Contribution à la maintenance des ontologies à partir d'analyses textuelles : extraction de termes et de relations entre termes

Les ontologies sont des nouvelles formes de contrôle intelligent de l'information. Elles présentent un savoir préalable requis pour un traitement systématique de l'information à des fins de navigation, de rappel, de précision, etc. Toutefois, les ontologies sont confrontées de façon continue à un problème d'évolution. Étant donné la complexité des changements à apporter, un processus de maintenance, du moins semi-automatique, s'impose de plus en plus pour faciliter cette tâche et assurer sa fiabilité.\ud L'approche proposée trouve son fondement dans un modèle cognitif décrivant un processus d'extraction de connaissances à partir de textes et de thésaurus. Nous mettons ainsi, les textes au centre du processus d'ingénierie des connaissances et présentons une approche se démarquant des techniques formelles classiques en représentation de connaissances par son indépendance de la langue. Les traitements textuels sont fondés principalement sur un processus de classification supporté par un réseau de neurones (ART 1) et sur l'Indexation Sémantique Latente appliquée sur des classes de termes. Partant de l'hypothèse que l'extraction -de connaissances à partir de textes ne peut se contenter d'un traitement statistique (ni même linguistique) de données textuelles pour accaparer toute leur richesse sémantique, un processus d'extraction de connaissances à partir d'un thésaurus a été conçu afin d'intégrer, le mieux possible, les connaissances du domaine au sein de l'ontologie. Ce processus est fondé principalement sur un calcul d'associations sémantiques entre des Vecteurs Conceptuels. Le modèle proposé représente une chaîne de traitement (ONTOLOGICO) au sein de la plateforme\ud SATIM. Ce modèle vise à assister les experts de domaine dans leur tâche de conceptualisation et de maintenance des ontologies en se basant sur un processus itératif supporté par un ensemble de modules, en particulier, un extracteur de termes, un lemmatiseur, un segmenteur, un classifieur, un module de raffinement sémantique basé sur l'Indexation Sémantique Latente et un identificateur de termes reliés basé sur le calcul de similarité sémantique entre les couples de vecteurs conceptuels. La découverte de relations entre termes pour les besoins d'une conceptualisation de domaine s'avère être le résultat d'une complémentarité de traitements appliqués tant sur des textes de domaine que sur un thésaurus. D'une part, les analyses textuelles fondées principalement sur l'application de l'Indexation Sémantique Latente sur des classes de termes génèrent des relations sémantiques précises. D'autre part, l'extraction de relations sémantiques à partir d'un thésaurus, en se basant sur une représentation par des Vecteurs conceptuels, constitue un choix théorique judicieux et performant. Ce processus joue en effet, un rôle important dans la complétude des relations.\ud Ce projet de recherche se place au coeur des échanges entre terminologie et acquisition de connaissances. Il amène une réflexion sur les divers paliers à envisager dans une telle démarche de modélisation de connaissances textuelles pour des objectifs de maintenance d'une ontologie de domaine. La méthodologie proposée constitue une aide précieuse dans le domaine de la maintenance des ontologies. Elle assiste les terminologues chargés de naviguer à travers de vastes données textuelles pour extraire et normaliser la terminologie et facilite la tâche des ingénieurs en connaissances, chargés de modéliser des domaines. ______________________________________________________________________________ MOTS-CLÉS DE L’AUTEUR : Maintenance d'ontologie, Traitement Automatique du Langage Naturel (TALN), Indexation Sémantique Latente, Vecteurs Conceptuels, Classification automatique, Réseaux de Neurones

Green and Gold Open Access Percentages and Growth, by Discipline

Most refereed journal articles today are published in subscription journals, accessible only to subscribing institutions, hence losing considerable research impact. Making articles freely accessible online ("Open Access," OA) maximizes their impact. Articles can be made OA in two ways: by self-archiving them on the web ("Green OA") or by publishing them in OA journals ("Gold OA"). We compared the percent and growth rate of Green and Gold OA for 14 disciplines in two random samples of 1300 articles per discipline out of the 12,500 journals indexed by Thomson-Reuters-ISI using a robot that trawled the web for OA full-texts. We sampled in 2009 and 2011 for publication year ranges 1998-2006 and 2005-2010, respectively. Green OA (21.4%) exceeds Gold OA (2.4%) in proportion and growth rate in all but the biomedical disciplines, probably because it can be provided for all journals articles and does not require paying extra Gold OA publication fees. The spontaneous overall OA growth rate is still very slow (about 1% per year). If institutions make Green OA self-archiving mandatory, however, it triples percent Green OA as well as accelerating its growth rate

