Community Ontology Research Articles

Prediction and curation of missing biomedical identifier mappings with Biomappings.

Biomedical identifier resources (such as ontologies, taxonomies, and controlled vocabularies) commonly overlap in scope and contain equivalent entries under different identifiers. Maintaining mappings between these entries is crucial for interoperability and the integration of data and knowledge. However, there are substantial gaps in available mappings motivating their semi-automated curation. Biomappings implements a curation workflow for missing mappings which combines automated prediction with human-in-the-loop curation. It supports multiple prediction approaches and provides a web-based user interface for reviewing predicted mappings for correctness, combined with automated consistency checking. Predicted and curated mappings are made available in public, version-controlled resource files on GitHub. Biomappings currently makes available 9274 curated mappings and 40691 predicted ones, providing previously missing mappings between widely used identifier resources covering small molecules, cell lines, diseases, and other concepts. We demonstrate the value of Biomappings on case studies involving predicting and curating missing mappings among cancer cell lines as well as small molecules tested in clinical trials. We also present how previously missing mappings curated using Biomappings were contributed back to multiple widely used community ontologies. The data and code are available under the CC0 and MIT licenses at https://github.com/biopragmatics/biomappings.

Extending and using anatomical vocabularies in the stimulating peripheral activity to relieve conditions project.

The stimulating peripheral activity to relieve conditions (SPARC) program is a US National Institutes of Health-funded effort to improve our understanding of the neural circuitry of the autonomic nervous system (ANS) in support of bioelectronic medicine. As part of this effort, the SPARC project is generating multi-species, multimodal data, models, simulations, and anatomical maps supported by a comprehensive knowledge base of autonomic circuitry. To facilitate the organization of and integration across multi-faceted SPARC data and models, SPARC is implementing the findable, accessible, interoperable, and reusable (FAIR) data principles to ensure that all SPARC products are findable, accessible, interoperable, and reusable. We are therefore annotating and describing all products with a common FAIR vocabulary. The SPARC Vocabulary is built from a set of community ontologies covering major domains relevant to SPARC, including anatomy, physiology, experimental techniques, and molecules. The SPARC Vocabulary is incorporated into tools researchers use to segment and annotate their data, facilitating the application of these ontologies for annotation of research data. However, since investigators perform deep annotations on experimental data, not all terms and relationships are available in community ontologies. We therefore implemented a term management and vocabulary extension pipeline where SPARC researchers may extend the SPARC Vocabulary using InterLex, an online vocabulary management system. To ensure the quality of contributed terms, we have set up a curated term request and review pipeline specifically for anatomical terms involving expert review. Accepted terms are added to the SPARC Vocabulary and, when appropriate, contributed back to community ontologies to enhance ANS coverage. Here, we provide an overview of the SPARC Vocabulary, the infrastructure and process for implementing the term management and review pipeline. In an analysis of >300 anatomical contributed terms, the majority represented composite terms that necessitated combining terms within and across existing ontologies. Although these terms are not good candidates for community ontologies, they can be linked to structures contained within these ontologies. We conclude that the term request pipeline serves as a useful adjunct to community ontologies for annotating experimental data and increases the FAIRness of SPARC data.

The Neuron Phenotype Ontology: A FAIR Approach to Proposing and Classifying Neuronal Types

The challenge of defining and cataloging the building blocks of the brain requires a standardized approach to naming neurons and organizing knowledge about their properties. The US Brain Initiative Cell Census Network, Human Cell Atlas, Blue Brain Project, and others are generating vast amounts of data and characterizing large numbers of neurons throughout the nervous system. The neuroscientific literature contains many neuron names (e.g. parvalbumin-positive interneuron or layer 5 pyramidal cell) that are commonly used and generally accepted. However, it is often unclear how such common usage types relate to many evidence-based types that are proposed based on the results of new techniques. Further, comparing different types across labs remains a significant challenge. Here, we propose an interoperable knowledge representation, the Neuron Phenotype Ontology (NPO), that provides a standardized and automatable approach for naming cell types and normalizing their constituent phenotypes using identifiers from community ontologies as a common language. The NPO provides a framework for systematically organizing knowledge about cellular properties and enables interoperability with existing neuron naming schemes. We evaluate the NPO by populating a knowledge base with three independent cortical neuron classifications derived from published data sets that describe neurons according to molecular, morphological, electrophysiological, and synaptic properties. Competency queries to this knowledge base demonstrate that the NPO knowledge model enables interoperability between the three test cases and neuron names commonly used in the literature.

