55 research outputs found
WebProt\'eg\'e: A Cloud-Based Ontology Editor
We present WebProt\'eg\'e, a tool to develop ontologies represented in the
Web Ontology Language (OWL). WebProt\'eg\'e is a cloud-based application that
allows users to collaboratively edit OWL ontologies, and it is available for
use at https://webprotege.stanford.edu. WebProt\'ege\'e currently hosts more
than 68,000 OWL ontology projects and has over 50,000 user accounts. In this
paper, we detail the main new features of the latest version of WebProt\'eg\'e
Discovering Beaten Paths in Collaborative Ontology-Engineering Projects using Markov Chains
Biomedical taxonomies, thesauri and ontologies in the form of the
International Classification of Diseases (ICD) as a taxonomy or the National
Cancer Institute Thesaurus as an OWL-based ontology, play a critical role in
acquiring, representing and processing information about human health. With
increasing adoption and relevance, biomedical ontologies have also
significantly increased in size. For example, the 11th revision of the ICD,
which is currently under active development by the WHO contains nearly 50,000
classes representing a vast variety of different diseases and causes of death.
This evolution in terms of size was accompanied by an evolution in the way
ontologies are engineered. Because no single individual has the expertise to
develop such large-scale ontologies, ontology-engineering projects have evolved
from small-scale efforts involving just a few domain experts to large-scale
projects that require effective collaboration between dozens or even hundreds
of experts, practitioners and other stakeholders. Understanding how these
stakeholders collaborate will enable us to improve editing environments that
support such collaborations. We uncover how large ontology-engineering
projects, such as the ICD in its 11th revision, unfold by analyzing usage logs
of five different biomedical ontology-engineering projects of varying sizes and
scopes using Markov chains. We discover intriguing interaction patterns (e.g.,
which properties users subsequently change) that suggest that large
collaborative ontology-engineering projects are governed by a few general
principles that determine and drive development. From our analysis, we identify
commonalities and differences between different projects that have implications
for project managers, ontology editors, developers and contributors working on
collaborative ontology-engineering projects and tools in the biomedical domain.Comment: Published in the Journal of Biomedical Informatic
the rare bone disorders use case
Background Lately, ontologies have become a fundamental building block in the
process of formalising and storing complex biomedical information. The
community-driven ontology curation process, however, ignores the possibility
of multiple communities building, in parallel, conceptualisations of the same
domain, and thus providing slightly different perspectives on the same
knowledge. The individual nature of this effort leads to the need of a
mechanism to enable us to create an overarching and comprehensive overview of
the different perspectives on the domain knowledge. Results We introduce an
approach that enables the loose integration of knowledge emerging from diverse
sources under a single coherent interoperable resource. To accurately track
the original knowledge statements, we record the provenance at very granular
levels. We exemplify the approach in the rare bone disorders domain by
proposing the Rare Bone Disorders Ontology (RBDO). Using RBDO, researchers are
able to answer queries, such as: “What phenotypes describe a particular
disorder and are common to all sources?” or to understand similarities between
disorders based on divergent groupings (classifications) provided by the
underlying sources
Enabling Web-scale data integration in biomedicine through Linked Open Data
The biomedical data landscape is fragmented with several isolated, heterogeneous data and knowledge sources, which use varying formats, syntaxes, schemas, and entity notations, existing on the Web. Biomedical researchers face severe logistical and technical challenges to query, integrate, analyze, and visualize data from multiple diverse sources in the context of available biomedical knowledge. Semantic Web technologies and Linked Data principles may aid toward Web-scale semantic processing and data integration in biomedicine. The biomedical research community has been one of the earliest adopters of these technologies and principles to publish data and knowledge on the Web as linked graphs and ontologies, hence creating the Life Sciences Linked Open Data (LSLOD) cloud. In this paper, we provide our perspective on some opportunities proffered by the use of LSLOD to integrate biomedical data and knowledge in three domains: (1) pharmacology, (2) cancer research, and (3) infectious diseases. We will discuss some of the major challenges that hinder the wide-spread use and consumption of LSLOD by the biomedical research community. Finally, we provide a few technical solutions and insights that can address these challenges. Eventually, LSLOD can enable the development of scalable, intelligent infrastructures that support artificial intelligence methods for augmenting human intelligence to achieve better clinical outcomes for patients, to enhance the quality of biomedical research, and to improve our understanding of living systems
