OntoCheck: verifying ontology naming conventions and metadata completeness in Protégé 4

BACKGROUND: Although policy providers have outlined minimal metadata guidelines and naming conventions, ontologies of today still display inter- and intra-ontology heterogeneities in class labelling schemes and metadata completeness. This fact is at least partially due to missing or inappropriate tools. Software support can ease this situation and contribute to overall ontology consistency and quality by helping to enforce such conventions. OBJECTIVE: We provide a plugin for the Protégé Ontology editor to allow for easy checks on compliance towards ontology naming conventions and metadata completeness, as well as curation in case of found violations. IMPLEMENTATION: In a requirement analysis, derived from a prior standardization approach carried out within the OBO Foundry, we investigate the needed capabilities for software tools to check, curate and maintain class naming conventions. A Protégé tab plugin was implemented accordingly using the Protégé 4.1 libraries. The plugin was tested on six different ontologies. Based on these test results, the plugin could be refined, also by the integration of new functionalities. RESULTS: The new Protégé plugin, OntoCheck, allows for ontology tests to be carried out on OWL ontologies. In particular the OntoCheck plugin helps to clean up an ontology with regard to lexical heterogeneity, i.e. enforcing naming conventions and metadata completeness, meeting most of the requirements outlined for such a tool. Found test violations can be corrected to foster consistency in entity naming and meta-annotation within an artefact. Once specified, check constraints like name patterns can be stored and exchanged for later re-use. Here we describe a first version of the software, illustrate its capabilities and use within running ontology development efforts and briefly outline improvements resulting from its application. Further, we discuss OntoChecks capabilities in the context of related tools and highlight potential future expansions. CONCLUSIONS: The OntoCheck plugin facilitates labelling error detection and curation, contributing to lexical quality assurance in OWL ontologies. Ultimately, we hope this Protégé extension will ease ontology alignments as well as lexical post-processing of annotated data and hence can increase overall secondary data usage by humans and computers

Designing novel abstraction networks for ontology summarization and quality assurance

Biomedical ontologies are complex knowledge representation systems. Biomedical ontologies support interdisciplinary research, interoperability of medical systems, and Electronic Healthcare Record (EHR) encoding. Ontologies represent knowledge using concepts (entities) linked by relationships. Ontologies may contain hundreds of thousands of concepts and millions of relationships. For users, the size and complexity of ontologies make it difficult to comprehend “the big picture” of an ontology\u27s content. For ontology editors, size and complexity make it difficult to uncover errors and inconsistencies. Errors in an ontology will ultimately affect applications that utilize the ontology. In prior studies abstraction networks (AbNs) were developed to provide a compact summary of an ontology\u27s content and structure. AbNs have been shown to successfully support ontology summarization and quality assurance (QA), e.g., for SNOMED CT and NCIt. Despite the success of these previous studies, several major, unaddressed issues affect the applicability and usability of AbNs. This thesis is broken into five major parts, each addressing one issue. The first part of this dissertation addresses the scalability of AbN-based QA techniques to large SNOMED CT hierarchies. Previous studies focused on relatively small hierarchies. The QA techniques developed for these small hierarchies do not scale to large hierarchies, e.g., Procedure and Clinical finding. A new type of AbN, called a subtaxonomy, is introduced to address this problem. Subtaxonomies summarize a subset of an ontology\u27s content. Several types of subtaxonomies and subtaxonomy-based QA studies are discussed. The second part of this dissertation addresses the need for summarization and QA methods for the twelve SNOMED CT hierarchies with no lateral relationships. Previously developed SNOMED CT AbN derivation methodologies, which require lateral relationships, cannot be applied to these hierarchies. The Tribal Abstraction Network (TAN) is a new type of AbN derived using only hierarchical relationships. A TAN-based QA methodology is introduced and the results of a QA review of the Observable entity hierarchy are reported. The third part focuses on the development of generic AbN derivation methods that are applicable to groups of structurally similar ontologies, e.g., those developed in the Web Ontology Language (OWL) format. Previously, AbN derivation techniques were applicable to only a single ontology at a time. AbNs that are applicable to many OWL ontologies are introduced, a preliminary study on OWL AbN granularity is reported on, and the results of several QA studies are presented. The fourth part describes Diff Abstraction Networks, which summarize and visualize the structural differences between two ontology releases. Diff Area Taxonomy and Diff Partial-area Taxonomy derivation methodologies are introduced and Diff Partial-area taxonomies are derived for three OWL ontologies. The Diff Abstraction Network approach is compared to the traditional ontology diff approach. Lastly, tools for deriving and visualizing AbNs are described. The Biomedical Layout Utility Framework is introduced to support the automatic creation, visualization, and exploration of abstraction networks for SNOMED CT and OWL ontologies