К онтологии религиозного сообщества

The purpose of the article is to draw closer to the ontology of a religious community. This need has been justified, theoretical resources have been proposed, based on which a similar ontology can be built. These include the concept of “assemblies”, actor-network theory (B. Latour, M. Kallon, J. Lo), the concept of “community” (M. Blanchot, J.-L. Nancy, J. Agamben). As a result, it is possible to consider a religious community as a binding event, not relying on objectification.

A novel approach for learning ontology from relational database: from the construction to the evaluation

The aim of converting relational database into Ontology is to provide applications that are based on the semantic representation of the data. Whereas, representing the data using ontologies has shown to be a useful mechanism for managing and exchanging data. This is the reason why bridging the gap between relational databases and ontologies has attracted the interest of the ontology community from early years, and it is commonly referred to as the database-to-ontology mapping problem. In this paper, we: (1) propose a new life cycle for ontology learning from RDBs based on the software engineering requirements; (2) describe a new method for building ontology from Relational database based on the predefined life cycle; (3) add three new semantics that can be extracted from RDB; (4) we suggest an evaluation process based on two categories of metrics: (i) conceptual ontology (TBox) evaluation metrics; (ii) factual ontology (ABox) evaluation metrics.

Ontology Integration: Approaches and Challenging Issues

In recent years, the decentralized development of ontologies has led to the generation of multiple ontologies of overlapping knowledge. This heterogeneity problem can be tackled by integrating existing ontologies to build a single coherent one. Ontology integration has been investigated during the last two decades, but it is still a challenging task. In this article, we provide a comprehensive survey of all ontology integration aspects. We discuss related notions and scrutinize existing techniques and literature approaches. We also detail the role of ontology matching in the ontology integration process. Indeed, the ontology community has adopted the splitting of the ontology integration problem into matching, merging and repairing sub-tasks, where matching is a necessary preceding step for merging, and repairing can be included in the matching process or performed separately. Ontology matching and merging systems have become quite proficient, however the trickiest part lies in the repairing step. We also focus on the case of a holistic integration of multiple heterogeneous ontologies, which needs further exploration. Finally, we investigate challenges, open issues, and future directions of the ontology integration and matching areas.

The Ontologies Community of Practice: A CGIAR Initiative for Big Data in Agrifood Systems

SummaryHeterogeneous and multidisciplinary data generated by research on sustainable global agriculture and agrifood systems requires quality data labeling or annotation in order to be interoperable. As recommended by the FAIR principles, data, labels, and metadata must use controlled vocabularies and ontologies that are popular in the knowledge domain and commonly used by the community. Despite the existence of robust ontologies in the Life Sciences, there is currently no comprehensive full set of ontologies recommended for data annotation across agricultural research disciplines. In this paper, we discuss the added value of the Ontologies Community of Practice (CoP) of the CGIAR Platform for Big Data in Agriculture for harnessing relevant expertise in ontology development and identifying innovative solutions that support quality data annotation. The Ontologies CoP stimulates knowledge sharing among stakeholders, such as researchers, data managers, domain experts, experts in ontology design, and platform development teams.

The FOUnt ontologies for quantities, units, and the physical world

Quantities and units of measure provide an important means by which intelligent agents interact with the physical world. Although multiple ontologies for quantities and units of measure have been proposed within the Applied Ontology community, they often incorporate questionable ontological commitm ents. Quantities are combined using notions of dimensional analysis that often conflate the combination of units with algebraic operations on real numbers. In this paper, we present an alternative approach that shifts the focus to the connection between kinds of measurements associated with a unit and the physical objects and processes that are being measured. One of the key features of this approach is that it makes minimal ontological commitments with respect to the TUpper upper ontology – the only new classes that are introduced are the classes for kinds of measures and associated units. We propose axiomatizations of the intended semantics for combining measurements, and the correct axiomatization of the relationship between the quantities and units of measure and the existing upper ontology.

