FOOPS!: An Ontology Pitfall Scanner for the FAIR principles

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This document describes FOOPS, an ontology validation service that checks ontologies for adherence to the FAIR principles. FOOPS tests ontologies against criteria related to findability, accessibility, interoperability, and reusability. It provides explanations for test failures to help users improve their ontologies. FOOPS validation results include an overall FAIRness score and coverage of FAIR categories to assess ontology quality, though there is no single threshold for what makes an ontology fully FAIR. The document demonstrates FOOPS and lists the types of tests it supports under each FAIR category. It invites feedback to help further improve FOOPS.

Engineering

FOOPS! An Ontology Pitfall Validator for the FAIR principles. DBpedia Day, 9th September, 2021
The rise of the FAIR Data principles
2
Other guidelines:
● Guidelines for Transparency and
Openness Promotion (TOP) [2]
● Reproducibility Enhancement
Principles (REP) [3]
● ...
Data (initially) [1]
Research Software
Methods
Semantic artefacts
[1] Wilkinson, M., Dumontier, M., Aalbersberg, I. et al. The FAIR Guiding Principles for scientific data
management and stewardship. Sci Data 3, 160018 (2016). https://doi.org/10.1038/sdata.2016.18
[2] https://www.cos.io/initiatives/top-guidelines
[3] Stodden, V et al Enhancing reproducibility for computational methods
https://www.science.org/lookup/doi/10.1126/science.aah6168

FOOPS! An Ontology Pitfall Validator for the FAIR principles. DBpedia Day, 9th September, 2021
FAIR in not new: Background in the Semantic Web
3
▪ Linked Data principles [1] and 5-star ranking
▪ LD Principles adapted to ontologies [2] [3]
▪ Best practices for accessing vocabularies [4]
▪ Tutorials for publishing vocabularies [5]
▪ FAIR
How does all come together?
☆ Available
☆☆ Machine readable
☆☆☆ Open format
☆☆☆☆ Use standards
☆☆☆☆☆ Link to other resources
1. Use URIs
2. HTTP URIs
3. Resolve and provide useful info
4. Link to other URIs
[1] https://www.w3.org/DesignIssues/LinkedData.html
[2] https://bvatant.blogspot.com/2012/02/is-your-linked-data-vocabulary-5-star_9588.html
[3] Janowicz, K., Hitzler, P., Adams, B., Kolas, D., Vardeman, I., et al.: Five stars of linked data vocabulary use. Semantic Web 5(3), 173–176 (2014)
[4] Best Practice Recipes for Publishing RDF Vocabularies. https://www.w3.org/TR/swbp-vocab-pub/
[5] Garijo, Daniel (2013): How to (properly) publish a vocabulary or ontology in the web. figshare. Journal contribution. https://doi.org/10.6084/m9.figshare.881824.v1

FOOPS! An Ontology Pitfall Validator for the FAIR principles. DBpedia Day, 9th September, 2021
▪ Validation service inspired by OOPS! (OntOlogy Pitfall Scanner)
▪ Designed to guide users
o Tests have an explanation
o Tests indicate potential errors
▪ Practical
o Based on years of ontology engineering practices at UPM
▪ Aligned to FAIR
FOOPS!: A Pitfall Scanner for the FAIR principles
4
Live demo: https://w3id.org/foops/

FOOPS! An Ontology Pitfall Validator for the FAIR principles. DBpedia Day, 9th September, 2021
FOOPS! in a nutshell
5
Enter a ontology URI

FOOPS! An Ontology Pitfall Validator for the FAIR principles. DBpedia Day, 9th September, 2021
FOOPS!: Getting the full report
6
Ontology metadata summary
FAIRness coverage by category
FAIRness overall score. Note: this
may be a quality indicator, but
there is no deﬁned threshold for
FAIRness.
FAIR Category
Check
Check description
Check coverage
Check explanation

FOOPS! An Ontology Pitfall Validator for the FAIR principles. DBpedia Day, 9th September, 2021
Summary of supported tests
7
Findable
● Ontology URI is resolvable
● Ontology URI is persistent
● Version IRI exists (and resolves)
● Ontology id is ontology URI
● Minimum metadata is available (e.g.,
title, description, version info, etc.)
● Ontology preﬁx is in registry
● Ontology is in registry
Accessible
● Ontology is available in RDF/HTML
(content negotiation)
● Ontology is in a registry (repeated)
● Ontology is URI is deﬁned in
HTTP/HTTPS
Interoperable
● Ontology is at least available in RDF
● Ontology reuses known vocabularies
for declaring metadata (DC, Schema,
PROV, etc.)
● Ontology extends other vocabularies
Reusable
● HTML representation of the ontology
exists
● Extensive metadata is provided with
the ontology
● Labels and descriptions exist for all
terms
● License is provided and resolvable
● Metadata includes provenance
information