Ten-year Analysis of University of Minho Green OA Self-Archiving Mandate

University of Minho adopted the first university-wide Green Open Access Mandate in Europe in 2004, requiring all research output to be self-archived in the institution's repository. The mandate was upgraded in 2011 to designate the repository as the sole mechanism for submitting publications for individual and institutional research performance assessment. A 10-year analysis shows that deposit rates are increasing and deposit delays are decreasing. Once the rest of the world follows Minho's example, universal Green OA will not be far behind

Estimating Open Access Mandate Effectiveness: The MELIBEA Score

MELIBEA is a Spanish database that uses a composite formula with eight weighted conditions to estimate the effectiveness of Open Access mandates (registered in ROARMAP). We analyzed 68 mandated institutions for publication years 2011-2013 to determine how well the MELIBEA score and its individual conditions predict what percentage of published articles indexed by Web of Knowledge is deposited in each institution's OA repository, and when. We found a small but significant positive correlation (0.18) between MELIBEA score and deposit percentage. We also found that for three of the eight MELIBEA conditions (deposit timing, internal use, and opt-outs), one value of each was strongly associated with deposit percentage or deposit latency (immediate deposit required, deposit required for performance evaluation, unconditional opt-out allowed for the OA requirement but no opt-out for deposit requirement). When we updated the initial values and weights of the MELIBEA formula for mandate effectiveness to reflect the empirical association we had found, the score's predictive power doubled (.36). There are not yet enough OA mandates to test further mandate conditions that might contribute to mandate effectiveness, but these findings already suggest that it would be useful for future mandates to adopt these three conditions so as to maximize their effectiveness, and thereby the growth of OA.Comment: 27 pages, 13 figures, 3 tables, 40 references, 7761 word

Graphical Models for Multi-dialect Arabic Isolated Words Recognition

AbstractThis paper presents the use of multiple hybrid systems for the recognition of isolated words from a large multi-dialect Arabic vocabulary. Such as the Hidden Markov models (HMM), Dynamic Bayesian networks (DBN) lack a discriminatory ability especially on speech recognition even if their progress is huge. Multi-Layer perceptrons (MLP) was applied in literature as an estimator of emission probabilities in HMM and proves it effectiveness. In order to ameliorate the results of recognition systems, we apply Support Vectors Machine (SVM) as an estimator of posterior probabilities since they are characterized by a high predictive power and discrimination. Moreover, they are based on a structural risk minimization (SRM) where the aim is to set up a classifier that minimizes a bound on the expected risk, rather than the empirical risk. In this work we have done a comparative study between three hybrid systems MLP/HMM, SVM/HMM and SVM/DBN and the standards models of HMM and DBN. In this paper, we describe the use of the hybrid model SVM/DBN for multi-dialect Arabic isolated words recognition. So, by using 67,132 speech files of Arabic isolated words, this work arises a comparative study of our acknowledgment system of it as the following: the use of especially the HMM standards leads to a recognition rate of 74.18%.as the average rate of 8 domains for everyone of the 4 dialects. Also, with the hybrid systems MLP/HMM and SVM/HMM we succeed in achieving the value of 77.74%.and 7806% respectively. Moreover, our proposed system SVM/DBN realizes the best performances, whereby, we achieve 87.67% as a recognition rate more than 83.01% obtained by GMM/DBN

Ten-year analysis of University of Minho Green OA self-archiving mandate

University of Minho adopted the first university-wide Green Open Access Man-date in Europe in 2004, requiring all research output to be self-archived in the institution’s repository. The mandate was upgraded in 2011 to designate the repository as the sole mechanism for submitting publications for individual and institutional research performance assessment. A 10-year analysis shows that deposit rates are increasing and deposit delays are decreasing. Once the rest of the world follows Minho’s example, universal Green OA will not be far behind

