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

Knowledge engineering in the legal domain: The construction of a FunGramKB Satellite Ontolog y

[ES] Una de las tareas más tediosas en la labor diaria de los profesionales del derecho es la búsqueda de información en el ámbito jurídico. Con el fin de implementar aplicaciones avanzadas del procesamiento del lenguaje natural en este dominio, hemos desarrollado un modelo de representación del conocimiento especializado orientado a la semántica profunda dentro del marco de FunGramKB, una base de conocimiento léxicoconceptual multilingüe de propósito general. Más concretamente, el resultado de esta investigación ha dado como fruto una ontología terminológica sobre derecho penal en el dominio del terrorismo y el crimen organizado transnacional para ser utilizada en sistemas inteligentes que permitan la comprensión automática del discurso legal. El objetivo de este artículo es la descripción de la metodología empleada en el desarrollo de dicha ontología, centrándonos en la descripción de la herramienta que asiste al lingüista en el proceso de adquisición y conceptualización de los términos.[EN] One of the most time-consuming tasks in the daily work of legal professions is the search for information in the field of law. To implement advanced computer-based applications of natural language processing in this regard, we have developed a model of specialized knowledge representation driven by the deep semantics of FunGramKB, a multilingual general-purpose lexico-conceptual knowledge base. In particular, our research results in a terminological ontology on criminal law in the domain of transnational terrorism and organized crime to be implemented in intelligent systems which aim to understand legal discourse automatically. The objective of this paper is to describe the methodology used in the development of that ontology, focusing on the computerised tool to assist linguists in the process of terminological acquisition and conceptualization.Este trabajo forma parte de diversos proyectos de investigación financiados por el Ministerio de Ciencia y Tecnología, códigos FFI2011-29798-C02-01, FFI2010-17610 y FFI2010-15983.Periñán Pascual, JC.; Arcas Túnez, F. (2014). La ingeniería del conocimiento en el dominio legal: La construcción de una Ontología Satélite en FunGramKB. Revista Signos. 47(84):113-139. https://doi.org/10.4067/S0718-09342014000100006S113139478

A semantic enhanced hybrid recommendation approach: A case study of e-Government tourism service recommendation system

© 2015 Elsevier B.V.All rights reserved. Recommender systems are effectively used as a personalized information filtering technology to automatically predict and identify a set of interesting items on behalf of users according to their personal needs and preferences. Collaborative Filtering (CF) approach is commonly used in the context of recommender systems; however, obtaining better prediction accuracy and overcoming the main limitations of the standard CF recommendation algorithms, such as sparsity and cold-start item problems, remain a significant challenge. Recent developments in personalization and recommendation techniques support the use of semantic enhanced hybrid recommender systems, which incorporate ontology-based semantic similarity measure with other recommendation approaches to improve the quality of recommendations. Consequently, this paper presents the effectiveness of utilizing semantic knowledge of items to enhance the recommendation quality. It proposes a new Inferential Ontology-based Semantic Similarity (IOBSS) measure to evaluate semantic similarity between items in a specific domain of interest by taking into account their explicit hierarchical relationships, shared attributes and implicit relationships. The paper further proposes a hybrid semantic enhanced recommendation approach by combining the new IOBSS measure and the standard item-based CF approach. A set of experiments with promising results validates the effectiveness of the proposed hybrid approach, using a case study of the Australian e-Government tourism services