KNIT: Ontology reusability through knowledge graph exploration

Ontologies have become a standard for knowledge representation across several domains. In Life Sciences, numerous ontologies have been introduced to represent human knowledge, often providing overlapping or conflicting perspectives. These ontologies are usually published as OWL or OBO, and are often registered in open repositories, e.g., BioPortal. However, the task of finding the concepts (classes and their properties) defined in the existing ontologies and the relationships between these concepts across different ontologies – for example, for developing a new ontology aligned with the existing ones – requires a great deal of manual effort in searching through the public repositories for candidate ontologies and their entities. In this work, we develop a new tool, KNIT, to automatically explore open repositories to help users fetch the previously designed concepts using keywords. User-specified keywords are then used to retrieve matching names of classes or properties. KNIT then creates a draft knowledge graph populated with the concepts and relationships retrieved from the existing ontologies. Furthermore, following the process of ontology learning, our tool refines this first draft of an ontology. We present three BioPortal-specific use cases for our tool. These use cases outline the development of new knowledge graphs and ontologies in the sub-domains of biology: genes and diseases, virome and drugs.This work has been funded by grant PID2020-112540RB-C4121, AETHER-UMA (A smart data holistic approach for context-aware data analytics: semantics and context exploitation). Funding for open access charge: Universidad de Málaga / CBUA

Community based mappings for the semantic web: MappingsTool

An extension of BioPortal, an open source ontology repository developed by the UNIVERSITY OF STANFORD, that facilitates the manipulation of mappings between ontologies. We provide a flexible web user interface that facilitate the workflow to create a mapping and the exploration of the relations between ontologies.Pera Mira, O. (2011). Community based mappings for the semantic web: MappingsTool. http://hdl.handle.net/10251/11159.Archivo delegad

The eXtensible ontology development (XOD) principles and tool implementation to support ontology interoperability

Abstract Ontologies are critical to data/metadata and knowledge standardization, sharing, and analysis. With hundreds of biological and biomedical ontologies developed, it has become critical to ensure ontology interoperability and the usage of interoperable ontologies for standardized data representation and integration. The suite of web-based Ontoanimal tools (e.g., Ontofox, Ontorat, and Ontobee) support different aspects of extensible ontology development. By summarizing the common features of Ontoanimal and other similar tools, we identified and proposed an “eXtensible Ontology Development” (XOD) strategy and its associated four principles. These XOD principles reuse existing terms and semantic relations from reliable ontologies, develop and apply well-established ontology design patterns (ODPs), and involve community efforts to support new ontology development, promoting standardized and interoperable data and knowledge representation and integration. The adoption of the XOD strategy, together with robust XOD tool development, will greatly support ontology interoperability and robust ontology applications to support data to be Findable, Accessible, Interoperable and Reusable (i.e., FAIR).https://deepblue.lib.umich.edu/bitstream/2027.42/140740/1/13326_2017_Article_169.pd

Overcoming the Ontology Enrichment Bottleneck with Quick Term Templates

The developers of the Ontology of Biomedical Investigations (OBI) primarily use Protégé for editing. However, adding many classes with similar patterns of logical definition is time consuming, error prone, and requires the editor to have some expertise in OWL. Therefore, the process is poorly suited for a large number of domain experts who have limited experience Protégé and ontology development. We have developed a procedure to ease this task and allow such domain experts to add terms to the ontology in a way that both effectively includes complex logical definitions yet requires minimal manual intervention by OBI developers. The procedure is based on editing a Quick Term Template in a spreadsheet format which is subsequently converted into an OWL file. This procedure promises to be a robust and scalable approach for ontology enrichment

Aufbau eines Online-Dienstes zur webbasierten Versionierung von Ontologien

Ontologien gewinnen in der heutigen Zeit zunehmend an Bedeutung. Informationen können effizient durch eine formale, vernetzte Struktur genutzt werden. Die Grundlage bildet ein formales System, das durch eine standardisierte Ontologiesprache definiert ist und zur Beschreibung von Ontologien verwendet wird. In dem Zusammenhang lassen sich Informationen in einem einheitlichen Format darstellen. Ontologien bestehen generell aus Konzepten, die durch Beziehungen miteinander verbunden sind. Ein großer Forschungszweig beschäftigt sich mit der Annotierung dieser Konzepte. Der Vorgang ermöglicht eine einheitliche Beschreibung, indem Ontologiekonzepte explizit zu Objekten der realen Welt zugeordnet werden

