Showing papers on "Ontology-based data integration published in 2023"

Interpretable ontology extension in chemistry

Abstract: Reference ontologies provide a shared vocabulary and knowledge resource for their domain. Manual construction and annotation enables them to maintain high quality, allowing them to be widely accepted across their community. However, the manual ontology development process does not scale for large domains. We present a new methodology for automatic ontology extension for domains in which the ontology classes have associated graph-structured annotations, and apply it to the ChEBI ontology, a prominent reference ontology for life sciences chemistry. We train Transformer-based deep learning models on the leaf node structures from the ChEBI ontology and the classes to which they belong. The models are then able to automatically classify previously unseen chemical structures, resulting in automated ontology extension. The proposed models achieved an overall F1 scores of 0.80 and above, improvements of at least 6 percentage points over our previous results on the same dataset. In addition, the models are interpretable: we illustrate that visualizing the model’s attention weights can help to explain the results by providing insight into how the model made its decisions. We also analyse the performance for molecules that have not been part of the ontology and evaluate the logical correctness of the resulting extension.

Ontology Based Access Control: A Case Study through Ontology Based Data Access

Abstract: Ontology Based Data Access (OBDA) is the provision of data access and data integration as a result of the mapping that is established between an ontology and a data source. Thus, storing large amounts of data becomes easier, more powerful queries can be written, and management of complex information systems can be performed quickly and effectively by using Semantic Web technologies. Ontology Based Access Control (OBAC) uses Semantic Web technologies to enable the enforcement of access control mechanism. Therefore, only authorized persons can access data to protect data privacy. In this study, OBDA and OBAC are integrated to improve security while providing data virtualization with a data model-independent access control approach. Therefore, a use case study for the healthcare domain is presented. Hence, a relational database for the hospital domain, a Hospital Ontology for the related hospital database and an access control policy are created. Also, the relevant mappings between the hospital database and the Hospital Ontology are established by using the Ontop framework and finally, various queries are executed by using Ontop SPARQL to evaluate mappings and access rules.

Knowledge Representation and Information Retrieval from Ontologies

Abstract: The Ontology is an emerging domain that assists is intelligent decision making and connects various users, thereby facilitating a way of presenting the information on a common platform that could be used by the users for decision making. The Ontology leads to structuring of the unstructured data and retrieval of information from the system. The Ontology allows to share and reuse the data and its related concepts in a homogenized manner so as to provide a single nomenclature to the unstructured data that has been gathered from different sources and paving a way for elucidating the identical data easily. As the times are progressing to the fourth business revolution, absolute use of the artificial intelligence authorized ontologies leads to the decision support in many real-time applications. There are many methodologies by which an Ontology can be designed and the data can be structured and retrieved from the Ontology. This research paper aims to present a survey of the existing literature present in the domain of Ontology that would help in understanding the knowledge representation and information retrieval from the Ontologies created using various techniques.

Towards clearer recognition and easier usefulness: development of a cross-lingual atherosclerotic cerebrovascular disease Ontology (Preprint)

Abstract: Atherosclerotic cerebrovascular disease could result in a great number of deaths and disabilities. However, it did not acquire enough attention. Up till now, less information, statistics, or clear consensus on the disease was revealed. Thus, no systematic concept datasets were released to help clinicians in the field to clarify the scope, assist research, and offer maximized value. The aims of this study were to (1) develop a comprehensive cross-lingual atherosclerotic cerebrovascular disease ontology. (2) describe the workflow, schema, and hierarchical structure, and the highlighted content of the ontology (3) design a brand-new rehabilitation ontology which was an important part overlooked in the existing ontologies (4) implement the evaluation of the proposed ontology (5) apply the proposed ontology to real-world scenarios and electronic health records to realize information retrieval, named entity recognition, novel expression discovery, and knowledge fusion. We implemented 9 steps based on the ontology development 101 methodologies combined with expert opinions. The final ontology included clinical requirements collection and specification, background investigation and knowledge acquisition, ontology selection and reuse, scope identification, schema definition, concept extraction, concept extension, ontology verification, and ontology evaluation. The current ontology included 10 top-level classes, respectively clinical manifestation, comorbidity, complication, diagnosis, model of atherosclerotic cerebrovascular disease, pathogenesis, prevention, rehabilitation, risk factor, and treatment. Totally, there are 1715 concepts in the 11-level ontology, covering 4588 Chinese terms, 6617 English terms, and 972 definitions. The ontology could be applied in real-world scenarios such as information retrieval, new expression discovery, named entity recognition, and knowledge fusion, and the use case proved that it could offer satisfying support to related medical scenarios. The proposed ontology provided a clear set of cross-lingual concepts and terms with an explicit hierarchical structure, helping scientific researchers to quickly retrieve relevant medical literature, assisting data scientists to efficiently identify relevant contents in electronic health records, and providing a clear domain framework for academic reference.

