in flexibility and adaptability was noticed.

Industrial ontologies for interoperability in agile and resilient manufacturing

Ontologies provide an opportunity to tackle the interoperability challenge in digital manufacturing. Although ontologies have been used in numerous industrial projects, the problem of semantic interoperability in the industry is still unresolved. One reason for the lack of success of ontologies in the industry is that most industrial ontologies have been developed based on local needs without taking wider applications into account or attempting to learn from earlier ontology initiatives or reuse the existing ontologies. In this position paper, we argue that for widespread adoption and implementation of ontologies, more systematic and coordinated efforts are needed with the participation of industry, academia, and government at an international level. We also hypothesise that a coordinated and systematic ontology development effort will incrementally lead to the creation of an ecosystem of interoperable software applications that support consistent data access and reasoning across the product life cycle. We discuss why efforts such as Industrial Ontology Foundry (IOF) can promote a methodological ontology development process by introducing modular, and reusable ontologies. We draw examples from two domain areas, namely supply chain management and production planning and scheduling, to demonstrate how industrial notions can be analysed ontologically according to IOF procedures and methods.

Precision omics data integration and analysis with interoperable ontologies and their application for COVID-19 research.

Precision medicine focuses on individual-level disease treatment and prevention. Precision omics is a new strategy that applies omics for precision medicine research, which requires standardized representation and integration of individual genetics and phenotypes, experimental conditions, and data analysis settings. Ontology has emerged as an important artificial intelligence component to become critical for standard data and metadata representation, standardization and integration. To support precision omics, interoperable ontologies are required in order to standardize and incorporate heterogeneous data and knowledge in a human- and computer-interpretable manner. With the interoperable omics data and knowledge, omics tools such as OmicsViz can also be evolved to process, integrate, visualize and analyze various omics data, leading to the identification of new knowledge and hypotheses of molecular mechanisms underlying disease outcomes. The precision COVID-19 omics study is provided as the primary use case to illustrate the rationale and implementation of the precision omics strategy.

The City of Hope POSEIDON enterprise-wide platform for real-world data and evidence in cancer.

e18813 Background: The City of Hope Center for Precision Medicine developed an enterprise-wide platform and precision medicine program to unlock the research potential and clinical value of complex and unique datasets by combining patient data with comprehensive genomic profiling and proprietary analytics. POSEIDON (Precision Oncology Software Environment Interoperable Data Ontologies Network) is a secure, cloud-based Oncology Insights Engine enabling exploration, analysis, visualization, and collaboration on our patient clinico-genomic data along with public data sources. This platform enables investigators to access and visualize data from clinical and multi-omics data and provides an engine that can be utilized for cohort discovery and exploration, preliminary feasibility testing to deriving patient specific insights based on real world data (RWD) and real-world evidence (RWE). Patients are consented through an IRB-approved protocol with active, opt-in participation. Methods: The POSEIDON Common Data Model (PCDM) is a standard, extensible data schema that incorporates patient data to support Precision Medicine. Data are incorporated from disparate data sources and stored in a combined harmonized manner promoting consistency of data and meaning across downstream applications. A multi-step process was created to capture and structure multiple data types into the PCDM. Natural language processing (NLP) tools are deployed to automate and structure valuable data elements from unstructured documents including pathology reports and clinical notes. NLP augmented software tools were developed to assist manual data abstractors to capture more complex terms and disease specific data elements which can include disease progression, progression free survival, and other outcomes. Results: Comprehensive data from 175,000 City of Hope patients are included within this environment for cohort exploration, longitudinal follow-up, outcomes, hypothesis development, and queries for synthetic controls. Data from disease specific-research registries constitute a rich dataset within POSEIDON by disease and tumor type, including lung cancer, colorectal cancer, breast cancer, leukemia, lymphoma and multiple myeloma, among other disease types. Automated genomic workflows were created to gain access to genomic profiling and whole exome sequencing. Genomic data is associated with the clinical data in the PCDM. Automated data flows from the Enterprise Data Warehouse EDW include data that is captured in discrete formats in the EDW and provided for in the PCDM and further enrich the data that flows from the disease registries. Statistically rigorous methods for de-identification are applied for collaborative studies. Conclusions: The City of Hope Center for Precision Medicine and the POSEIDON platform offer an exceptional resource for collaborative RWD & RWE studies.

Knowledge Representation with Ontologies and Semantic Web Technologies to Promote Augmented and Artificial Intelligence in Systems Engineering

ABSTRACTThis article discusses knowledge representation using ontologies and semantic web technologies to enable artificial intelligence (AI) for Systems Engineering. Technology trends indicate new methods and tools for digital engineering will incorporate AI and machine learning (ML) technologies. ML techniques support classification, clustering, and association identification, but struggle to explain the rationale for decision making, where multi‐domain semantic modeling and rule‐based reasoning can excel. Knowledge representation plays a key role in applying this type of AI. Ontologies are a means to domain modeling and reasoning required across Digital Thread domains instantiated in digital system models (DSM). These evolve over time as digital twins, which co‐evolve with physical instantiations of a DSM. Semantic technologies and ontologies formalize knowledge as an enabler for reasoning, with interoperable ontologies enabling reason about systems engineering across domains.