The Ontologies Community of Practice: An Initiative by the CGIAR Platform for Big Data in Agriculture

Heterogeneous and multidisciplinary data generated by research on sustainable global agriculture and agrifood systems requires quality data labelling to be interoperable. As recommended by the FAIR principles, data, labels and metadata must use controlled vocabularies and ontologies that are popular in the knowledge domain and commonly used by the community. Despite the existence of robust ontologies in the Life Sciences, there is currently no agreed full set of ontologies recommended for data annotation across agricultural research disciplines, which may span genetics, environment, agroecology, biology and socioeconomics. In this paper, we discuss the added value of the Ontologies Community of Practice (CoP) of the CGIAR Platform for Big Data in Agriculture for harnessing relevant ontology expertise. This CoP aims to stimulate knowledge sharing and directly support platform development teams by producing ontologies or contributing missing concepts, recommending best practices and identifying mitigation solutions when gold standard datasets are difficult to attain.

ROBOT: A Tool for Automating Ontology Workflows

BackgroundOntologies are invaluable in the life sciences, but building and maintaining ontologies often requires a challenging number of distinct tasks such as running automated reasoners and quality control checks, extracting dependencies and application-specific subsets, generating standard reports, and generating release files in multiple formats. Similar to more general software development, automation is the key to executing and managing these tasks effectively and to releasing more robust products in standard forms.For ontologies using the Web Ontology Language (OWL), the OWL API Java library is the foundation for a range of software tools, including the Protégé ontology editor. In the Open Biological and Biomedical Ontologies (OBO) community, we recognized the need to package a wide range of low-level OWL API functionality into a library of common higher-level operations and to make those operations available as a command-line tool.ResultsROBOT (a recursive acronym for “ROBOT is an OBO Tool”) is an open source library and command-line tool for automating ontology development tasks. The library can be called from any programming language that runs on the Java Virtual Machine (JVM). Most usage is through the command-line tool, which runs on macOS, Linux, and Windows. ROBOT provides ontology processing commands for a variety of tasks, including commands for converting formats, running a reasoner, creating import modules, running reports, and various other tasks. These commands can be combined into larger workflows using a separate task execution system such as GNU Make, and workflows can be automatically executed within continuous integration systems.ConclusionsROBOT supports automation of a wide range of ontology development tasks, focusing on OBO conventions. It packages common high-level ontology development functionality into a convenient library, and makes it easy to configure, combine, and execute individual tasks in comprehensive, automated workflows. This helps ontology developers to efficiently create, maintain, and release high-quality ontologies, so that they can spend more time focusing on development tasks. It also helps guarantee that released ontologies are free of certain types of logical errors and conform to standard quality control checks, increasing the overall robustness and efficiency of the ontology development lifecycle.

Husserlian Mereology and Intimate Community Membership

ABSTRACT This article provides an interpretation of Edmund Husserl's concept of personal community based on his formal mereology. Husserl's unique ontology of community is demonstrated, which opens up and informs a Husserlian account of the intentionality belonging to community membership. My focus here is exclusively on the intentionality of “intimate” community membership, appealing to Husserl's technical notion of intimacy as found in his theory of parts and wholes. I argue that Husserl's notorious account of some communities understood as “personalities of a higher order” is indicative of mereological intimacy.

The Blooming of Forgetting: Deep Play as a Focus for Improvisation in Community Music

The Blooming of Forgetting is a report on two community music projects grounded in improvisation and deep listening that took place in Melbourne, Australia, in 2016: combining personal reflection and theorising, an attempt is made to analyse what occurs "on the ground" in community music improvisation; what resonances for ideas of community ontology might arise, and why "deep play" could be an ideal zone for such projects to aim towards.

A First Step in the Co-Evolution of Blockchain and Ontologies: Towards Engineering an Ontology of Governance at the Blockchain Protocol Level

At the beginning of 2018, there is a growing belief that blockchain technologies constitute a revolutionary innovation in how we transfer value electronically. In that vein, blockchain may be a suitable complement to ontologies to achieve a big part of the vision of the semantic Web by Tim Berners-Lee. We believe that if this complementarity is to be achieved blockchain and ontologies must co-evolve. In this paper, we focus on what and how to engineer models, methods, designs, and implementations for this co-evolution. As a first step in this co-evolution, we propose a conceptual design of a governance ontology represented as meta-data tags to be embedded and instantiated in a smart contract at the blockchain protocol level. We develop this design by examining and analyzing smart contracts from the infamous The DAO experiment on the Ethereum blockchain. We believe there are two contributions of this paper: it serves to inform and implore the blockchain and ontology communities to recognize and collaborate with each other; and it outlines a roadmap for engineering artifacts to bridge the gap between blockchain community focus on protocol-level blockchain interoperability and the ontology community focus on semantic-level interoperability.