FOOPS! An Ontology Pitfall Validator for the FAIR principles. DBpedia Day, 9th September, 2021
Questions?
8
Help us improve FOOPS!
Errors? (please be gentle)
New tests?
Suggestions?
Drop us a message:
foops@delicias.dia.ﬁ.upm.es
Twitter: @OOPSoeg
https://w3id.org/foops/

La presentazione è stata fatta in occasione di corsi di formazione presso la rRegione Campania organizzati da ForumPA. La presentazione si suddivide in due parti principali: una descrizione estesa di OntoPiA, la rete di ontologie e vocabolari controllati della pubblica amministrazione italiana, e i suoi principi di modellazione e una descrizione del processo di produzione di Linked (Open) Data mediante lo standard R2RML

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dgarijo

Scientific software is crucial for understanding, reusing and reproducing results in computational sciences. Software is often stored in code repositories, which may contain human readable instructions necessary to use it and set it up. However, a significant amount of time is usually required to understand how to invoke a software component, prepare data in the format it requires, and use it in combination with other software. In this presentation we introduce OKG-Soft, an open knowledge graph that describes scientific software in a machine readable manner. OKG-Soft includes: 1) an ontology designed to describe software and the specific data formats it uses; 2) an approach to publish software metadata as an open knowledge graph, linked to other Web of Data objects; and 3) a framework to annotate, query, explore and curate scientific software metadata.

Towards Human-Guided Machine Learning - IUI 2019

dgarijo

Automated Machine Learning (AutoML) systems are emerging that automatically search for possible solutions from a large space of possible kinds of models. Although fully automated machine learning is appropriate for many applications, users often have knowledge that supplements and constraints the available data and solutions. This paper proposes human-guided machine learning (HGML) as a hybrid approach where a user interacts with an AutoML system and tasks it to explore different problem settings that reflect the user’s knowledge about the data available. We present: 1) a task analysis of HGML that shows the tasks that a user would want to carry out, 2) a characterization of two scientific publications, one in neuroscience and one in political science, in terms of how the authors would search for solutions using an AutoML system, 3) requirements for HGML based on those characterizations, and 4) an assessment of existing AutoML systems in terms of those requirements.

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Traditional approaches to ontology development have a large lapse between the time when a user using the ontology has found a need to extend it and the time when it does get extended. For scientists, this delay can be weeks or months and can be a significant barrier for adoption. We present a new approach to ontology development and data annotation enabling users to add new metadata properties on the fly as they describe their datasets, creating terms that can be immediately adopted by others and eventually become standardized. This approach combines a traditional, consensus-based approach to ontology development, and a crowdsourced approach where ex-pert users (the crowd) can dynamically add terms as needed to support their work. We have implemented this approach as a socio-technical system that includes: 1) a crowdsourcing platform to support metadata annotation and addition of new terms, 2) a range of social editorial processes to make standardization decisions for those new terms, and 3) a framework for ontology revision and updates to the metadata created with the previous version of the ontology. We present a prototype implementation for the paleoclimate community, the Linked Earth Framework, currently containing 700 datasets and engaging over 50 active contributors. Users exploit the platform to do science while extending the metadata vocabulary, thereby producing useful and practical metadata

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dgarijo

WIDOCO is a WIzard for DOCumenting Ontologies that guides users through the documentation process of their vocabularies. Given an RDF vocabulary, WIDOCO detects missing vocabulary metadata and creates a documentation with diagrams, human readable descriptions of the ontology terms and a summary of changes with respect to previous versions of the ontology. The documentation consists on a set of linked enriched HTML pages that can be further extended by end users. WIDOCO is open source and builds on well established Semantic Web tools. So far, it has been used to document more than one hundred ontologies in different domains.

Towards Automating Data Narratives

dgarijo

We propose a new area of research on automating data narratives. Data narratives are containers of information about computationally generated research findings. They have three major components: 1) A record of events, that describe a new result through a workflow and/or provenance of all the computations executed; 2) Persistent entries for key entities involved for data, software versions, and workflows; 3) A set of narrative accounts that are automatically generated human-consumable renderings of the record and entities and can be included in a paper. Different narrative accounts can be used for different audiences with different content and details, based on the level of interest or expertise of the reader. Data narratives can make science more transparent and reproducible, because they ensure that the text description of the computational experiment reflects with high fidelity what was actually done. Data narratives can be incorporated in papers, either in the methods section or as supplementary materials. We introduce DANA, a prototype that illustrates how to generate data narratives automatically, and describe the information it uses from the computational records. We also present a formative evaluation of our approach and discuss potential uses of automated data narratives.