Semantic Mapping of Component Framework Interface Ontologies for Interoperability of Vehicle Applications

Abstract Over the past few years, ontology merging, and ontology semantic alignment has gained significant interest as research topics in automotive application domain for finding solutions to semantic data heterogeneity. To accomplish the complex and novel vehicle service requirements such as autonomous driving, V2X (Vehicle-to-Vehicle communication), etc. the automotive applications involve collaborations of several platform-specific data from heterogeneous enterprises component frameworks and consequently there has been increase in data interoperability issues. At the application component level, data interoperability relies on the semantic alignment or mapping between the various component framework interfaces data models represented as XML schemas (XSD). With the XML schemas being the preferred standard for the interface description exchange between most of the automotive application domain components, however, the data interoperability between the semantically equivalent but structurally different data constructs of multiple heterogeneous XSDs stands as a challenge in the absence of an ontology-based approach. To confront this crucial requirement for data interoperability and to increase in effect the reuse of existing components through their interfaces, we propose an approach to semantically map the various component framework interface data models when expressed as ontology schemas, based on the exploration of semantic synergies. The transformation between XSD and RDF (Resource Description Framework) schema representations and the use of queries over the ontology schemas for semantic mapping are demonstrated including a real-world case study.

Interoperability of disparate engineering domain ontologies using basic formal ontology

ABSTRACTAs engineering applications require management of ever larger volumes of data, ontologies offer the potential to capture, manage, and augment data with the capability for automated reasoning and semantic querying. Unfortunately, considerable barriers hinder wider deployment of ontologies in engineering. Key among these is lack of a shared top-level ontology to unify and organise disparate aspects of the field and coordinate co-development of orthogonal ontologies. As a result, many engineering ontologies are limited to their scope, and functionally difficult to extend or interoperate with other engineering ontologies. This paper demonstrates how the use of a top-level ontology, specifically the Basic Formal Ontology (BFO), greatly facilitates interoperability of multiple engineering-related ontologies. We constructed a system of formal linked ontologies by re-engineering legacy ontologies to be conformant with BFO and developing new BFO-conformant ontologies to capture knowledge in the engineering design, enterprise, human factors, manufacturing, and application domain of additive manufacturing. The resulting Integrated Framework for Additively Manufactured Products (IFAMP), including the body knowledge instantiated on its basis, serve as the basis for a proposed Design with Additive Manufacturing Method (DWAM), which we believe can support the design of innovative products with semantically enhanced ideation tools and enhanced access to application domain knowledge. The method and its facilitation through the ontological framework are demonstrated using a case study in medicine.

웹 기반 3차원 지도 서비스의 상호운용성 및 확장을 위한 온톨로지 기반의 통합 프레임워크 설계

3차원 지도 서비스는 실세계를 동일하게 투영함에 따라 그 활용 가능성이 점점 더 증가하고 있으며, 특히 어디서나 접근 가능한 웹 기반의 3차원 지도 서비스는 현재 많은 국가와 기업에서 이미 서비스를 제공하고 있다. 현재 제공되는 HTML5/WebGL 기반의 3차원 지도 서비스의 경우 스크립트 언어의 한계, 대용량 3차원 공간정보 처리시 발생하는 병목 현상 등으로 인하여 이전 플러그-인 방법에 비해 렌더링 속도가 느리다. 또한, 3차원 공간정보는 가변성이 떨어져 3차원 지도 서비스와 새로운 도메인의 메쉬업이나 확장이 어렵다는 단점이 존재한다. 따라서 본 연구에서는 이러한 문제점을 해결하기 위하여 온톨로지 기반의 통합 프레임워크를 설계하였다. 먼저 렌더링 속도향상을 위하여 모델을 세분화한 뒤 클러스터링 기반의 균일화 작업을 통해 데이터를 구조화하였다. 이때, 구조화된 데이터의 상호운용성 및 타 도메인으로의 확장 가능성을 확보하기 위하여 온톨로지를 활용하였다. 이는 웹 기반의 3차원 지도 서비스의 속도 개선 및 확장 가능성 확보가 가능하여 서비스 유용성 향상에 기여할 것으로 예상한다.