Testing the Finch Hypothesis on Green OA Mandate Ineffectiveness

We have now tested the Finch Committee's Hypothesis that Green Open Access Mandates are ineffective in generating deposits in institutional repositories. With data from ROARMAP on institutional Green OA mandates and data from ROAR on institutional repositories, we show that deposit number and rate is significantly correlated with mandate strength (classified as 1-12): The stronger the mandate, the more the deposits. The strongest mandates generate deposit rates of 70%+ within 2 years of adoption, compared to the un-mandated deposit rate of 20%. The effect is already detectable at the national level, where the UK, which has the largest proportion of Green OA mandates, has a national OA rate of 35%, compared to the global baseline of 25%. The conclusion is that, contrary to the Finch Hypothesis, Green Open Access Mandates do have a major effect, and the stronger the mandate, the stronger the effect (the Liege ID/OA mandate, linked to research performance evaluation, being the strongest mandate model). RCUK (as well as all universities, research institutions and research funders worldwide) would be well advised to adopt the strongest Green OA mandates and to integrate institutional and funder mandates.Comment: 6 pages, 1 table, 4 figure

Self-selected or mandated, open access increases citation impact for higher quality research

Articles whose authors make them Open Access (OA) by self-archiving them online are cited significantly more than articles accessible only to subscribers. Some have suggested that this "OA Advantage" may not be causal but just a self-selection bias, because authors preferentially make higher-quality articles OA. To test this we compared self-selective self-archiving with mandatory self-archiving for a sample of 27,197 articles published 2002-2006 in 1,984 journals. The OA Advantage proved just as high for both. Logistic regression showed that the advantage is independent of other correlates of citations (article age; journal impact factor; number of co-authors, references or pages; field; article type; country or institution) and greatest for the most highly cited articles. The OA Advantage is real, independent and causal, but skewed. Its size is indeed correlated with quality, just as citations themselves are (the top 20% of articles receive about 80% of all citations). The advantage is greater for the more citeable articles, not because of a quality bias from authors self-selecting what to make OA, but because of a quality advantage, from users self-selecting what to use and cite, freed by OA from the constraints of selective accessibility to subscribers only. [See accompanying RTF file for responses to feedback. Four PDF files provide Supplementary Analysis.

Purification and biochemical characterization of a secreted group IIA chicken intestinal phospholipase A2

<p>Abstract</p> <p>Background</p> <p>Secretory phospholipase A2 group IIA (IIA PLA2) is a protein shown to be highly expressed in the intestine of mammals. However, no study was reported in birds.</p> <p>Results</p> <p>Chicken intestinal group IIA phospholipase A₂(ChPLA₂-IIA) was obtained after an acidic treatment (pH.3.0), precipitation by ammonium sulphate, followed by sequential column chromatographies on Sephadex G-50 and mono-S ion exchanger. The enzyme was found to be a monomeric protein with a molecular mass of around 14 kDa. The purified enzyme showed a substrate preference for phosphatidylethanolamine and phosphatidylglycerol, and didn't hydrolyse phosphatidylcholine. Under optimal assay conditions, in the presence of 10 mM NaTDC and 10 mM CaCl_2,a specific activity of 160 U.mg^-1for purified ChPLA₂-IIA was measured using egg yolk as substrate. The fifteen NH2-terminal amino acid residues of ChPLA₂-IIA were sequenced and showed a close homology with known intestinal secreted phospholipases A₂. The gene encoding the mature ChPLA₂-IIA was cloned and sequenced. To further investigate structure-activity relationship, a 3D model of ChPLA₂-IIA was built using the human intestinal phospholipase A₂structure as template.</p> <p>Conclusion</p> <p>ChPLA2-IIA was purified to homogeneity using only two chromatographic colomns. Sequence analysis of the cloned cDNA indicates that the enzyme is highly basic with a pI of 9.0 and has a high degree of homology with mammalian intestinal PLA₂-IIA.</p