Research on semantic-based data integration

Abstract: With the development of information technology, "information silo" problem is getting serious, which hinders the exchange of information between enterprises. Data integration is the key technology to solve this problem, but traditional data integration methods have problems such as weak scalability and differences in language representation. In this paper, a method is proposed to complete data integration by establishing ontology, data mapping and semantic inference, which effectively solves the differences in data structure, data source and language representation and has strong scalability. In order to verify the feasibility of the method, experiments are conducted in this study with data describing people, and the completion of ontology construction, data mapping and semantic reasoning illustrates that the experiments achieve the expected results. Finally, the ontology model of nucleic acid detection is constructed in this paper, and the integration of nucleic acid detection data is completed to verify the feasibility of engineering the method.

OntoTrek: 3D visualization of application ontology class hierarchies

Abstract: An application ontology often reuses terms from other related, compatible ontologies. The extent of this interconnectedness is not readily apparent when browsing through larger textual presentations of term class hierarchies, be it Manchester text format OWL files or within an ontology editor like Protege. Users must either note ontology sources in term identifiers, or look at ontology import file term origins. Diagrammatically, this same information may be easier to perceive in 2 dimensional network or hierarchical graphs that visually code ontology term origins. However, humans, having stereoscopic vision and navigational acuity around colored and textured shapes, should benefit even more from a coherent 3-dimensional interactive visualization of ontology that takes advantage of perspective to offer both foreground focus on content and a stable background context. We present OntoTrek, a 3D ontology visualizer that enables ontology stakeholders—students, software developers, curation teams, and funders—to recognize the presence of imported terms and their domains, ultimately illustrating how projects can capture knowledge through a vocabulary of interwoven community-supported ontology resources.

A Photovoltaic System Model Integrating FAIR Digital Objects and Ontologies

Abstract: Smart grids of the future will create and provide huge data volumes, which are subject to FAIR (Findable, Accessible, Interoperable, and Reusable) data management solutions when used within the scientific domain and for operation. FAIR Digital Objects (FDOs) provide access to (meta)data, and ontologies explicitly describe metadata as well as application data objects and domains. The present paper proposes a novel approach to integrate FAIR digital objects and ontologies as metadata models in order to support data access for energy researchers, energy research applications, operational applications and energy information systems. As the first example domain to be modeled using an ontology and to get integrated with FAIR digital objects, a photovoltaic (PV) system model is selected. For the given purpose, a discussion of existing energy ontologies shows the necessity to develop a new PV ontology. By integration of FDOs, this new PV ontology is introduced in the present paper. Furthermore, the concept of FDOs is integrated with the PV ontology in such a way that it allows for generalization. By this, the present paper contributes to a sustainable data management for smart grid operation, especially for interoperability, by using ontologies and, hence, unambiguous semantics. An information system application that visualizes the PV system, its describing data and collected sensor data, is proposed. As a proof of concept the details of the use case implementation are presented.