Ontology-based representation and analysis of host-Brucella interactions

Abstract Background Biomedical ontologies are representations of classes of entities in the biomedical domain and how these classes are related in computer- and human-interpretable formats. Ontologies support data standardization and exchange and provide a basis for computer-assisted automated reasoning. IDOBRU is an ontology in the domain of Brucella and brucellosis. Brucella is a Gram-negative intracellular bacterium that causes brucellosis, the most common zoonotic disease in the world. In this study, IDOBRU is used as a platform to model and analyze how the hosts, especially host macrophages, interact with virulent Brucella strains or live attenuated Brucella vaccine strains. Such a study allows us to better integrate and understand intricate Brucella pathogenesis and host immunity mechanisms. Results Different levels of host-Brucella interactions based on different host cell types and Brucella strains were first defined ontologically. Three important processes of virulent Brucella interacting with host macrophages were represented: Brucella entry into macrophage, intracellular trafficking, and intracellular replication. Two Brucella pathogenesis mechanisms were ontologically represented: Brucella Type IV secretion system that supports intracellular trafficking and replication, and Brucella erythritol metabolism that participates in Brucella intracellular survival and pathogenesis. The host cell death pathway is critical to the outcome of host-Brucella interactions. For better survival and replication, virulent Brucella prevents macrophage cell death. However, live attenuated B. abortus vaccine strain RB51 induces caspase-2-mediated proinflammatory cell death. Brucella-associated cell death processes are represented in IDOBRU. The gene and protein information of 432 manually annotated Brucella virulence factors were represented using the Ontology of Genes and Genomes (OGG) and Protein Ontology (PRO), respectively. Seven inference rules were defined to capture the knowledge of host-Brucella interactions and implemented in IDOBRU. Current IDOBRU includes 3611 ontology terms. SPARQL queries identified many results that are critical to the host-Brucella interactions. For example, out of 269 protein virulence factors related to macrophage-Brucella interactions, 81 are critical to Brucella intracellular replication inside macrophages. A SPARQL query also identified 11 biological processes important for Brucella virulence. Conclusions To systematically represent and analyze fundamental host-pathogen interaction mechanisms, we provided for the first time comprehensive ontological modeling of host-pathogen interactions using Brucella as the pathogen model. The methods and ontology representations used in our study are generic and can be broadened to study the interactions between hosts and other pathogens.http://deepblue.lib.umich.edu/bitstream/2027.42/113668/1/13326_2015_Article_36.pd

OntoFox: web-based support for ontology reuse

<p>Abstract</p> <p>Background</p> <p>Ontology development is a rapidly growing area of research, especially in the life sciences domain. To promote collaboration and interoperability between different projects, the OBO Foundry principles require that these ontologies be open and non-redundant, avoiding duplication of terms through the re-use of existing resources. As current options to do so present various difficulties, a new approach, MIREOT, allows specifying import of single terms. Initial implementations allow for controlled import of selected annotations and certain classes of related terms.</p> <p>Findings</p> <p>OntoFox <url>http://ontofox.hegroup.org/</url> is a web-based system that allows users to input terms, fetch selected properties, annotations, and certain classes of related terms from the source ontologies and save the results using the RDF/XML serialization of the Web Ontology Language (OWL). Compared to an initial implementation of MIREOT, OntoFox allows additional and more easily configurable options for selecting and rewriting annotation properties, and for inclusion of all or a computed subset of terms between low and top level terms. Additional methods for including related classes include a SPARQL-based ontology term retrieval algorithm that extracts terms related to a given set of signature terms and an option to extract the hierarchy rooted at a specified ontology term. OntoFox's output can be directly imported into a developer's ontology. OntoFox currently supports term retrieval from a selection of 15 ontologies accessible via SPARQL endpoints and allows users to extend this by specifying additional endpoints. An OntoFox application in the development of the Vaccine Ontology (VO) is demonstrated.</p> <p>Conclusions</p> <p>OntoFox provides a timely publicly available service, providing different options for users to collect terms from external ontologies, making them available for reuse by import into client OWL ontologies.</p