7 research outputs found
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
HypTrails: A Bayesian Approach for Comparing Hypotheses About Human Trails on the Web
When users interact with the Web today, they leave sequential digital trails
on a massive scale. Examples of such human trails include Web navigation,
sequences of online restaurant reviews, or online music play lists.
Understanding the factors that drive the production of these trails can be
useful for e.g., improving underlying network structures, predicting user
clicks or enhancing recommendations. In this work, we present a general
approach called HypTrails for comparing a set of hypotheses about human trails
on the Web, where hypotheses represent beliefs about transitions between
states. Our approach utilizes Markov chain models with Bayesian inference. The
main idea is to incorporate hypotheses as informative Dirichlet priors and to
leverage the sensitivity of Bayes factors on the prior for comparing hypotheses
with each other. For eliciting Dirichlet priors from hypotheses, we present an
adaption of the so-called (trial) roulette method. We demonstrate the general
mechanics and applicability of HypTrails by performing experiments with (i)
synthetic trails for which we control the mechanisms that have produced them
and (ii) empirical trails stemming from different domains including website
navigation, business reviews and online music played. Our work expands the
repertoire of methods available for studying human trails on the Web.Comment: Published in the proceedings of WWW'1
Étude de l'évolution du modèle de l'utilisateur des systèmes de construction collaborative d'ontologies
National audienceCet article rend compte d'une étude en cours sur l'évolution du modèle de l'utilisateur de systèmes de construction collaborative d'ontologies. Par modèle de l'utilisateur (ou modèle du contributeur), nous entendons la représentation que les concepteurs se font des utilisateurs de leurs systèmes et plus généralement des contributeurs à la construction des ontologies. Nous décrivons : 1) la méthode que nous utilisons pour étudier l'évolution du modèle de l'utilisateur ; 2) l'évolution de ce modèle (en termes de types d'utilisateurs, de caractérisations de l'utilisateur et de caractérisations de l'environnement de l'utilisateur) ; 3) les évolutions parallèles : a) des méthodes de conception des systèmes collaboratifs ; b) des systèmes eux-mêmes ; et c) des méthodes de construction collaborative des ontologies. Nous mentionnons quelques perspectives d'évolution envisagées par les concepteurs eux-mêmes. Cette étude vise à faire ressortir l'importance d'acquérir une meilleure connaissance des contributeurs potentiels à la construction collaborative des ontologies afin d'obtenir des outils collaboratifs mieux adaptés à ces contributeurs
Discovering beaten paths in collaborative ontology-engineering projects using Markov chains
AbstractBiomedical taxonomies, thesauri and ontologies in the form of the International Classification of Diseases 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 International Classification of Diseases, which is currently under active development by the World Health Organization 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 the way these different stakeholders collaborate will enable us to improve editing environments that support such collaborations. In this paper, we uncover how large ontology-engineering projects, such as the International Classification of Diseases 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 frequently change after specific given ones) 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
Actes des 29es Journées Francophones d'Ingénierie des Connaissances, IC 2018
International audienc