62 research outputs found
Automatic annotation of bioinformatics workflows with biomedical ontologies
Legacy scientific workflows, and the services within them, often present
scarce and unstructured (i.e. textual) descriptions. This makes it difficult to
find, share and reuse them, thus dramatically reducing their value to the
community. This paper presents an approach to annotating workflows and their
subcomponents with ontology terms, in an attempt to describe these artifacts in
a structured way. Despite a dearth of even textual descriptions, we
automatically annotated 530 myExperiment bioinformatics-related workflows,
including more than 2600 workflow-associated services, with relevant
ontological terms. Quantitative evaluation of the Information Content of these
terms suggests that, in cases where annotation was possible at all, the
annotation quality was comparable to manually curated bioinformatics resources.Comment: 6th International Symposium on Leveraging Applications (ISoLA 2014
conference), 15 pages, 4 figure
Development of a method to evaluate odour quality based on non-expert analysis
International audienceCharacterizing odour quality is a complex process that consists in identifying a set of descriptors that best synthesizes the olfactory perception. Generally, this characterization results in a limited set of descriptors provided by professionals in sensorial analysis. These experts previously learnt a common language to describe characteristic odour (Odour wheel or Champ des odeurs ©). These sensorial analysis sessions cost industrial manufacturers large sums every year. If this characterization is entrusted to neophytes, the number of participants of a sensorial analysis session can be significantly enlarged while reducing costs. However, each individual description is no more related to a set of non-ambiguous descriptors but to a bag of terms in natural language. Two issues are then related to odour characterization. The first one is how translating free natural language descriptions into structured descriptors; the second one is how summarizing a set of individual characterizations into a consistent and synthetic unique characterization for professional purposes. This paper will propose an approach based on natural language Processing and Knowledge Representation based techniques to formalize and automatize both translation of bags of terms into sets of descriptors and summarization of sets of structured descriptors
A Computational Model of Conceptual Combination
We describe the Interactional-Constraint (ICON) model of
conceptual combination. This model is based on the idea that
combinations are interpreted by incrementally constraining
the range of interpretation according to the interacting
influence of both constituent nouns. ICON consists of a series
of discrete stages, combining data from the British National
Corpus, the WordNet lexicon and the Web to predict the
dominant interpretation of a combination and a range of
factors relating to ease of interpretation. One of the major
advantages of the model is that it does not require a tailored
knowledge base, thus broadening its scope and utility. We
evaluate ICON’s reliability and find that it is accurate in
predicting word senses and relations for a wide variety of
combinations. However, its ability to predict ease of
interpretation is poor. The implications for models of
conceptual combination are discussed
OBIRS-feedback, une méthode de reformulation utilisant une ontologie de domaine
National audienceLes performances d'un système de recherche d'information (SRI) peuvent être dégradées en termes de précision du fait de la difficulté pour des utilisateurs à formuler précisément leurs besoins en information. La reformulation ou l'expansion de requêtes constitue une des réponses à ce problème dans le cadre des SRI. Dans cet article, nous proposons une nouvelle méthode de reformulation de requêtes conceptuelles qui, à partir de documents jugés pertinents par l'utilisateur et d'une ontologie de domaine, cherche un ensemble de concepts maximisant les performances du SRI. Celles-ci sont évaluées, de manière originale, à l'aide d'indicateurs dont une formalisation est proposée. Cette méthode a été évaluée en utilisant notre moteur OBIRS, l'ontologie de domaine MeSH et la collection de tests MuCHMORE
Dealing with uncertain entities in ontology alignment using rough sets
This is the author's accepted manuscript. The final published article is available from the link below. Copyright @ 2012 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.Ontology alignment facilitates exchange of knowledge among heterogeneous data sources. Many approaches to ontology alignment use multiple similarity measures to map entities between ontologies. However, it remains a key challenge in dealing with uncertain entities for which the employed ontology alignment measures produce conflicting results on similarity of the mapped entities. This paper presents OARS, a rough-set based approach to ontology alignment which achieves a high degree of accuracy in situations where uncertainty arises because of the conflicting results generated by different similarity measures. OARS employs a combinational approach and considers both lexical and structural similarity measures. OARS is extensively evaluated with the benchmark ontologies of the ontology alignment evaluation initiative (OAEI) 2010, and performs best in the aspect of recall in comparison with a number of alignment systems while generating a comparable performance in precision
Semantic Similarity in Cheminformatics
Similarity in chemistry has been applied to a variety of problems: to predict biochemical properties of molecules, to disambiguate chemical compound references in natural language, to understand the evolution of metabolic pathways, to predict drug-drug interactions, to predict therapeutic substitution of antibiotics, to estimate whether a compound is harmful, etc. While measures of similarity have been created that make use of the structural properties of the molecules, some ontologies (the Chemical Entities of Biological Interest (ChEBI) being one of the most relevant) capture chemistry knowledge in machine-readable formats and can be used to improve our notions of molecular similarity. Ontologies in the biomedical domain have been extensively used to compare entities of biological interest, a technique known as ontology-based semantic similarity. This has been applied to various biologically relevant entities, such as genes, proteins, diseases, and anatomical structures, as well as in the chemical domain. This chapter introduces the fundamental concepts of ontology-based semantic similarity, its application in cheminformatics, its relevance in previous studies, and future potential. It also discusses the existing challenges in this area, tracing a parallel with other domains, particularly genomics, where this technique has been used more often and for longer
Automatic Generation of Educational Quizzes from Domain Ontologies
International audienceEducational quizzes are very valuable resources to test or evaluate the knowledge acquired by learners and to support lifelong learning on various topics or subjects, in an informal and entertaining way. The production of quizzes is a very time-consuming task and its automation is thus a real challenge in e-Education. In this paper, we address the research question of how to automate the generation of quizzes by taking advantage of existing knowledge sources available on the Web. We propose an approach that allows learners to take advantage of the knowledge captured in domain ontologies available on the Web and to discover or acquire a more in-depth knowledge of a specific domain by solving educational quizzes automatically generated from an ontology modelling the domain. The implementation and experimentation of our approach is presented through the use case of a world-famous French game of manually generated multiple-choice questions
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