Automated Hypothesis Testing with Large Scale Scientific Workflows

dgarijo

(Credit to Varun Ratnakar and Yolanda Gil). The automation of important aspects of scientific data analysis would significantly accelerate the pace of science and innovation. Although important aspects of data analysis can be automated, the hypothesize-test-evaluate discovery cycle is largely carried out by hand by researchers. This introduces a significant human bottleneck, which is inefficient and can lead to erroneous and incomplete explorations. We introduce a novel approach to automate the hypothesize-test-evaluate discovery cycle with an intelligent system that a scientist can task to test hypotheses of interest in a data repository. Our approach captures three types of data analytics knowledge: 1) common data analytic methods represented as semantic workflows; 2) meta-analysis methods that aggregate those results, represented as meta-workflows; and 3) data analysis strategies that specify for a type of hypothesis what data and methods to use, represented as lines of inquiry. Given a hypothesis specified by a scientist, appropriate lines of inquiry are triggered, which lead to retrieving relevant datasets, running relevant workflows on that data, and finally running meta-workflows on workflow results. The scientist is then presented with a level of confidence on the initial hypothesis (or a revised hypothesis) based on the data and methods applied. We have implemented this approach in the DISK system, and applied it to multi-omics data analysis.

OntoSoft: A Distributed Semantic Registry for Scientific Software

dgarijo

Credit to Yolanda Gil. OntoSoft is a distributed semantic registry for scientific software. This paper describes three major novel contributions of OntoSoft: 1) a software metadata registry designed for scientists, 2) a distributed approach to software registries that targets communities of interest, and 3) metadata crowdsourcing through access control. Software metadata is organized using the OntoSoft ontology along six dimensions that matter to scientists: identify software, understand and assess software, execute software, get support for the software, do research with the software, and update the software. OntoSoft is a distributed registry where each site is owned and maintained by a community of interest, with a distributed semantic query capability that allows users to search across all sites. The registry has metadata crowdsourcing capabilities, supported through access control so that software authors can allow others to expand on specific metadata properties.

OEG tools for supporting Ontology Engineering

dgarijo

Software Metadata: Describing "dark software" in GeoSciences

dgarijo

Credit to Yolanda Gil. In this talk I provide an overview of the current state of the art for software description in geosciences, along with our approach to facilitate this task in OntoSoft, a distributed semantic registry for scientific software. Three key aspects of OntoSoft are: a software metadata ontology designed for scientists, a distributed approach to software registries that targets communities of interest, and metadata crowdsourcing through access control. Software metadata is organized using the OntoSoft ontology, designed to support scientists to share, document, and reuse software, and organized along six dimensions: identify software, understand and assess software, execute software, get support for the software, do research with the software, and update the software.

Reproducibility Using Semantics: An Overview

dgarijo

Overview of the different approaches for addressing reproducibilities (using semantics) in laboratory protocols, workflow description and publication and workflow infrastructure. Furthermore, Research Objects are introduced as a means to capture the context and annotations of scientific experiments, together with the privacy and IPR concerns that may arise. This presentation was presented in Dagstuhl Seminar 16041: http://www.dagstuhl.de/16041

PhD Thesis: Mining abstractions in scientific workflows

dgarijo

Slides of the presentation for my PhD dissertation. I strongly recommend downloading the slides, as they have animations that are easier to see in power point. The abstract of the thesis is as follows: "Scientific workflows have been adopted in the last decade to represent the computational methods used in in silico scientific experiments and their associated research products. Scientific workflows have demonstrated to be useful for sharing and reproducing scientific experiments, allowing scientists to visualize, debug and save time when re-executing previous work. However, scientific workflows may be difficult to understand and reuse. The large amount of available workflows in repositories, together with their heterogeneity and lack of documentation and usage examples may become an obstacle for a scientist aiming to reuse the work from other scientists. Furthermore, given that it is often possible to implement a method using different algorithms or techniques, seemingly disparate workflows may be related at a higher level of abstraction, based on their common functionality. In this thesis we address the issue of reusability and abstraction by exploring how workflows relate to one another in a workflow repository, mining abstractions that may be helpful for workflow reuse. In order to do so, we propose a simple model for representing and relating workflows and their executions, we analyze the typical common abstractions that can be found in workflow repositories, we explore the current practices of users regarding workflow reuse and we describe a method for discovering useful abstractions for workflows based on existing graph mining techniques. Our results expose the common abstractions and practices of users in terms of workflow reuse, and show how our proposed abstractions have potential to become useful for users designing new workflows".

