Christophe Cruz

Bio: Christophe Cruz is an academic researcher from University of Burgundy. The author has contributed to research in topics: Ontology (information science) & Semantic Web. The author has an hindex of 14, co-authored 141 publications receiving 850 citations. Previous affiliations of Christophe Cruz include Centre national de la recherche scientifique.

A Survey on Ontology Evaluation Methods

Abstract: Ontologies nowadays have become widely used for knowledge representation, and are considered as foundation for Semantic Web. However with their wide spread usage, a question of their evaluation increased even more. This paper addresses the issue of finding an efficient ontology evaluation method by presenting the existing ontology evaluation techniques, while discussing their advantages and drawbacks. The presented ontology evaluation techniques can be grouped into four categories: gold standard-based, corpus-based, task-based and criteria based approaches.

Continuum: a spatiotemporal data model to represent and qualify filiation relationships

Abstract: This work introduces an ontology-based spatio-temporal data model to represent entities evolving in space and time. A dynamic phenomenon generates a complex relationship network between the entities involved in the process. At the abstract level, the relationships can be identity or topological filiations. The existence of an identity filiation depends on whether the object changes its identity or not. On the other hand, topological filiations are based exclusively on the spatial component, like in the case of growth, reduction, merging or splitting. When combining identity and topological filiations, six filiation relationships are obtained, forming a second abstract level. Upper-level filiation relationships provide better semantic vocabulary to describe the modeled phenomena, thus allowing the implementation of spatial, temporal and identity constraints. In this paper, we present a method based on identity and topological filiation relationships, to improve the capabilities of standard knowledge bases using Semantic Web technologies. Our method enables us to check the consistency of spatio-temporal and semantic data. An example is given in the field of urban growth to show the capabilities of the model.

Semantic Building Information Model and Multimedia for Facility Management

Abstract: In the field of civil engineering, the proliferation of stakeholders and the heterogeneity of modeling tools detract from the quality of the design process, construction and building maintenance. In this paper, we present a Web-based platform lets geographically dispersed project participants—from facility managers and architects to electricians to plumbers—directly use and exchange project documents in a centralized virtual environment using a simple Web browser. A 3D visualization lets participants move around in the building being designed and obtain information about the objects that compose it. This approach is based both on a semantic architecture called CDMF and IFC 2x3. Our framework, based on Building Information Modeling features, facilitates data maintenance (data migration, model evolution) during the building lifecycle and reduces the volume of data.

A Review on Applications of Big Data for Disaster Management

Abstract: The term “disaster management” comprises both natural and man-made disasters. Highly pervaded with various types of sensors, our environment generates large amounts of data. Thus, big data applications in the field of disaster management should adopt a modular view, going from a component to nation scale. Current research trends mainly aim at integrating component, building, neighborhood and city levels, neglecting the region level for managing disasters. Current research on big data mainly address smart buildings and smart grids, notably in the following areas: energy waste management, prediction and planning of power generation needs, improved comfort, usability and endurance based on the integration of energy consumption data, environmental conditions and levels of occupancy. This paper aims presenting a systematic literature review on the applications of big data in disaster management. The paper will first presents the visual definition of disaster management and describes big data; it will then illustrate the findings and gives future recommendations after a systematic literature review.

User Modeling and User-Adapted Interaction

Abstract: Please provide a short abstract of 100 to 250 words. The abstract should not contain any undefined abbreviations or unspecified references. Work planned but not completed should not appear in the abstract.