Learning concise pattern for interlinking with extended version space

fan2014bInternational audienceMany data sets on the web contain analogous data which represent the same resources in the world, so it is helpful to interlink different data sets for sharing information. However, finding correct links is very challenging because there are many instances to compare. In this paper, an interlinking method is proposed to interlink instances across different data sets. The input is class correspondences, property correspondences and a set of sample links that are assessed by users as either "positive" or "negative". We apply a machine learning method, Version Space, in order to construct a classifier, which is called interlinking pattern, that can justify correct links and incorrect links for both data sets. We improve the learning method so that it resolves the no-conjunctive-pattern problem. We call it Extended Version Space. Experiments confirm that our method with only 1% of sample links already reaches a high F-measure (around 0.96-0.99). The F-measure quickly converges, being improved by nearly 10% than other comparable approaches

Dataset interlinking module

euzenat2011eThis report presents the first version of the interlinking module for the Datalift platform as well as strategies for future developments

Asian CHI symposium: HCI research from Asia and on Asian contexts and cultures

This symposium showcases the latest HCI work from Asia and those focusing on incorporating Asian sociocultural factors in their design and implementation. In addition to circulating ideas and envisioning future research in human-computer interaction, this symposium aims to foster social networks among academics (researchers and students) and practitioners and grow a research community from Asia

Apprentissage de motifs concis pour le liage de données RDF

There are many data sets being published on the web with Semantic Web technology. The data sets usually contain analogous data which represent the similar resources in the world. If these data sets are linked together by correctly identifying the similar instances, users can conveniently query data through a uniform interface, as if they are connecting a single database. However, finding correct links is very challenging because web data sources usually have heterogeneous ontologies maintained by different organizations. Many existing solutions have been proposed for this problem. (1) One straight-forward idea is to compare the attribute values of instances for identifying links, yet it is impossible to compare all possible pairs of attribute values. (2) Another common strategy is to compare instances with correspondences found by instance-based ontology matching, which can generate attribute correspondences based on overlapping ranges between two attributes, while it is easy to cause incomparable attribute correspondences or undiscovered comparable attribute correspondences. (3) Many existing solutions leverage Genetic Programming to construct interlinking patterns for comparing instances, however the running times of the interlinking methods are usually long. In this thesis, an interlinking method is proposed to interlink instances for different data sets, based on both statistical learning and symbolic learning. On the one hand, the method discovers potential comparable attribute correspondences of each class correspondence via a K-medoids clustering algorithm with instance value statistics. We adopt K-medoids because of its high working efficiency and high tolerance on irregular data and even incorrect data. The K-medoids classifies attributes of each class into several groups according to their statistical value features. Groups from different classes are mapped when they have similar statistical value features, to determine potential comparable attribute correspondences. The clustering procedure effectively narrows the range of candidate attribute correspondences. On the other hand, our solution also leverages a symbolic learning method, called Version Space. Version Space is an iterative learning model that searches for the interlinking pattern from two directions. Our design can solve the interlinking task that does not have a single compatible conjunctive interlinking pattern that covers all assessed correct links with a concise format. The interlinking solution is evaluated with large-scale real-world data from IM@OAEI and CKAN. Experiments confirm that the solution with only 1% of sample links already reaches a high accuracy (up to 0.94-0.99 on F-measure). The F-measure quickly converges improving on other state-of-the-art approaches, by nearly 10 percent of their F-measure values.De nombreux jeux de données sont publiés sur le web à l’aide des technologies du web sémantique. Ces jeux de données contiennent des données qui représentent des liens vers des ressources similaires. Si ces jeux de données sont liés entre eux par des liens construits correctement, les utilisateurs peuvent facilement interroger des données à travers une interface uniforme, comme s’ils interrogeaient un jeu de données unique. Mais, trouver des liens corrects est très difficile car de nombreuses comparaisons doivent être effectuées. Plusieurs solutions ont été proposées pour résoudre ce problème : (1) l’approche la plus directe est de comparer les valeurs d’attributs d’instances pour identifier les liens, mais il est impossible de comparer toutes les paires possibles de valeurs d’attributs. (2) Une autre stratégie courante consiste à comparer les instances selon les attribut correspondants trouvés par l’alignement d’ontologies à base d’instances, qui permet de générer des correspondances d’attributs basés sur des instances. Cependant, il est difficile d’identifier des instances similaires à travers les ensembles de données car,dans certains cas, les valeurs des attributs en correspondance ne sont pas les mêmes.(3) Plusieurs méthodes utilisent la programmation génétique pour construire des modèles d’interconnexion afin de comparer différentes instances, mais elles souffrent de longues durées d’exécution.Dans cette thèse, une méthode d’interconnexion est proposée pour relier les instances similaires dans différents ensembles de données, basée à la fois sur l’apprentissage statistique et sur l’apprentissage symbolique. L’entrée est constituée de deux ensembles de données, des correspondances de classes sur les deux ensembles de données et un échantillon de liens “positif” ou “négatif” résultant d’une évaluation de l’utilisateur. La méthode construit un classifieur qui distingue les bons liens des liens incorrects dans deux ensembles de données RDF en utilisant l’ensemble des liens d’échantillons évalués. Le classifieur est composé de correspondances d’attributs entre les classes correspondantes et de deux ensembles de données,qui aident à comparer les instances et à établir les liens. Le classifieur est appelé motif d’interconnexion dans cette thèse. D’une part, notre méthode découvre des correspondances potentielles entre d’attributs pour chaque correspondance de classe via une méthode d’apprentissage statistique : l’algorithme de regroupement K-medoids,en utilisant des statistiques sur les valeurs des instances. D’autre part, notre solution s’appuie sur un modèle d’interconnexion par une méthode d’apprentissage symbolique: l’espace des versions, basée sur les correspondances d’attributs potentielles découvertes et l’ensemble des liens de l’échantillon évalué. Notre méthode peut résoudre la tâche d’interconnexion quand il n’existe pas de motif d’interconnexion combiné qui couvre tous les liens corrects évalués avec un format concis.L’expérimentation montre que notre méthode d’interconnexion, avec seulement1% des liens totaux dans l’échantillon, atteint une F-mesure élevée (de 0,94 à 0,99)