Aligning large biomedical ontologies for semantic interoperability using graph partitioning

Ontologies formal specifications of domain knowledge play an imperative role in the semantic web and are developed by several domain experts in the biomedical field. Ontology alignment or mapping is the process of identifying correspondences among the concepts in the ontology to facilitate data integration between heterogeneous data sources. In particular, the proposed ontology mapping system addresses two pivotal issues: 1) to facilitate the automated alignment process by incorporating random forests (RF), an ensemble learning system that is stable for outliers; 2) to improve the execution time by partitioning the ontologies using cluster-walktrap methodology and identify the correspondence between the concepts in parallel. The performance of the system is pragmatically evaluated on benchmark datasets in the anatomy and large biomedical ontology tracks of the OAEI 2013 and 2014. With the aid of the proposed system, quantifiable improvement is noticed to an extent of 4.4% in average precision, recall and F-measure.

Aligning large biomedical ontologies for semantic interoperability using graph partitioning

Ontologies formal specifications of domain knowledge play an imperative role in the semantic web and are developed by several domain experts in the biomedical field. Ontology alignment or mapping is the process of identifying correspondences among the concepts in the ontology to facilitate data integration between heterogeneous data sources. In particular, the proposed ontology mapping system addresses two pivotal issues: 1) to facilitate the automated alignment process by incorporating random forests (RF), an ensemble learning system that is stable for outliers; 2) to improve the execution time by partitioning the ontologies using cluster-walktrap methodology and identify the correspondence between the concepts in parallel. The performance of the system is pragmatically evaluated on benchmark datasets in the anatomy and large biomedical ontology tracks of the OAEI 2013 and 2014. With the aid of the proposed system, quantifiable improvement is noticed to an extent of 4.4% in average precision, recall and F-measure.

Iberian electricity market ontology to enable smart grid market simulation

Several approaches have been proposed to enhance the potential of distributed generation (DG). Some of the most prominent solutions include the aggregation of DG units and other players, culminating in the concept of and Smart Grid (SG). In this context, several simulation tools arose to study and test the new market mechanisms. However, all of these simulators are closed and centred in their object of study, neglecting the potential advantages of interoperating with other systems from the same domain. This work proposes the use of ontologies for systems interoperability in the power and energy systems domain. The ontologies have been developed and implemented in MASCEM and MASGriP - multi-agent simulators of electricity markets, and SG operation and management,respectively; thus enabling joint electricity market and SG simulations.

Late Breaking Abstracts.

Late Breaking Abstracts.

Improving the interoperability of biomedical ontologies with compound alignments

BackgroundOntologies are commonly used to annotate and help process life sciences data. Although their original goal is to facilitate integration and interoperability among heterogeneous data sources, when these sources are annotated with distinct ontologies, bridging this gap can be challenging. In the last decade, ontology matching systems have been evolving and are now capable of producing high-quality mappings for life sciences ontologies, usually limited to the equivalence between two ontologies. However, life sciences research is becoming increasingly transdisciplinary and integrative, fostering the need to develop matching strategies that are able to handle multiple ontologies and more complex relations between their concepts.ResultsWe have developed ontology matching algorithms that are able to find compound mappings between multiple biomedical ontologies, in the form of ternary mappings, finding for instance that “aortic valve stenosis”(HP:0001650) is equivalent to the intersection between “aortic valve”(FMA:7236) and “constricted” (PATO:0001847). The algorithms take advantage of search space filtering based on partial mappings between ontology pairs, to be able to handle the increased computational demands. The evaluation of the algorithms has shown that they are able to produce meaningful results, with precision in the range of 60-92% for new mappings. The algorithms were also applied to the potential extension of logical definitions of the OBO and the matching of several plant-related ontologies.ConclusionsThis work is a first step towards finding more complex relations between multiple ontologies. The evaluation shows that the results produced are significant and that the algorithms could satisfy specific integration needs.

The Application of Reference Ontologies for Semantic Interoperability in an Integrated Product Development Process in Smart Factories

Efficient information sharing demands formal information structures in order to make sure the semantic interoperability across different phases of Product Development Process. This research presents an application of reference ontologies to support semantic interoperability during the Product Development Process. The concepts inherent to the product development process are formalized in reference ontologies and these ontologies are specialized according to the specific product data, respecting semantic rules. As a result, the potential benefits and limitations of the ontology-driven interoperability were discussed, contributing to the semantic interoperability field and improving the product and manufacturing design.

Potential of ontology for interoperability in e-government

Interoperability is one of the fundamental requirements to enable electronic government. Its implementation can be classified into technical, syntactic, semantic, and organizational levels. At the semantic level, ontology is regarded as a practical solution to be considered. In this context, its adoption was identified in several countries, with different levels of maturity and so many focuses as the specific implementations. One of the main challenges to be overcome is the legal question that refers to the legislation to assure “the preservation of the legal meaning of data”. The lack of efficient mechanisms to support the deployment and use of ontologies can turn the overall task time-expensive, restricted in scope, or even unfeasible. Additionally, many initiatives are recent and need to be validated over time. This paper presents a non-exhaustive survey of the state of interoperability in e-government from the perspective of ontologies' use. The cases of Palestine, European Union, Netherlands, Estonia, and Brazil are discussed.