A More Decentralized Vision for Linked Data
We claim that ten years into Linked Data there are still many unresolved challenges towards arriving at a truly machine-readable and decentralized Web of data. With a focus on the the biomedical domain, currently, one of the most promising adopters of Linked Data, we highlight and exemplify key technical and non-technical challenges to the success of Linked Data, and we outline potential solution strategies
A More Decentralized Vision for Linked Data
In this deliberately provocative position paper, we claim that ten years into Linked Data there are still (too?) many unresolved challenges towards arriving at a truly machine-readable and decentralized Web of data. We take a deeper look at the biomedical domain - currently, one of the most promising "adopters" of Linked Data - if we believe the ever-present "LOD cloud" diagram. Herein, we try to highlight and exemplify key technical and non-technical challenges to the success of LOD, and we outline potential solution strategies. We hope that this paper will serve as a discussion basis for a fresh start towards more actionable, truly decentralized Linked Data, and as a call to the community to join forces.Series: Working Papers on Information Systems, Information Business and Operation
Use of ontology structure and Bayesian models to aid the crowdsourcing of ICD-11 sanctioning rules
M-ATOLL: A Framework for the Lexicalization of Ontologies in Multiple Languages
Walter S, Unger C, Cimiano P. M-ATOLL: A Framework for the Lexicalization of Ontologies in Multiple Languages. In: Mika P, Tudorache T, Bernstein A, et al., eds. The Semantic Web – ISWC 2014. Lecture Notes in Computer Science. Vol 8796. Cham: Springer International Publishing; 2014: 472-486.Many tasks in which a system needs to mediate between natural language expressions and elements of a vocabulary in an ontology or dataset require knowledge about how the elements of the vocabulary (i.e. classes, properties, and individuals) are expressed in natural language. In a multilingual setting, such knowledge is needed for each of the supported languages. In this paper we present M-ATOLL, a framework for automatically inducing ontology lexica in multiple languages on the basis of a multilingual corpus. The framework exploits a set of language-specific dependency patterns which are formalized as SPARQL queries and run over a parsed corpus. We have instantiated the system for two languages: German and English. We evaluate it in terms of precision, recall and F-measure for English and German by comparing an automatically induced lexicon to manually constructed ontology lexica for DBpedia. In particular, we investigate the contribution of each single dependency pattern and perform an analysis of the impact of different parameters
A Simple Standard for Sharing Ontological Mappings (SSSOM).
Despite progress in the development of standards for describing and exchanging scientific information, the lack of easy-to-use standards for mapping between different representations of the same or similar objects in different databases poses a major impediment to data integration and interoperability. Mappings often lack the metadata needed to be correctly interpreted and applied. For example, are two terms equivalent or merely related? Are they narrow or broad matches? Or are they associated in some other way? Such relationships between the mapped terms are often not documented, which leads to incorrect assumptions and makes them hard to use in scenarios that require a high degree of precision (such as diagnostics or risk prediction). Furthermore, the lack of descriptions of how mappings were done makes it hard to combine and reconcile mappings, particularly curated and automated ones. We have developed the Simple Standard for Sharing Ontological Mappings (SSSOM) which addresses these problems by: (i) Introducing a machine-readable and extensible vocabulary to describe metadata that makes imprecision, inaccuracy and incompleteness in mappings explicit. (ii) Defining an easy-to-use simple table-based format that can be integrated into existing data science pipelines without the need to parse or query ontologies, and that integrates seamlessly with Linked Data principles. (iii) Implementing open and community-driven collaborative workflows that are designed to evolve the standard continuously to address changing requirements and mapping practices. (iv) Providing reference tools and software libraries for working with the standard. In this paper, we present the SSSOM standard, describe several use cases in detail and survey some of the existing work on standardizing the exchange of mappings, with the goal of making mappings Findable, Accessible, Interoperable and Reusable (FAIR). The SSSOM specification can be found at http://w3id.org/sssom/spec. Database URL: http://w3id.org/sssom/spec
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