Data linking with ontology alignment

fan2012aInternational audienceIt is a trend to publish RDF data on the web, so that users can share information semantically. Then, linking isolated data sets together is highly needed. I would like to reduce the comparison scale by isolating the types of resources to be compared, so that it enhances the accuracy of the linking process. I propose a data linking method for linked data on the web. Such a method can interlink linked data automatically by referring to an ontology alignment between linked data sets. Alignments can provide them entities to compare

Data linking with ontology alignment

fan2012aInternational audienceIt is a trend to publish RDF data on the web, so that users can share information semantically. Then, linking isolated data sets together is highly needed. I would like to reduce the comparison scale by isolating the types of resources to be compared, so that it enhances the accuracy of the linking process. I propose a data linking method for linked data on the web. Such a method can interlink linked data automatically by referring to an ontology alignment between linked data sets. Alignments can provide them entities to compare

Flight dynamics modeling of a small ducted fan aerial vehicle based on parameter identification

AbstractThis paper presents a simple and useful modeling method to acquire a dynamics model of an aerial vehicle containing unknown parameters using mechanism modeling, and then to design different identification experiments to identify the parameters based on the sources and features of its unknown parameters. Based on the mathematical model of the aerial vehicle acquired by modeling and identification, a design for the structural parameters of the attitude control system is carried out, and the results of the attitude control flaps are verified by simulation experiments and flight tests of the aerial vehicle. Results of the mathematical simulation and flight tests show that the mathematical model acquired using parameter identification is comparatively accurate and of clear mechanics, and can be used as the reference and basis for the structural design

Convergent Synthesis of Polysubstituted Furans via Catalytic Phosphine Mediated Multicomponent Reactions

Tri- or tetrasubstituted furans have been prepared from terminal activated olefins and acyl chlorides or anhydrides by a multicomponental convergent synthesis mode. Instead of stoichiometric nBu3P, only catalytic nBu3P or nBu3P=O is needed to furnish the furans in modest to excellent yields with a good functional group tolerance under the aid of reducing agent silane. This synthetic method features a silane-driven catalytic intramolecular Wittig reaction as a key annulation step and represents the first successful application of catalytic Wittig reaction in multicomponent cascade reaction

Evaluation Of Microstructural Evolution And Corrosion Types In Ultrasonic Assisted Laser Re-Melted Thermal Barrier Coatings Under Exposure To Molten Salts

In this study, yttria-stabilized zirconia (YSZ) thermal barrier coatings (TBCs) with improved microstructure and properties were re-melted using the novel ultrasonic assisted laser re-melting technique and its hot corrosion behavior was investigated in 50 wt% Na2SO4+50 wt% V2O5 molten salts at 1100 °C. The results indicated that the microstructure and corrosion types of the laser re-melted TBCs were significantly affected by the ultrasonic vibration power output (UVPO). The increased convection effect of low UVPO (20%) caused the enhanced nucleation of equiaxed grain and improvement in crack distribution and size. High strain tolerance led to the enhancement in the self-healing (eliminate cracks spacing) performance of cracks when exposed to the thermal expansion and phase transformation volume expansion, and seal the macroscopic diffusion channels of particles, thus promoting the propagation of only intergranular corrosion (internal corrosion). However, intensive ultrasonic nonlinear effect of high UVPO (50%) led to the generation of the uneven surface with coarse irregularly distributed cracks that are not entirely self-healing when exposed to volume expansion. Thus, the massive coatings particles could migrate preferentially outward through the cracks and voids to react with molten salts on the surface (external corrosion), leading to formation of the corrosion products