Toward semantic standard and process ontology for Additive manufacturing

Abstract: Abstract Advanced modelling of additive manufacturing often requires the combination of models at multiple scales and multi-physics. Therefore, building the modelling workflow describing the process is complicated. The modelling is also only a part of the innovation process and must be connected to practices, experimental work, and characterisation. Efficient communication and data exchange between the different actors could quickly become a challenge. Recent developments in the frame of the EMMC (European Material Modelling Council) and in the EU project OntoTrans points toward the early integration of semantic description and the creation of dedicated domain ontologies. This require an unambiguous and consistent use of terms and definitions for various concepts within each field of technology, and international standards is an available source for structured technical terms and definitions. For additive manufacturing (AM) the international standard ISO/ASTM 52900 “Additive manufacturing - General principles - Fundamentals and vocabulary” is the internationally recognised source for terms and definitions. Basing the ontology on the AM terminology standard will greatly facilitate integration of AM processes as a part of an industrial manufacturing system. Therefore, the present work attempts to harmonise the standard terminology and the ontology concepts. Then, to improve the impact and connection to material science, the concepts will be connected to a microstructure domain ontology and to the top- and middle-level ontology EMMO. The conceptualisation and application of the ontologies will be illustrated through simple examples of process and material modelling.

Ontology Based Agriculture Data Mining using IWO and RNN

Abstract: An ontology is a machine-interpretable formal description of domain knowledge. In current years, ontologies have risen to prominence as a key tool for demonstrating domain knowledge and a key element of several knowledge management systems, decision-support systems (DSS) and other intelligent systems including in agriculture. However, a study of the current literature on agricultural ontologies suggests that the majority of research that suggest agricultural ontologies lack a clear assessment mechanism. This is unwanted because this is impossible to assess the value of ontologies in research and practise without well-structured assessment mechanisms. Furthermore, relying on such ontologies and sharing them on the Semantic Web or amongst semantic-aware apps is problematic. This paper presents a framework for selecting appropriate assessment techniques for Ontology Based Agriculture Data Mining utilizing Invasive Weed Optimization (IWO) and Re-current Neural Network (RNN) that appears to be absent from most recent agricultural ontology research. The framework facilitates the selection of relevant evaluation techniques for a particular ontology based on its intended user.

Ontology Partitioning for Managing Change Effects

Abstract: Ontology evolution refers to the process of an ontology modification in response to change in the domain or its conceptualization. It takes in several phases which the most important are changes representation, semantics of changes, propagation and validation. Analysis of changes to be made in ontology is necessary to identify potential consequences on ontology and on dependent objects. Indeed, a modification of an ontological entity can generate impacts making the system inconsistent. Thus, impacts propagation is important to keep the system stable. However, managing changes and their effects is not a simple task; it is more difficult if concerned ontology is voluminous. We propose an approach to partition large ontology for change effects managing. Our proposed approach consists of creating a weighted dependency graph from ontology structure and then determining communities using the Island Line algorithm.

An Ontology-Driven Approach to the Analytical Platform Development for Data-Intensive Domains

Abstract: The development and support of knowledge-based systems for experts in the field of social network analysis is complicated by the problems of viability maintenance inevitably emerging in data intensive domains. Largely this is the case due to the properties of semistructured objects and processes that are analyzed by data specialists using data mining techniques and other automated analytical tools. In order to be viable a modern knowledge-based analytical platform should be able to integrate heterogeneous information, present it to users in an understandable way and support tools for functionality extensibility. In this paper we introduce an ontological approach to analytical platform development. Common requirements for analytical platform have been identified and substantiated. Theoretical basis of the proposed approach is described. General structure of the knowledge base is designed. The core of the platform is the multifaceted ontology including data ontologies describing data sources and data types and structures, problem ontologies describing specific user’s tasks, domain ontologies. Ontology-based domain-specific modeling tools are the part of analytical platform software too. The information integration method, design patterns for developing analytical platform core functionality such as ontology repository management, domain-specific languages generation and source code round-trip synchronization with DSL-models are proposed. Diagrams and schemes are included to paper to illustrate approach description.