InfAcrOnt: calculating cross-ontology term similarities using information flow by a random walk

BackgroundSince the establishment of the first biomedical ontology Gene Ontology (GO), the number of biomedical ontology has increased dramatically. Nowadays over 300 ontologies have been built including extensively used Disease Ontology (DO) and Human Phenotype Ontology (HPO). Because of the advantage of identifying novel relationships between terms, calculating similarity between ontology terms is one of the major tasks in this research area. Though similarities between terms within each ontology have been studied with in silico methods, term similarities across different ontologies were not investigated as deeply. The latest method took advantage of gene functional interaction network (GFIN) to explore such inter-ontology similarities of terms. However, it only used gene interactions and failed to make full use of the connectivity among gene nodes of the network. In addition, all existent methods are particularly designed for GO and their performances on the extended ontology community remain unknown.ResultsWe proposed a method InfAcrOnt to infer similarities between terms across ontologies utilizing the entire GFIN. InfAcrOnt builds a term-gene-gene network which comprised ontology annotations and GFIN, and acquires similarities between terms across ontologies through modeling the information flow within the network by random walk. In our benchmark experiments on sub-ontologies of GO, InfAcrOnt achieves a high average area under the receiver operating characteristic curve (AUC) (0.9322 and 0.9309) and low standard deviations (1.8746e-6 and 3.0977e-6) in both human and yeast benchmark datasets exhibiting superior performance. Meanwhile, comparisons of InfAcrOnt results and prior knowledge on pair-wise DO-HPO terms and pair-wise DO-GO terms show high correlations.ConclusionsThe experiment results show that InfAcrOnt significantly improves the performance of inferring similarities between terms across ontologies in benchmark set.

Semantically Defining Populations for 'Omics Research

The study of populations is central to ‘omics research, whether sequencing environmental samples, controlling for population structure when looking for genetic variation within a species, or studying the evolution of large clades. Researchers use different operational definitions of populations and communities, via the highly varied creation of operational taxonomic units (OTUs) and, in some cases, use of unclustered sequences. The use of different methods, even within one study type (Swarm, UCLUST, CD HIT, etc.), creates very different OTUs, possibly affecting interpretation and leading to questionable reproducibility. The Population and Community Ontology (PCO) offers the semantics to clarify exactly which collection of organisms (i.e., ecological community or population) was used in an investigation. When combined with methods for standardizing observational data from the BioCollections Ontology (BCO), protocol classes from the Ontology for Biomedical investigations (OBI), and characterization of environments from the Environment Ontology (ENVO), PCO can fully describe the methods used to derive organismal or species-based (i.e. taxonomic) OTUs used for biodiversity analysis and monitoring. PCO is not well suited to describe “OTUs” based on sequence variants that may or may not map to population or individual level variation (e.g., output of some clustering algorithms). In this case, the Sequence Ontology (SO) may be more appropriate. This presentation will describe the key ontology design patterns used in the PCO and provide examples of how and when PCO and related ontologies should be used in omics research, with a focus on environmental/metagenomic sequencing applications.

Building semantics in the domain of trait data: an OBO Library approach

As the volume and diversity of digitised trait data grows with ever-increasing speed, there is a clear need to capture the knowledge which contextualises it. Many researchers are addressing similar challenges by using ontology-based approaches to represent knowledge and use it to better structure data across resources, however, there is immense variation in how and for what purpose these ontologies are built. While some approaches emphasise quick and lightweight deployment for specific projects, others spend considerable effort in creating heavy duty, finely specified semantics for a wide user base. Effectively ontologising trait data collections is likely to require a hybrid of these strategies and must also consider how to meld emerging efforts with those that have matured into well-adopted, production-oriented systems. This contribution will provide an overview of existng ontologies linked to traits, as well as the best practices used to create and develop them within the Open Biological and Biomedical Ontologies (OBO) Foundry and Library (Smith et al. 2007). Specifically, it will outline a collaborative model for future, open development, based on the domain semantics of the Ontology of Biological Attributes (OBA), the Environment Ontology (ENVO; Buttigieg et al. 2013, Buttigieg et al. 2016b), the Population and Community Ontology (PCO; Walls et al. 2014), and recent work on bridging phenotypes and environments (e.g. Thessen et al. 2015). Finally, perspectives on linking trait semantics, and hence trait data, to societal goals via OBO-aligned efforts to represent the semantics of the United Nations' Sustainable Development Agenda for 2030 (e.g. Buttigieg et al. 2016a) will be offered as a means to bridge scientific data with global socio-ecological goals.

