Academic literature on the topic 'Database-to-ontology mapping principles'

ABSTRACT As the planarian research community expands, the need for an interoperable data organization framework for tool building has become increasingly apparent. Such software would streamline data annotation and enhance cross-platform and cross-species searchability. We created the Planarian Anatomy Ontology (PLANA), an extendable relational framework of defined Schmidtea mediterranea (Smed) anatomical terms used in the field. At publication, PLANA contains over 850 terms describing Smed anatomy from subcellular to system levels across all life cycle stages, in intact animals and regenerating body fragments. Terms from other anatomy ontologies were imported into PLANA to promote interoperability and comparative anatomy studies. To demonstrate the utility of PLANA as a tool for data curation, we created resources for planarian embryogenesis, including a staging series and molecular fate-mapping atlas, and the Planarian Anatomy Gene Expression database, which allows retrieval of a variety of published transcript/gene expression data associated with PLANA terms. As an open-source tool built using FAIR (findable, accessible, interoperable, reproducible) principles, our strategy for continued curation and versioning of PLANA also provides a platform for community-led growth and evolution of this resource.

In times of emerging diseases, data sharing and data integration are of particular relevance for One Health Surveillance (OHS) and decision support. Furthermore, there is an increasing demand to provide governmental data in compliance to the FAIR (Findable, Accessible, Interoperable, Reusable) data principles. Semantic web technologies are key facilitators for providing data interoperability, as they allow explicit annotation of data with their meaning, enabling reuse without loss of the data collection context. Among these, we highlight ontologies as a tool for modeling knowledge in a field, which simplify the interpretation and mapping of datasets in a computer readable medium; and the Resource Description Format (RDF), which allows data to be shared among human and computer agents following this knowledge model. Despite their potential for enabling cross-sectoral interoperability and data linkage, the use and application of these technologies is often hindered by their complexity and the lack of easy-to-use software applications. To overcome these challenges the OHEJP Project ORION developed the Health Surveillance Ontology (HSO). This knowledge model forms a foundation for semantic interoperability in the domain of One Health Surveillance. It provides a solution to add data from the target sectors (public health, animal health and food safety) in compliance with the FAIR principles of findability, accessibility, interoperability, and reusability, supporting interdisciplinary data exchange and usage. To provide use cases and facilitate the accessibility to HSO, we developed the One Health Linked Data Toolbox (OHLDT), which consists of three new and custom-developed web applications with specific functionalities. The first web application allows users to convert surveillance data available in Excel files online into HSO-RDF and vice versa. The web application demonstrates that data provided in well-established data formats can be automatically translated in the linked data format HSO-RDF. The second application is a demonstrator of the usage of HSO-RDF in a HSO triplestore database. In the user interface of this application, the user can select HSO concepts based on which to search and filter among surveillance datasets stored in a HSO triplestore database. The service then provides automatically generated dashboards based on the context of the data. The third web application demonstrates the use of data interoperability in the OHS context by using HSO-RDF to annotate meta-data, and in this way link datasets across sectors. The web application provides a dashboard to compare public data on zoonosis surveillance provided by EFSA and ECDC. The first solution enables linked data production, while the second and third provide examples of linked data consumption, and their value in enabling data interoperability across sectors. All described solutions are based on the open-source software KNIME and are deployed as web service via a KNIME Server hosted at the German Federal Institute for Risk Assessment. The semantic web extension of KNIME, which is based on the Apache Jena Framework, allowed a rapid an easy development within the project. The underlying open source KNIME workflows are freely available and can be easily customized by interested end users. With our applications, we demonstrate that the use of linked data has a great potential strengthening the use of FAIR data in OHS and interdisciplinary data exchange.

M. Tech. (Information Technology, Department of Information and Communications Technology, Faculty of Applied an Computer Sciences), Vaal University of Technolog
One of the main research topics in Semantic Web is the semantic extraction of knowledge stored in relational databases through ontologies. This is because ontologies are core components of the Semantic Web. Therefore, several tools, algorithms and frameworks are being developed to enable the automatic conversion of relational databases into ontologies. Ontologies produced with these tools, algorithms and frameworks needs to be valid and competent for them to be useful in Semantic Web applications within the target knowledge domains. However, the main challenges are that many existing automatic ontology construction tools, algorithms, and frameworks fail to address the issue of ontology verification and ontology competency evaluation. This study investigates possible solutions to these challenges. The study began with a literature review in the semantic web field. The review let to the conceptualisation of a framework for semantic knowledge extraction to deal with the abovementioned challenges. The proposed framework had to be evaluated in a real life knowledge domain. Therefore, a knowledge domain was chosen as a case study. The data was collected and the business rules of the domain analysed to develop a relational data model. The data model was further implemented into a test relational database using Oracle RDBMS. Thereafter, Protégé plugins were applied to automatically construct ontologies from the relational database. The resulting ontologies are further validated to match their structures against existing conceptual database-to-ontology mapping principles. The matching results show the performance and accuracy of Protégé plugins in automatically converting relational databases into ontologies. Finally, the study evaluated the resulting ontologies against the requirements of the knowledge domain. The requirements of the domain are modelled with competency questions (CQs) and mapped to the ontology using SPARQL queries design, execution and analysis against users’ views of CQs answers. Experiments show that, although users have different views of the answers to CQs, the execution of the SPARQL translations of CQs against the ontology does produce outputs instances that satisfy users’ expectations. This indicates that Protégé plugins generated ontology from relational database embodies domain and semantic features to be useful in Semantic Web applications.

In this chapter we propose a new methodology for ontology building, which is based on a set of mapping rules from a conceptual schema (Entity-Relationship) and its corresponding logical model (relational model) toward a conceptual ontology. The proposed methodology consists of three big steps, which are, the Transformation (mapping), the Formalization and the Codification. The crucial step in the building process of this methodology, is the transformation, this last is based on an automatic extraction of information for a conceptual model ER (such as entities, relationship, properties and cardinalities). The data stored in the database (the schema extension) are extracted and used to create instances of the ontology. At the end of this stage we will have a complete conceptual ontology can be used in different applications. The objective behind this work has several aims, which can be exploited in many fields, among others, search and retrieval of relevant terms in a domain of discourse, our methodology minimizes the manual work and gives a good result in an optimal time, the second is to facilitate the migration of an information system based on a classical approach (entity-relationships and relational model) towards another solution based on an ontological approach, while retaining the principle of operation of the first system.

A conceptual modeling grammar should be based on the theory of ontology and possess clear ontological semantics to represent problem domain knowledge in a precise and consistent manner. In this paper, we follow the notion of ontological expressiveness and conduct an ontological analysis of a newly-developed conceptual modeling grammar termed MibML (Multiagent-based Integrative Business Modeling Language). The grammar is developed to respond to the emerging needs for a special-purpose conceptual modeling grammar for the MIBIS (Multiagent-based Integrative Business Information Systems) universe. We assign ontological semantics to the MibML constructs and their relationship using the BWW (Bunge-Wand-Weber) model. This paper provides a starting point to further develop ontological principles and step-by-step guidelines to ensure the straightforward mapping from domain knowledge into MibML modeling constructs.