Design and development of dynamic Agri‐ontology for IoT interoperability

Abstract: In recent years, the usage and applications of Internet of Things (IoT) have increased exponentially. IoT connects multiple heterogeneous devices like sensors, micro controllers, actuators, smart devices like mobiles, watches, etc. IoT contributes the data produced in the context of data collection, including the domains like military, agriculture, healthcare, etc. The diversity of possible applications at the intersection of the IoT and the web semantics has prompted many research teams to work at the interface between these two disciplines. This makes it possible to collect data and control various objects in transparent way. The challenge lies in the use of this data. Ontologies address this challenge to meet specific data needs in the IoT field. This paper presents the implementation of a dynamic agriculture ontology‐building tool that parses the ontology files to extract full data and update it based on the user needs. The technology is used to create the angular library for parsing the OWL files. The proposed ontology framework would accept user‐defined ontologies and provide an interface for an online updating of the owl files to ensure the interoperability in the agriculture IoT.

Integrated heat and electric energy ontology for digital twins of active distribution grids

Abstract: The paper presents the initial version of an energy domain ontology intended to consistently describe active heating and electric distribution grids in the Industry 4.0 and energy transition context. The ontology applies as a semantic basis for digital twins of grid segments to reduce costs of integrating numerous disparate models that comprise the twin. Therefore, our ontology, unlike many others, is tailored for the convenience of modeling grid operation and management processes in all parts and aspects. Yet, top-level ontology concepts are made compatible with semantic models and ontologies existing in the energy domain, such as Common Information Model (CIM), Smart Appliances REFerence (SAREF), Domain Analysis-Based Global Energy Ontology (DABGEO). The taxonomy of processes to be twinned occupies a central place in our ontology. Taxonomies of other top-level concepts relate to them: events trigger processes, actors initiate processes and participate in them, equipment and other resources are used/affected, and models formally represent everything.

An Ontology-based Approach to Facilitate Semantic Interoperability of Context-Aware Systems

Abstract: Semantic interoperability is one of the most critical challenges for software developers while integrating two or more context-aware systems. In such circumstances, it is essential to understand the meaning and interpretation of various contexts in different business domains and to align context ontologies together. Our investigations reveal that existing works poorly address these requirements. To fill this gap, this paper proposes the Ontology as a Service (OaaS) approach to facilitate the semantic interoperability of context-aware systems. In the proposed solution, the complexities of semantic interoperability are resolved and handled by a standalone ontology service, which can be easily reused and consumed by different ontology service consumers and brokers. The proposed ontology service includes several ontology repositories, a web service that positions a context concept in the existing ontologies and another web service that maps the relationship between existing context concepts. We evaluated our approach with a case study that resolves three semantic differences between two IoT applications originating from heterogeneous domains of smart home and health environments.

OntoEval: an Automated Ontology Evaluation System

Abstract: Developing semantically-aware web services requires comprehensive and accurate ontologies. Evaluating an existing ontology or adapting it is a labor-intensive and complex task for which no automated tools exist. Nevertheless, in this paper we propose a tool that aims at making this vision come true, i.e., we present a tool for the automated evaluation of ontologies that allows one to rapidly assess an ontology’s coverage of a domain and identify specific problems in the ontology’s structure. The tool evaluates the domain coverage and correctness of parent-child relations of a given ontology based on domain information derived from a text corpus representing the domain. The tool provides both overall statistics and detailed analysis of sub-graphs of the ontology. In the demo, we show how these features can be used for the iterative improvement of an ontology.

