Nanotechnologies are among the fastest growing areas of scientific research, and have important applications in a wide variety of fields. At the same time, scientists and regulators are concerned about the potential health and environmental risks related to the widespread production and use of the nanomaterials created by these technologies. Several recent animal and cell culture studies have s...

This research project aims to use machine learning techniques to improve the performance of three-dimensional vision systems. Building on our earlier work, our approach represents and organizes models of object classes in a hierarchy of probabilistic concepts, and it uses Bayesian inference methods to focus attention , recognize objects in images, and make predictions about occluded parts. The ...

Neuro-symbolic integration merges background knowledge and neural networks to provide a more effective learning system. It uses the Core Method as a means to encode rules. However, this method has several drawbacks in dealing with rules that have temporal extent. First, it demands some interface with the world which buffers the input patterns so they can be represented all at once. This imposes...

Microarray data represents valuable information resources, nevertheless the knowledge is hidden inside the data and it is not easy to mine. Background knowledge is also stored in various formats and it is challenging to automatically infer the biological meaning from existing repositories. This paper deals with a new gene-expression knowledgefusion system that combines molecular biology data fr...

This paper presents work that evaluates background knowledge for use in improving accuracy for text classification using Latent Semantic Indexing (LSI). LSI’s singular value decomposition process can be performed on a combination of training data and background knowledge. Intuitively, the closer the background knowledge is to the classification task, the more helpful it will be in terms of crea...

This article presents a technique for Stream Reasoning, consisting in incremental maintenance of materializations of ontological entailments in the presence of streaming information. Previous work, delivered in the context of deductive databases, describes the use of logic programming for the incremental maintenance of such entailments. Our contribution is a new technique that exploits the natu...

In this paper, we address the problem of detecting expressions of moral values in tweets using content analysis. This is a particularly challenging problem because moral values are often only implicitly signaled in language, and tweets contain little contextual information due to length constraints. To address these obstacles, we present a novel approach to automatically acquire background know...

Many data mining problems can be solved better if more background knowledge is added: predictive models can become more accurate, and descriptive models can reveal more interesting findings. However, collecting and integrating background knowledge is tedious manual work. In this paper, we introduce the RapidMiner Linked Open Data Extension, which can extend a dataset at hand with additional att...

Biclustering methods have proven to be critical tools in the exploratory analysis of high-dimensional data including information networks, microarray experiments, and bag of words data. However, most biclustering methods fail to answer specific questions of interest and do not incorporate background knowledge and expertise from the user. To this end, query-based biclustering algorithms have bee...

In this article, we present a framework to model and to use knowledge provided by experts for remote sensing image interpretation of coastal area. The goal of this approach is to associate semantic to regions issued from the segmentation of an image. The idea is to start with a raw description of the knowledge given by the expert on the different thematic object classes present in the image. Th...