Sub-Classifier Construction for Error Correcting Output Code Using Minimum Weight Perfect Matching

Multi-class classification is mandatory for real world problems and one of promising techniques for multi-class classification is Error Correcting Output Code. We propose a method for constructing the Error Correcting Output Code to obtain the suitable combination of positive and negative classes encoded to represent binary classifiers. The minimum weight perfect matching algorithm is applied to find the optimal pairs of subset of classes by using the generalization performance as a weighting criterion. Based on our method, each subset of classes with positive and negative labels is appropriately combined for learning the binary classifiers. Experimental results show that our technique gives significantly higher performance compared to traditional methods including the dense random code and the sparse random code both in terms of accuracy and classification times. Moreover, our method requires significantly smaller number of binary classifiers while maintaining accuracy compared to the One-Versus-One.Comment: 7 pages, 3 figure

Juego de ontología que propaga activamente evaluaciones de usuarios a través de ontologías superiores

Ontology matching is a complex and largely user-driven process of finding correspondences between entities belonging to different ontologies. Many algorithms have been proposed to automate the matching generation. However, they can’t be fully automated since the user input is required to accept, reject, or create new alignments or matchings.This paper extends on active learning framework for ontology matching, which tries to find the most informative candidate matches to query the user. In our approach the user’s feedback exploits upper ontologies as semantic bridges. Such bridges contribute to the overall matching process while considering the supervised information and its propagation in correcting mistake matchings. In the experimentation our work outperformed the previous version where none upper ontology was used, while it remains as competitive as state of the art ontology matching system.Coincidencia ontología es un proceso complejo y en gran parte impulsado por los usuarios para encontrar correspondencias entre entidades de diferentes ontologías. Se han propuesto muchos algoritmos para automatizar la generación de coincidencias. Sin embargo, no pueden ser totalmente automatizados ya que se requiere la intervención del usuario para aceptar, rechazar, o crear nuevas alineaciones o matchings.En este trabajo se extiende sobre el marco de aprendizaje activo para la adaptación de la ontología, que trata de encontrar las coincidencias de candidatos más informativos para consultar al usuario. En nuestro enfoque de retroalimentación del usuario explota ontologías superiores como puentes semánticos. Estos puentes contribuyen al proceso de correspondencia en general teniendo en cuenta la información supervisada y su propagación en la corrección de concordancias de error. En la experimentación nuestro trabajo superó a la versión anterior en el que se no ha de utilizar ningún elemento de ontología superior, mientras que sigue siendo tan competitivo como el estado del sistema de concordancia de la ontología arte

Core Ontologies for Safe Autonomous Driving

Abstract. Representing the knowledge of driving environments in a structured machine-readable format is necessary for safe autonomous driving. We use ontologies to represent the knowledge of maps, driving paths, and driving environments to improve safety for smart vehicles. In this paper, we introduce core ontologies that are used for developing Advanced Driver Assistance Systems. The ontologies can be reused and extended for constructing Knowledge Base for smart vehicles as well as for implementing different types of Advanced Driver Assistance Systems

A Linked Data Approach to Know-How

Abstract. The Web is one of the major repositories of human generated know-how, such as step-by-step videos and instructions. This knowledge can be potentially reused in a wide variety of applications, but it cur-rently suffers from a lack of structure and isolation from related knowl-edge. To overcome these challenges we have developed a Linked Data framework which can automate the extraction of know-how from exist-ing Web resources and generate links to related knowledge on the Linked Data Cloud. We have implemented our framework and used it to extract a Linked Data representation of two of the largest know-how reposi-tories on the Web. We demonstrate two possible uses of the resulting dataset of real-world know-how. Firstly, we use this dataset within a Web application to offer an integrated visualization of distributed know-how resources. Lastly, we show the potential of this dataset for inferring common sense knowledge about tasks.

Proceedings of The Tenth International Workshop on Ontology Matching (OM-2015)

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