Global Ontology Entities Embeddings

Abstract: Ontologies are among the most widely used types of knowledge representation formalisms. The application of deep learning techniques in the field of ontology engineering has reinforced the need to learn and generate representations of ontological data. This allows ontologies to be exploited by such models, and thus automate various ontology engineering tasks, where most of the existing tools and machine learning approaches require a numerical feature vectors associated with each concept. This paper outlines a novel approach for learning global ontology entities embeddings by exploiting the structure and the various taxonomic and semantic relationships present in ontologies, taking into account all the information present in the ontological graph and carried by the OWL/RDF triples. Thus, producing global ontology entities embeddings capturing the global ontological graph semantics and similarities enclosed in the source ontology. Three different neural network models have been proposed based on two architectures: multi-input and multi-output, trained using the contrastive estimation technique. The evaluation on OWL/RDF ontologies and word semantic similarity tasks using various graph and WordNet based similarity measures, show that our approach yields competitive results outperforming the state-of-the-art ontology and word embedding models.

An ontology for maintenance procedure documentation

Abstract: In mining, manufacturing and industrial process industries, maintenance procedures are used as an aid to guide technicians through complex manual tasks. These procedures are not machine-readable, and cannot support reasoning in digitally integrated manufacturing systems. Procedure documents contain unstructured text and are stored in a variety of formats. The aim of this work is to query information held in real industrial maintenance procedures. To achieve this, we develop an ontology for maintenance procedures using the OWL 2 description language. We leverage classes and object properties from the ISO 15926 Part 14 Upper Ontology and create a domain ontology. The key contribution of this paper is a demonstration of trade-offs required when modelling an existing engineering artifact, where an abstraction of its contents is given a-priori. We provide an ontologically rigorous abstraction of notions captured in procedure documentation to a set of classes, relations and axioms that allow reasoning over the contents. Validation of the ontology is performed via a series of competency questions based on queries relevant to technicians, engineers and schedulers in industry. The ontology is applied to real world maintenance procedures from two industrial organisations.

Design of a blockchain based secure and efficient ontology generation model for multiple data genres using augmented stratification in healthcare industry

Abstract: Abstract Ontology generation is a process of relationship analysis, and representation for multiple data categories using automatic or semi-automatic approaches. This process requires a domain knowledgebase that describes given input data using entity-to-entity relations. A wide variety of approaches are proposed for this purpose, and each of them processes & converts input data using multiple relationship evaluation stages. These stages include data-preprocessing, correlation analysis, entity mapping, and ontology generation. A very few of these approaches are dataset independent, and most of them do not implement security measures during ontology generation, which limits their security, scalability & deployment capabilities during real-time implementation. Thus, in this text a blockchain based secure & efficient ontology generation model for multiple data genres using augmented stratification (BOGMAS) is described. The BOGMAS model uses a semi-supervised approach for ontology generation from almost any structured or unstructured dataset. It uses a variance-based method (VBM) for reduction of redundant numerical features from the dataset, while textual features are converted to numerical values via standard word2vec model, and then processed using VBM. This model uses a combination of linear support vector machine (LSVM), and extra trees (ET) stratifiers for variance estimation, which makes the model highly efficient, and reduces redundant features from the output ontology. These feature sets & their variances are given to a correlation engine for relationship estimation, and ontology generation. Each ontology record is secured using a mutable proof-of-work (PoW) based blockchain model, which assists in imbibing transparency, traceability, and distributed peer-to-peer processing capabilities. The generated ontology is represented using an incremental OWL (W3C Web Ontology Language) format, which assists in dynamically sizing the ontology depending upon incoming data. Performance of the proposed BOGMAS model is evaluated in terms of precision & recall of representation, memory usage, computational complexity, and accuracy of attack detection. It is observed that the proposed model is highly efficient in terms of precision, recall & accuracy performance, but has incrementally higher computational complexity & delay of ontology formation when compared with existing approaches. Due to this incremental increase in delay, the proposed model is observed to be applicable for a wide variety of real-time scenarios, which include but are not limited to, medical ontology generation, sports ontology generation, and internet of things (IoT) ontology generation with high security levels.

