Khaoula Mahmoudi

Towards geographic databases enrichment

The geographic database (GDB) is the backbone of the geographic information system (GIS). Indeed, all kinds of data managements are based and strongly affected by the type, relevancy and scope of the stored data. Nevertheless, this dataset is sometimes insufficient to make the adequate decision. Then, it is of primary interest to provide other data sources to complement the inherent GDB. In this context, we propose a four staged semantic data enrichment approach consisting of: text segmentation, theme identification, delegation and text filtering. Besides, a refinement is eventually executed to enhance the data enrichment results.

Text thematic fragmentation and identification

Given the exponential growth of online information, one of the primary difficulties facing users is the information overload. Huge amounts of these electronic information are mainly encapsulated in text documents. So, the researchers are striving to develop robust methods to provide users and managers with prominent solutions needed for text analysis. This allows maximizing profits by saving time and money devoted to manage the increasing amount of information. In this context and among others we find text segmentation and theme identification. In this paper and by resting on the existing well known approaches and advances in the text segmentation and theme identification fields, we propose new solutions to reach the objectives behind achieving these two techniques more accurately.

A Bridge between Document Management System and Geographical Information System

The power of geographic information system is to help managers make critical decisions they face daily. The ability to make sound decisions relies upon the availability of relevant information. Typically, spatial databases do not contain much information that could support the decision making process in all situations. To extend the available dataset, we propose an approach for the enrichment of geographical databases (GDB) and especially their semantic component. This enrichment is performed by providing knowledge extracted from web documents to supplement the aspatial data of the GDB. The knowledge extraction process is reached through the generation of condensed representation of the relevant information derived from a web corpus. This process is carried out in a distributed fashion that complies with the multi-agents paradigm.

Gist of Text Extraction

The amount of spatial information we are managing is continuously in growth. Such information is increasingly used in most economic sectors by having a direct impact on productivity. Geographic information system (GIS) stores spatial data in geographic databases (GDB). Typically, such databases do not store all the data and details needed to sustain decision makers, since this entails a high cost of data processing and maintenance. In front to this situation, new technologies were proposed to pursue the relevant and timely information. Concretely, this is accomplished through data enrichment. In this context, we propose an approach to enrich the semantic aspect of the GDB. The objective is to save time in retrieving and updating the data to reflect the current state of geographic entities.