Concepts Extraction

Structuring huge documents with a high speed is still a challenge. In this paper, we propose a heuristic based on pseudo-concepts to derive a tree of words reflecting in decreasing "importance" order the semantic macro-structure of the space of documents or the micro-structure of a document. Both macro and micro structures are used to browse inside the space of documents. The advantage of the proposed methods with respect to previous ones using exact formal concepts [2,4,11], is that by only selecting approximate formal concepts associated to the different pairs of a binary relation linking documents or sentences inside a document to indexing words, we improve the structuring process in terms of time complexity while keeping acceptable meaning of generated text structure. Experimentation [12] realized with documents with big size showed that response time of the structuring system as well as the browsing trees are very helpful for users to get the global structured view of the space of documents and the detailed view inside a selected document. Starting from an already created conceptual meta-search engine merging Google and Yahoo search results [4,11], we now have a way to compile more web pages in much shorter time.

Online news media provides aggregated news and stories from different sources all over the world and up-to-date news coverage. The main goal of this study is to find a solution that is considered as a homogeneous source for the news and to represent the news in a new conceptual framework. Furthermore, the user can easily and quickly find different updated news in a fast way through the designed interface. The Mobile App implementation is based on modeling the multi-level conceptual analysis frame. Discovering main concepts of any domain is captured from the hidden unstructured data that are analyzed by the proposed solution. Concepts are discovered through analyzing data patterns to be structured into a treebased interface for easy navigation for the end user. Our final experiment results show that analyzing the news before displaying to the end-user and restructuring the final output in a conceptual multilevel structure produces a new display frame for the end user to find the related information of interest.

—Online marketplaces are e-commerce websites where thousands of products are provided by multiple third parties. There are dozens of these differently structured marketplaces that need to be visited by the end users to reach their targets. This searching process consumes a lot of time and effort; moreover it negatively affects the user experience. In this paper, extensive analysis and evaluation of the existing e-marketplaces are performed to improve the end-users experience through a Mobile App. The main goal of this study is to find a solution that is capable of integrating multiple heterogeneous hidden data sources and unify the received responses into one single, structured and homogeneous source. Furthermore, the user can easily choose the desired product or reformulate the query through the interface. The proposed Android Mobile App is based on the multi-level conceptual analysis and modeling discipline, in which, data are analyzed in a way that helps in discovering the main concepts of any unknown domain captured from the hidden web. These concepts discovered through information extraction are then structured into a tree-based interface for easy navigation and query reformulation. The application has been evaluated through substantial experiments and compared to other existing mobile applications. The results showed that analyzing the query results and restructuring the output before displaying to the end-user in a conceptual multilevel mechanism are reasonably effective in terms of number of clicks, time taken and number of navigation screens. Based on the proposed intelligent application, the interface is minimized to only two navigation screens, and the time needed to browse products from multiple marketplaces is kept reasonable in order to reach the target product.