A system for automated construction of ontologiesof scientific subject domains based on ontology design patterns

Abstract: В настоящее время онтологии признаны наиболее эффективным средством формализации и систематизации знаний и данных в научных предметных областях (НПО). В докладе представлен подход к автоматизированному построению онтологий НПО на основе паттернов онтологического проектирования (паттернов ОП) и реализующая его система, которая включает онтологию паттернов ОП, репозиторий паттернов ОП, редактор онтологий, редактор паттернов ОП, редактор данных, информационно-аналитический интернет-ресурс и подсистему автоматического пополнения онтологии. At present, ontologies are recognized as the most effective means of formalizing and systematizing knowledge and data in scientific subject domains (SSD). The paper presents an approach to the automated construction of ontologies based on ontology design patterns (ODPs) and a system that implements it. The system includes an ontology of ODPs, a repository of ODPs, an ontology editor, a data editor, an ontology editor, an information-analytical Internet resource, and a subsystem for automatic ontology population.

Ontology-based Mediation with Quality Criteria

Abstract: This paper presents a semantic system named OntMed for an ontology-based data integration of heterogeneous data sources to achieve interoperability between heterogeneous data sources. Our system is based on the quality criteria (consistency, completeness and conciseness) for building the reliable analysis contexts to provide an accurate unified view of data to the end user. The generation of an error-free global analysis context with the semantic validation of initial mappings generates accuracy, and provides the means to access and exchange information in semantically sound manner. In addition, data integration in this way becomes more practical for dynamic situations and helps decision maker to work within more consistent and reliable virtual data warehouse. We also discuss our successful participation in the Ontology Alignment for Query Answering (OA4QA) track at OAEI 2015 campaign, where our system (DKP-AOM) has performed fair enough and became one of only matchers whose alignments allowed answering all the queries of the evaluation.

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

Abstract: Geological modeling currently uses various computer-based applications. Data harmonization at the semantic level by means of ontologies is essential for making these applications interoperable. Since geo-modeling is currently part of multidisciplinary projects, semantic harmonization is required to model not only geological knowledge but also to integrate other domain knowledge at a general level. For this reason, the domain ontologies used for describing geological knowledge must be based on a sound ontology background to ensure the described geological knowledge is integratable. This paper presents a domain ontology: GeoFault, resting on the Basic Formal Ontology BFO (Arp et al., 2015) and the GeoCore ontology (Garcia et al., 2020). It models the knowledge related to geological faults. Faults are essential to various industries but are complex to model. They can be described as thin deformed rock volumes or as spatial arrangements resulting from the different displacements of geological blocks. At a broader scale, faults are currently described as mere surfaces, which are the components of complex fault arrays. The reference to the BFO and GeoCore package allows assigning these various fault elements to define ontology classes and their logical linkage within a consistent ontology framework. The GeoFault ontology covers the core knowledge of faults 'strico sensu,' excluding ductile shear deformations. This considered vocabulary is essentially descriptive and related to regional to outcrop scales, excluding microscopic, orogenic, and tectonic plate structures. The ontology is molded in OWL 2, validated by competency questions with two use cases, and tested using an in-house ontology-driven data entry application. The work of GeoFault provides a solid framework for disambiguating fault knowledge and a foundation of fault data integration for the applications and the users.

Mapping of extensible markup language-to-ontology representation for effective data integration

Abstract: Extensible markup language (XML) is well-known as the standard for data exchange over the Internet. It is flexible and has high expressibility to express the relationship between the data stored. Yet, the structural complexity and the semantic relationships are not well expressed. On the other hand, ontology models the structural, semantic and domain knowledge effectively. By combining ontology with visualization effect, one will be able to have a closer view based on respective user requirements. In this paper, we propose several mapping rules for the transformation of XML into ontology representation. Subsequently, we show how the ontology is constructed based on the proposed rules using the sample domain ontology in University of Wisconsin-Milwaukee (UWM) and mondial datasets. We also look at the schemas, query workload, and evaluation, to derive the extended knowledge from the existing ontology. The correctness of the ontology representation has been proven effective through supporting various types of complex queries in simple protocol and resource description framework query language (SPARQL) language.

