20,657 research outputs found
Ontology (Science)
Increasingly, in data-intensive areas of the life sciences, experimental results are being described in algorithmically useful ways with the help of ontologies. Such ontologies are authored and maintained by scientists to support the retrieval, integration and analysis of their data. The proposition to be defended here is that ontologies of this type – the Gene Ontology (GO) being the most conspicuous example – are a _part of science_. Initial evidence for the truth of this proposition (which some will find self-evident) is the increasing recognition of the importance of empirically-based methods of evaluation to the ontology develop¬ment work being undertaken in support of scientific research. Ontologies created by scientists must, of course, be associated with implementations satisfying the requirements of software engineering. But the ontologies are not themselves engineering artifacts, and to conceive them as such brings grievous consequences. Rather, ontologies such as the GO are in different respects comparable to scientific theories, to scientific databases, and to scientific journal publications. Such a view implies a new conception of what is involved in the author¬ing, maintenance and application of ontologies in scientific contexts, and therewith also a new approach to the evaluation of ontologies and to the training of ontologists
The evaluation of ontologies: Editorial review vs. democratic ranking
Increasingly, the high throughput technologies used by biomedical researchers are bringing about a situation in which large bodies of data are being described using controlled structured vocabulariesâalso known as ontologiesâin order to support the integration and analysis of this data. Annotation of data by means of ontologies is already contributing in significant ways to the cumulation of scientific knowledge and, prospectively, to the applicability of cross-domain algorithmic reasoning in support of scientific advance. This very success, however, has led to a proliferation of ontologies of varying scope and quality. We define one strategy for achieving quality assurance of ontologiesâa plan of action already adopted by a large community of collaborating ontologistsâwhich consists in subjecting ontologies to a process of peer review analogous to that which is applied to scientific journal articles
The Foundational Model of Anatomy Ontology
Anatomy is the structure of biological organisms. The term also denotes the scientific
discipline devoted to the study of anatomical entities and the structural and
developmental relations that obtain among these entities during the lifespan of an
organism. Anatomical entities are the independent continuants of biomedical reality on
which physiological and disease processes depend, and which, in response to etiological
agents, can transform themselves into pathological entities. For these reasons, hard copy
and in silico information resources in virtually all fields of biology and medicine, as a
rule, make extensive reference to anatomical entities. Because of the lack of a
generalizable, computable representation of anatomy, developers of computable
terminologies and ontologies in clinical medicine and biomedical research represented
anatomy from their own more or less divergent viewpoints. The resulting heterogeneity
presents a formidable impediment to correlating human anatomy not only across
computational resources but also with the anatomy of model organisms used in
biomedical experimentation. The Foundational Model of Anatomy (FMA) is being
developed to fill the need for a generalizable anatomy ontology, which can be used and
adapted by any computer-based application that requires anatomical information.
Moreover it is evolving into a standard reference for divergent views of anatomy and a
template for representing the anatomy of animals. A distinction is made between the FMA
ontology as a theory of anatomy and the implementation of this theory as the FMA
artifact. In either sense of the term, the FMA is a spatial-structural ontology of the
entities and relations which together form the phenotypic structure of the human
organism at all biologically salient levels of granularity. Making use of explicit
ontological principles and sound methods, it is designed to be understandable by human
beings and navigable by computers. The FMAâs ontological structure provides for
machine-based inference, enabling powerful computational tools of the future to reason
with biomedical data
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A platform for semantic web studies
The Semantic Web can be seen as a large, heterogeneous network of ontologies and semantic documents. Characterizing these ontologies, the way they relate and the way they are organized can help in better understanding how knowledge is produced and published online. It also provides new ways to explore and exploit this large collection of ontologies. In this paper, we present the foundation of a research platform for characterizing the Semantic Web, relying on the collection of ontologies and the functionalities provided by the Watson Semantic Web search engine. We more specifically focus on formalizing and monitoring relationships between ontologies online, considering a variety of different relations (similarity, versioning, agreement, modularity) and how they can help us obtaining meaningful overviews of the current state of the Semantic Web
ArCo: the Italian Cultural Heritage Knowledge Graph
ArCo is the Italian Cultural Heritage knowledge graph, consisting of a
network of seven vocabularies and 169 million triples about 820 thousand
cultural entities. It is distributed jointly with a SPARQL endpoint, a software
for converting catalogue records to RDF, and a rich suite of documentation
material (testing, evaluation, how-to, examples, etc.). ArCo is based on the
official General Catalogue of the Italian Ministry of Cultural Heritage and
Activities (MiBAC) - and its associated encoding regulations - which collects
and validates the catalogue records of (ideally) all Italian Cultural Heritage
properties (excluding libraries and archives), contributed by CH administrators
from all over Italy. We present its structure, design methods and tools, its
growing community, and delineate its importance, quality, and impact
Some Issues on Ontology Integration
The word integration has been used with different
meanings in the ontology field. This article
aims at clarifying the meaning of the word âintegrationâ
and presenting some of the relevant work
done in integration. We identify three meanings of
ontology âintegrationâ: when building a new ontology
reusing (by assembling, extending, specializing
or adapting) other ontologies already available;
when building an ontology by merging several
ontologies into a single one that unifies all of
them; when building an application using one or
more ontologies. We discuss the different meanings
of âintegrationâ, identify the main characteristics
of the three different processes and proposethree words to distinguish among those meanings:integration, merge and use
The OBO Foundry: Coordinated Evolution of Ontologies to Support Biomedical Data Integration
The value of any kind of data is greatly enhanced when it exists in a form that allows it to be integrated with other data. One approach to integration is through the annotation of multiple bodies of data using common controlled vocabularies or âontologiesâ. Unfortunately, the very success of this approach has led to a proliferation of ontologies, which itself creates obstacles to integration. The Open Biomedical Ontologies (OBO) consortium has set in train a strategy to overcome this problem. Existing OBO ontologies, including the Gene Ontology, are undergoing a process of coordinated reform, and new ontologies being created, on the basis of an evolving set of shared principles governing ontology development. The result is an expanding family of ontologies designed to be interoperable, logically well-formed, and to incorporate accurate representations of biological reality. We describe the OBO Foundry initiative, and provide guidelines for those who might wish to become involved in the future
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