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FOOPS!: An Ontology Pitfall Scanner for the FAIR principles

1. FOOPS! An Ontology Pitfall Scanner for the FAIR principles Daniel Garijo, Oscar Corcho, María Poveda-Villalón Ontology Engineering Group, Universidad Politécnica de Madrid, Spain DBpedia Day - Semantics 21 daniel.garijo@upm.es @dgarijov

2. FOOPS! An Ontology Pitfall Validator for the FAIR principles. DBpedia Day, 9th September, 2021 The rise of the FAIR Data principles 2 Other guidelines: ● Guidelines for Transparency and Openness Promotion (TOP) [2] ● Reproducibility Enhancement Principles (REP) [3] ● ... Data (initially) [1] Research Software Methods Semantic artefacts [1] Wilkinson, M., Dumontier, M., Aalbersberg, I. et al. The FAIR Guiding Principles for scientific data management and stewardship. Sci Data 3, 160018 (2016). https://doi.org/10.1038/sdata.2016.18 [2] https://www.cos.io/initiatives/top-guidelines [3] Stodden, V et al Enhancing reproducibility for computational methods https://www.science.org/lookup/doi/10.1126/science.aah6168

3. FOOPS! An Ontology Pitfall Validator for the FAIR principles. DBpedia Day, 9th September, 2021 FAIR in not new: Background in the Semantic Web 3 ▪ Linked Data principles [1] and 5-star ranking ▪ LD Principles adapted to ontologies [2] [3] ▪ Best practices for accessing vocabularies [4] ▪ Tutorials for publishing vocabularies [5] ▪ FAIR How does all come together? ☆ Available ☆☆ Machine readable ☆☆☆ Open format ☆☆☆☆ Use standards ☆☆☆☆☆ Link to other resources 1. Use URIs 2. HTTP URIs 3. Resolve and provide useful info 4. Link to other URIs [1] https://www.w3.org/DesignIssues/LinkedData.html [2] https://bvatant.blogspot.com/2012/02/is-your-linked-data-vocabulary-5-star_9588.html [3] Janowicz, K., Hitzler, P., Adams, B., Kolas, D., Vardeman, I., et al.: Five stars of linked data vocabulary use. Semantic Web 5(3), 173–176 (2014) [4] Best Practice Recipes for Publishing RDF Vocabularies. https://www.w3.org/TR/swbp-vocab-pub/ [5] Garijo, Daniel (2013): How to (properly) publish a vocabulary or ontology in the web. figshare. Journal contribution. https://doi.org/10.6084/m9.figshare.881824.v1

4. FOOPS! An Ontology Pitfall Validator for the FAIR principles. DBpedia Day, 9th September, 2021 ▪ Validation service inspired by OOPS! (OntOlogy Pitfall Scanner) ▪ Designed to guide users o Tests have an explanation o Tests indicate potential errors ▪ Practical o Based on years of ontology engineering practices at UPM ▪ Aligned to FAIR FOOPS!: A Pitfall Scanner for the FAIR principles 4 Live demo: https://w3id.org/foops/

5. FOOPS! An Ontology Pitfall Validator for the FAIR principles. DBpedia Day, 9th September, 2021 FOOPS! in a nutshell 5 Enter a ontology URI

6. FOOPS! An Ontology Pitfall Validator for the FAIR principles. DBpedia Day, 9th September, 2021 FOOPS!: Getting the full report 6 Ontology metadata summary FAIRness coverage by category FAIRness overall score. Note: this may be a quality indicator, but there is no deﬁned threshold for FAIRness. FAIR Category Check Check description Check coverage Check explanation

7. FOOPS! An Ontology Pitfall Validator for the FAIR principles. DBpedia Day, 9th September, 2021 Summary of supported tests 7 Findable ● Ontology URI is resolvable ● Ontology URI is persistent ● Version IRI exists (and resolves) ● Ontology id is ontology URI ● Minimum metadata is available (e.g., title, description, version info, etc.) ● Ontology preﬁx is in registry ● Ontology is in registry Accessible ● Ontology is available in RDF/HTML (content negotiation) ● Ontology is in a registry (repeated) ● Ontology is URI is deﬁned in HTTP/HTTPS Interoperable ● Ontology is at least available in RDF ● Ontology reuses known vocabularies for declaring metadata (DC, Schema, PROV, etc.) ● Ontology extends other vocabularies Reusable ● HTML representation of the ontology exists ● Extensive metadata is provided with the ontology ● Labels and descriptions exist for all terms ● License is provided and resolvable ● Metadata includes provenance information

8. FOOPS! An Ontology Pitfall Validator for the FAIR principles. DBpedia Day, 9th September, 2021 Questions? 8 Help us improve FOOPS! Errors? (please be gentle) New tests? Suggestions? Drop us a message: foops@delicias.dia.ﬁ.upm.es Twitter: @OOPSoeg https://w3id.org/foops/

FOOPS!: An Ontology Pitfall Scanner for the FAIR principles

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FOOPS!: An Ontology Pitfall Scanner for the FAIR principles