Enhancing Cluster Quality of Numerical Datasets with Domain Ontology

Abstract: Ontology-based clustering has gained attention in recent years due to the potential benefits of ontology. Current ontology-based clustering approaches have mainly been applied to reduce the dimensionality of attributes in text document clustering. Reduction in dimensionality of attributes using ontology helps to produce high quality clusters for a dataset. However, ontology-based approaches in clustering numerical datasets have not been gained enough attention. Moreover, some literature mentions that ontology-based clustering can produce either high quality or low-quality clusters from a dataset. Therefore, in this paper we present a clustering approach that is based on domain ontology to reduce the dimensionality of attributes in a numerical dataset using domain ontology and to produce high quality clusters. For every dataset, we produce three datasets using domain ontology. We then cluster these datasets using a genetic algorithm-based clustering technique called GenClust++. The clusters of each dataset are evaluated in terms of Sum of Squared-Error (SSE). We use six numerical datasets to evaluate the performance of our ontology-based approach. The experimental results of our approach indicate that cluster quality gradually improves from lower to the higher levels of a domain ontology.

Extending a Public Health Ontology in Turkish for Improved AI Applications

Abstract: Domain ontologies are significant structured and semantic resources for information systems. Therefore, they are proposed and used in many application domains including medicine, finance, law, and energy. In this chapter, the authors extend a public health ontology in a semi-automatic manner in order to increase its usability in information systems and social media analysis applications. Basically, the public health ontology is extended with new concepts from a medical ontology, and it is partially aligned with this medical ontology. The final form of the ontology is named Public Health Ontology for Turkish (PHOTr). With this extension, the authors expect that the number and range of artificial intelligence applications that can benefit from the ontology will increase considerably. Also presented in this chapter are important future research directions based on the current study, in addition to information about the applications that can make use of PHOTr.

NGBO: Introducing -omics metadata to biobanking ontology

Abstract: Background With improvements in high throughput sequencing technologies and the constant generation of large biomedical datasets, biobanks increasingly take on the role of managing and delivering not just specimens but also data. However, re-using data from different biobanks is challenged by incompatible data representations. Contextual data describing biobank digital resources often contain unstructured textual information incompatible with computational processes such as automated data discovery and integration. Therefore, a consistent and comprehensive contextual data framework is needed to increase discovery, reusability, and integrability across data sources. Methods Based on available genomics standards (e.g., Minimum information about a microarray experiment (MIAME)), the College of American Pathologists (CAP) laboratory accreditation requirements, and the Open Biological and Biomedical Ontologies Foundry principles, we developed the Next Generation Biobanking Ontology (NGBO). In addition, we created new terms and re-used concepts from the Ontology for Biomedical Investigations (OBI) and the Ontology for Biobanking (OBIB) to build NGBO. Results The Next Generation Biobanking Ontology https://www.ebi.ac.uk/ols4/ontologies/ngbo is an open application ontology representing omics contextual data, licensed under the Apache License 2.0. The ontology focuses on capturing information about three main activities: wet bench analysis used to generate omics data, bioinformatics analysis used to process and interpret data, and data management. In this paper, we demonstrated the use of the NGBO to add semantic statements to real-life use cases and query data previously stored in unstructured textual format.

Methods of managing the evolution of ontologies and their alignments

Abstract: Abstract Nowadays, none can expect that knowledge about some part of reality will not change. Consequently, a representation of such evolving knowledge (for example, ontologies) also changes. Such changes entail that applications incorporating such knowledge may become compromised and yield wrong results. An example of such an application is ontology alignment which can be informally described as a set of connections between two ontologies. Those connections mark elements from two ontologies that relate to the same parts of reality. In changing one of the corresponding ontologies, such connections may become invalid. One may designate the ontology alignment once again from scratch for altered ontologies. However, such an approach is time and resource-consuming. The paper comprehensively presents our ontology evolution and alignment maintenance framework. It can be used to preserve the validity of ontology alignment using only the analysis of changes introduced to maintained ontologies. The precise definition of ontologies is provided, along with a definition of the ontology change log. A set of algorithms that allow revalidating ontology alignments have been built based on such elements.