Development of ontological knowledge representation: learning hydrocarbons with double bonds at the secondary level

This paper presents the development of an ontological knowledge organization and representation, and explains how application of appropriate methods for its visualization can lead to meaningful learning. We have applied systemic diagrams (SD) as a method of visualizing ontological knowledge organization. Seven ontological models for "Hydrocarbons with double bonds", following the development from concept map to systemic diagram, are constructed. Chemical properties of alkenes are particularly elaborated and represented as a final systemic diagram (SDf). [AJCE, 3(2), June 2013

A Secure Multi-Layer e-Document Method for Improving e-Government Processes

Abstract: In recent years, there has been a tremendous growth in e-Government services due to advances in Information Communication Technology and the number of citizens engaging in eGovernment transactions. In government administration, it is very time consuming to process different types of documents and there are many data input problems. There is also a need to satisfy citizens' requests to retrieve government information and to link these requests to build an online document without asking the citizen to input the data more than once. To provide an e-Government service which is easy to access, fast and secure, the e-Document plays an important role in the management and interoperability of e-Government Systems. To meet these challenges, this paper presents a Secure Multilayer e-Application (SMeA) method for improving e-Government processes. This method involves five steps: namely (i) identifying an e-Template; (ii) building a SMeA; (iii) mapping the data; (iv) processing the e-Application; and (v) approving the e-Application. The first step involves requirements analysis and the last four involve data analysis for building a SMeA. To demonstrate its usefulness, we applied SMeA to a case study of an application for a licence to set up a new business in Vietnam

Representación computacional del lenguaje natural escrito

When humans read, or hear, words, they immediately relatethem to a concept. This is possible due to the informationalready stored in the brain and also to human’s ability toselect, process, and associate such information with words.However, for a computer, natural language text is only asequence of bits that does not convey any meaning on itsown, unless properly processed. A computer interprets thisbit sequence by modeling the processing that takes place inhuman minds, namely structuring and linking the text withpreviously stored information. During this process, as wellas when describing its results, the text is represented usingvarious formal structures that permit automatic processing,interpretation, and comparison of information. In this paper,we present a detailed description of these structures.Cuando el ser humano lee o escucha una palabra, inmediatamente la relaciona con un concepto. Esto es posible gracias a la acumulación de información y a la posibilidad de filtrar, procesar y relacionar dicha información. Para la máquina, una expresión escrita en el lenguaje natural es una cadena de bits que no aporta información por sí sola. Un computador interpreta esta cadena de bits, modelando el proceso que tiene lugar en la mente humana, estructurando y relacionado la cadena con información previamente almacenada. En el proceso, así como al momento de describir los resultados, el texto es representado por estructuras formales que permiten el procesamiento automático, la interpretación y la comparación de la información. Este artículo presenta una descripción detallada de estas estructuras

Concept Mining: A Conceptual Understanding based Approach

Due to the daily rapid growth of the information, there are considerable needs to extract and discover valuable knowledge from data sources such as the World Wide Web. Most of the common techniques in text mining are based on the statistical analysis of a term either word or phrase. These techniques consider documents as bags of words and pay no attention to the meanings of the document content. In addition, statistical analysis of a term frequency captures the importance of the term within a document only. However, two terms can have the same frequency in their documents, but one term contributes more to the meaning of its sentences than the other term. Therefore, there is an intensive need for a model that captures the meaning of linguistic utterances in a formal structure. The underlying model should indicate terms that capture the semantics of text. In this case, the model can capture terms that present the concepts of the sentence, which leads to discover the topic of the document. A new concept-based model that analyzes terms on the sentence, document and corpus levels rather than the traditional analysis of document only is introduced. The concept-based model can effectively discriminate between non-important terms with respect to sentence semantics and terms which hold the concepts that represent the sentence meaning. The proposed model consists of concept-based statistical analyzer, conceptual ontological graph representation, concept extractor and concept-based similarity measure. The term which contributes to the sentence semantics is assigned two different weights by the concept-based statistical analyzer and the conceptual ontological graph representation. These two weights are combined into a new weight. The concepts that have maximum combined weights are selected by the concept extractor. The similarity between documents is calculated based on a new concept-based similarity measure. The proposed similarity measure takes full advantage of using the concept analysis measures on the sentence, document, and corpus levels in calculating the similarity between documents. Large sets of experiments using the proposed concept-based model on different datasets in text clustering, categorization and retrieval are conducted. The experiments demonstrate extensive comparison between traditional weighting and the concept-based weighting obtained by the concept-based model. Experimental results in text clustering, categorization and retrieval demonstrate the substantial enhancement of the quality using: (1) concept-based term frequency (tf), (2) conceptual term frequency (ctf), (3) concept-based statistical analyzer, (4) conceptual ontological graph, (5) concept-based combined model. In text clustering, the evaluation of results is relied on two quality measures, the F-Measure and the Entropy. In text categorization, the evaluation of results is relied on three quality measures, the Micro-averaged F1, the Macro-averaged F1 and the Error rate. In text retrieval, the evaluation of results relies on three quality measures, the precision at 10 documents retrieved P(10), the preference measure (bpref), and the mean uninterpolated average precision (MAP). All of these quality measures are improved when the newly developed concept-based model is used to enhance the quality of the text clustering, categorization and retrieval