The Universal Ontology: A Vision for Conceptual Modeling and the Semantic Web (Invited Paper)

Olivé, Antoni

doi:10.1007/978-3-319-69904-2_1

Cited by 5 publications

(6 citation statements)

References 37 publications

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“…A schema enables FAIR data when it is intuitive for others in the field, enables efficient functional use, and is standard enough that data can be compared across databases. This need for well-organized data motivates the design of chemical data structures as an active area of chemical research; projects like the Chemical Entities of Biological Interest (ChEBI), Chemical Methods Ontology (CHMO), Chemical Information Ontology (CHEMINF), Materials Design Ontology (MDO), and more are attempting to create organizational schemas for chemical data. ,− …”

Section: Chemistry’s Big Data Eramentioning

confidence: 99%

“…Given this historical background, it may seem obvious or natural to some contemporary chemists to assume that chemical ontologies should be striving for a universal ontology , that is, an ontology in which all relevant fundamental physical and chemical relationships are captured and recorded in a single comprehensive organizational scheme. This impulse would be supported by efforts to build universal ontologies presently underway in computer and information science. ,− While current chemical ontology initiatives may not explicitly aim for a universal ontology, the potential for a unified framework to organize chemical knowledge remains a compelling idea that may be held implicitly or explicitly by many chemists.…”

Section: Chemistry’s Big Data Eramentioning

confidence: 99%

See 1 more Smart Citation

Promises and Perils of Big Data: Philosophical Constraints on Chemical Ontologies

Duke,

McCoy,

Risko

et al. 2024

J. Am. Chem. Soc.

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Chemistry is experiencing a paradigm shift in the way it interacts with data. So-called "big data" are collected and used at unprecedented scales with the idea that algorithms can be designed to aid in chemical discovery. As data-enabled practices become ever more ubiquitous, chemists must consider the organization and curation of their data, especially as it is presented to both humans and increasingly intelligent algorithms. One of the most promising organizational schemes for big data is a construct termed an ontology. In data science, ontologies are systems that represent relations among objects and properties in a domain of discourse. As chemistry encounters larger and larger data sets, the ontologies that support chemical research will likewise increase in complexity, and the future of chemistry will be shaped by the choices made in developing big data chemical ontologies. How such ontologies will work should therefore be a subject of significant attention in the chemical community. Now is the time for chemists to ask questions about ontology design and use: How should chemical data be organized? What can be reasonably expected from an organizational structure? Is a universal ontology tenable? As some of these questions may be new to chemists, we recommend an interdisciplinary approach that draws on the long history of philosophers of science asking questions about the organization of scientific concepts, constructs, models, and theories. This Perspective presents insights from these long-standing studies and initiates new conversations between chemists and philosophers.■ CHEMISTRY'S BIG DATA ERA As with most sciences in the 21st century, chemistry is witnessing a seismic shift in data collection, storage, and use. The collection of large volumes of data (so-called "big data") for the purpose of using computer algorithms to aid in chemical discovery is becoming commonplace, and many experiments produce more raw data than any one scientist could review in a lifetime. Moreover, federal funding agencies now require extensive data management plans and that data be made open under FAIR (f̲ indable, a̲ ccessible, i̲ nteroperable, and r̲ eusable) 1 data principles. In the United States, the National Science Foundation, National Institutes of Health, and Department of Energy among others have begun requiring data management plans for proposal applications, 2−4 while similar efforts exist globally to promote robust and accessible research data management. 5,6 Along with funding agencies, researchers across the globe are pushing for accessible, usable, and reliably reproducible data in chemistry. 7−9 Many argue that this new era of data will transform modern chemistry from an Edisonian model of trial-and-error to data-driven and machine-enabled design through artificial intelligence (AI). 10,11 Chemical Data: Historical Background. Although the modern era of big data is still incipient, data hold an integral role in the history of chemistry (Figure 1). 12−14 Chemists have long prioritized documenting and sharing thei...

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Section: Chemistry’s Big Data Eramentioning

confidence: 99%

Section: Chemistry’s Big Data Eramentioning

confidence: 99%

Promises and Perils of Big Data: Philosophical Constraints on Chemical Ontologies

Duke,

McCoy,

Risko

et al. 2024

J. Am. Chem. Soc.

View full text Add to dashboard Cite

show abstract

“…In terms of data integration, with the objective of selecting a core ontology, we used the concept of Universal Ontology (UO) presented by Olivé [39]. By UO, Olivé means the formal specification of all the concepts that we use and share.…”

Section: Electronics 2020 X Xx For Peer Reviewmentioning

confidence: 99%

“…Concerning the ontology mapping process between the specialized domain ontologies and the core ontology, there are two kinds of mapping: vertical and horizontal [39]. Vertical mappings define the correspondences between the domain ontology and the concepts at the general level (WordNet in our approach).…”

Section: Electronics 2020 X Xx For Peer Reviewmentioning

confidence: 99%

“…In general, a correspondence can be an "equivalence" (the concepts are the same), an "IsA" (a concept is a subtype of the other), or a "disjointness" (no entity or property can be an instance of two concepts) [40]. In our approach, we proposed the use of In terms of data integration, with the objective of selecting a core ontology, we used the concept of Universal Ontology (UO) presented by Olivé [39]. By UO, Olivé means the formal specification of all the concepts that we use and share.…”

Section: Electronics 2020 X Xx For Peer Reviewmentioning

confidence: 99%

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A Machine Learning and Integration Based Architecture for Cognitive Disorder Detection Used for Early Autism Screening

Peral

Gil

Rotbei³

et al. 2020

Electronics

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About 15% of the world’s population suffers from some form of disability. In developed countries, about 1.5% of children are diagnosed with autism. Autism is a developmental disorder distinguished mainly by impairments in social interaction and communication and by restricted and repetitive behavior. Since the cause of autism is still unknown, there have been many studies focused on screening for autism based on behavioral features. Thus, the main purpose of this paper is to present an architecture focused on data integration and analytics, allowing the distributed processing of input data. Furthermore, the proposed architecture allows the identification of relevant features as well as of hidden correlations among parameters. To this end, we propose a methodology able to integrate diverse data sources, even data that are collected separately. This methodology increases the data variety which can lead to the identification of more correlations between diverse parameters. We conclude the paper with a case study that used autism data in order to validate our proposed architecture, which showed very promising results.

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A Reference Framework for Conceptual Modeling

Delcambre

Liddle

Pastor

2018

Conceptual Modeling

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The Universal Ontology: A Vision for Conceptual Modeling and the Semantic Web (Invited Paper)

Cited by 5 publications

References 37 publications

Promises and Perils of Big Data: Philosophical Constraints on Chemical Ontologies

Promises and Perils of Big Data: Philosophical Constraints on Chemical Ontologies

A Machine Learning and Integration Based Architecture for Cognitive Disorder Detection Used for Early Autism Screening

A Reference Framework for Conceptual Modeling

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