Populous: A Tool For Populating OWL Ontologies From Templates

We present Populous, a tool for gathering content with which to populate an ontology. Domain experts need to add content, that is often repetitive in its form, but without having to tackle the underlying ontological representation. Populous presents users with a table based form in which columns are constrained to take values from particular ontologies; the user can select a concept from an ontology via its meaningful label to give a value for a given entity attribute.
Populated tables are mapped to patterns that can then be used to automatically generate the ontology's content. Populous's contribution is in the knowledge gathering stage of ontology development. It separates knowledge gathering from the conceptualisation and also separates the user from the standard ontology authoring environments. As a result, Populous can allow knowledge to be gathered in a straight-forward manner that can then be used to do mass production of ontology content

USING ONTOLOGY’S IN HUMAN RESOURCE MANAGEMENT

Abstract: In today’s business environment, human resource management is playing an important role and it is present in each organization. Human resource management (HRM) is the strategic and coherent approach to the management of an organization's most valued assets - the people working in the organization. We try to design an ontology for human resource management, and we want to encompass in it all common items no matter in which area we want to apply this structure. This paper wants to propose a model for a human resource management system. This future structure will be incorporated into an ontology. Ontologies are used to capture knowledge about some domains of interest and in our case, it is used to capture knowledge about an human resource management system.Ontology, Knowledge Representation, HRM.

Identitas: A Better Way To Be Meaningless

It is often recommended that identifiers for ontology terms should be semantics-free or meaningless. In practice, ontology developers tend to use numeric identifiers, starting at 1 and working upwards. In this paper we present a critique of current ontology semantics-free identifiers; monotonically increasing numbers have a number of significant usability flaws which make them unsuitable as a default option, and we present a series of alternatives. We have provide an implementation of these alternatives which can be freely combined.Comment: 2 pages, accepted at ICBO 201

Document Navigation: Ontology or Knowledge Organization System?

Bioinformatics relies heavily on web resources for information gathering. Ontologies are being developed to fill the background knowledge needed to drive Semantic Web applications. This paper discusses how formal ontologies are not always suited for document navigation on the web. Converting ontologies into a model with looser semantics, allows cheap and rapid generation of useful knowledge systems. The message is that ontologies are not the only knowledge artefact needed; vocabularies and other classification schemes with weaker semantics have their role and are the best solution in certain circumstances

Populous: A tool for populating ontology templates

We present Populous, a tool for gathering content with which to populate an ontology. Domain experts need to add content, that is often repetitive in its form, but without having to tackle the underlying ontological representation. Populous presents users with a table based form in which columns are constrained to take values from particular ontologies; the user can select a concept from an ontology via its meaningful label to give a value for a given entity attribute. Populated tables are mapped to patterns that can then be used to automatically generate the ontology's content. Populous's contribution is in the knowledge gathering stage of ontology development. It separates knowledge gathering from the conceptualisation and also separates the user from the standard ontology authoring environments. As a result, Populous can allow knowledge to be gathered in a straight-forward manner that can then be used to do mass production of ontology content.Comment: in Adrian Paschke, Albert Burger begin_of_the_skype_highlighting end_of_the_skype_highlighting, Andrea Splendiani, M. Scott Marshall, Paolo Romano: Proceedings of the 3rd International Workshop on Semantic Web Applications and Tools for the Life Sciences, Berlin,Germany, December 8-10, 201

SPARQL-enabled identifier conversion with Identifiers.org

Motivation: On the semantic web, in life sciences in particular, data is often distributed via multiple resources. Each of these sources is likely to use their own International Resource Identifier for conceptually the same resource or database record. The lack of correspondence between identifiers introduces a barrier when executing federated SPARQL queries across life science data. Results: We introduce a novel SPARQL-based service to enable on-the-fly integration of life science data. This service uses the identifier patterns defined in the Identifiers.org Registry to generate a plurality of identifier variants, which can then be used to match source identifiers with target identifiers. We demonstrate the utility of this identifier integration approach by answering queries across major producers of life science Linked Data. Availability and implementation: The SPARQL-based identifier conversion service is available without restriction at http://identifiers.org/services/sparql. Contact: [email protected]

Tracking workflow execution with TavernaProv

Apache Taverna is a scientific workflow system for combining web services and local tools. Taverna records provenance of workflow runs, intermediate values and user interactions, both as an aid for debugging while designing the workflow, but also as a record for later reproducibility and comparison. Taverna also records provenance of the evolution of the workflow definition (including a chain of wasDerivedFrom relations), attributions and annotations; for brevity we here focus on how Taverna's workflow run provenance extends PROV and is embedded with Research Objects.Document id: https://github.com/stain/2016-provweek-tavernaprov

OLS Client and OLS Dialog: Open source tools to annotate public omics datasets

The availability of user‐friendly software to annotate biological datasets and experimental details is becoming essential in data management practices, both in local storage systems and in public databases. The Ontology Lookup Service (OLS, http://www.ebi.ac.uk/ols) is a popular centralized service to query, browse and navigate biomedical ontologies and controlled vocabularies. Recently, the OLS framework has been completely redeveloped (version 3.0), including enhancements in the data model, like the added support for Web Ontology Language based ontologies, among many other improvements. However, the new OLS is not backwards compatible and new software tools are needed to enable access to this widely used framework now that the previous version is no longer available. We here present the OLS Client as a free, open‐source Java library to retrieve information from the new version of the OLS. It enables rapid tool creation by providing a robust, pluggable programming interface and common data model to programmatically access the OLS. The library has already been integrated and is routinely used by several bioinformatics resources and related data annotation tools. Secondly, we also introduce an updated version of the OLS Dialog (version 2.0), a Java graphical user interface that can be easily plugged into Java desktop applications to access the OLS. The software and related documentation are freely available at https://github.com/PRIDE-Utilities/ols-client and https://github.com/PRIDE-Toolsuite/ols-dialog.publishedVersio

The Monarch Initiative in 2019: an integrative data and analytic platform connecting phenotypes to genotypes across species.

In biology and biomedicine, relating phenotypic outcomes with genetic variation and environmental factors remains a challenge: patient phenotypes may not match known diseases, candidate variants may be in genes that haven\u27t been characterized, research organisms may not recapitulate human or veterinary diseases, environmental factors affecting disease outcomes are unknown or undocumented, and many resources must be queried to find potentially significant phenotypic associations. The Monarch Initiative (https://monarchinitiative.org) integrates information on genes, variants, genotypes, phenotypes and diseases in a variety of species, and allows powerful ontology-based search. We develop many widely adopted ontologies that together enable sophisticated computational analysis, mechanistic discovery and diagnostics of Mendelian diseases. Our algorithms and tools are widely used to identify animal models of human disease through phenotypic similarity, for differential diagnostics and to facilitate translational research. Launched in 2015, Monarch has grown with regards to data (new organisms, more sources, better modeling); new API and standards; ontologies (new Mondo unified disease ontology, improvements to ontologies such as HPO and uPheno); user interface (a redesigned website); and community development. Monarch data, algorithms and tools are being used and extended by resources such as GA4GH and NCATS Translator, among others, to aid mechanistic discovery and diagnostics