Bootstrapping the applied ontology practice: Ontology communities, then and now

After more than two decades since the interdisciplinary field of ontology emerged for information science, it is now poised to make a huge impact. The ontological approach aims at making intended meaning explicit and computable. By enabling better human-machine understanding across complex socio-technical chains, ontologies are facilitating the vision of co-evolution of humans and their tools. Applying ontology requires a very broad spectrum of disciplines and skills, and is therefore best tackled not by individuals, but by communities of people working collaboratively. This position paper attempts to trace the brief history of ontology communities from the author's vantage point, identifying some of the key dates and key players (mostly familiar names, but some unsung heroes as well) that have made things happen for the field of applied ontology. Inspired by Doug Engelbart's bootstrap strategy, the author has developed and operated (until his recent retirement) a virtual collaborative environment (CIM3.net), on which some ontology communities (such as Ontolog and Ontology Summit) have thrived. Observing that the funding and career opportunities have changed for the better in the past few years, the author reflects on how the ontology communities of practice might leverage what they have built so far, and take applied ontology into an even better future.

A estrutura genética das entificações comunitárias

Thinkers of Heideggerian tradition such as Jean-Luc Nancy and Roberto Esposito, through concepts such as being-in-common and communitas, suggest a fundamental ontology of community (Mitsein). This ontology, however, has generated a difficulty of interpretation and operation due to the hegemonic subjectivist paradigm that anticipates the reading of these concepts. This article aims to redescribe this ontology from a referential shift: from the Heideggerian metaphysical universe to the field of “absolute immanence” (Deleuze). This shift allows structuralizing and conferring operational communication to that ontology, as well as observing new political phenomena, such as those made possible by today’s technical communication devices.

Moving the mountain: analysis of the effort required to transform comparative anatomy into computable anatomy.

The diverse phenotypes of living organisms have been described for centuries, and though they may be digitized, they are not readily available in a computable form. Using over 100 morphological studies, the Phenoscape project has demonstrated that by annotating characters with community ontology terms, links between novel species anatomy and the genes that may underlie them can be made. But given the enormity of the legacy literature, how can this largely unexploited wealth of descriptive data be rendered amenable to large-scale computation? To identify the bottlenecks, we quantified the time involved in the major aspects of phenotype curation as we annotated characters from the vertebrate phylogenetic systematics literature. This involves attaching fully computable logical expressions consisting of ontology terms to the descriptions in character-by-taxon matrices. The workflow consists of: (i) data preparation, (ii) phenotype annotation, (iii) ontology development and (iv) curation team discussions and software development feedback. Our results showed that the completion of this work required two person-years by a team of two post-docs, a lead data curator, and students. Manual data preparation required close to 13% of the effort. This part in particular could be reduced substantially with better community data practices, such as depositing fully populated matrices in public repositories. Phenotype annotation required ∼40% of the effort. We are working to make this more efficient with Natural Language Processing tools. Ontology development (40%), however, remains a highly manual task requiring domain (anatomical) expertise and use of specialized software. The large overhead required for data preparation and ontology development contributed to a low annotation rate of approximately two characters per hour, compared with 14 characters per hour when activity was restricted to character annotation. Unlocking the potential of the vast stores of morphological descriptions requires better tools for efficiently processing natural language, and better community practices towards a born-digital morphology.Database URL: http://kb.phenoscape.org

Meeting report: advancing practical applications of biodiversity ontologies

We describe the outcomes of three recent workshops aimed at advancing development of the Biological Collections Ontology (BCO), the Population and Community Ontology (PCO), and tools to annotate data using those and other ontologies. The first workshop gathered use cases to help grow the PCO, agreed upon a format for modeling challenging concepts such as ecological niche, and developed ontology design patterns for defining collections of organisms and population-level phenotypes. The second focused on mapping datasets to ontology terms and converting them to Resource Description Framework (RDF), using the BCO. To follow-up, a BCO hackathon was held concurrently with the 16th Genomics Standards Consortium Meeting, during which we converted additional datasets to RDF, developed a Material Sample Core for the Global Biodiversity Information Framework, created a Web Ontology Language (OWL) file for importing Darwin Core classes and properties into BCO, and developed a workflow for converting biodiversity data among formats.