Final report of Task #5: Current document index system for document retrieval investigation

In Part I of this report, we describe the work completed during the last fiscal year (October 1, 2002 thru September 30, 2003). The single biggest challenge this past year has been to develop and deliver a new software technology to classify Homeland Security Sensitive documents with high precision. Not only was a satisfactory system developed, an operational version was delivered to CACI in April 2003. The delivered system is called the Homeland Security Classifier (HSC). In Part II we give an overview of the projects ISRI has completed during the first four years of this cooperative agreement (October 1, 1998 thru September 30, 2002). Each of the deliverables associated with these projects has been thoroughly described in previous reports

Email Classification Using Adaptive Ontologies Learning

Email is a way of communication for the today’s internet world, private and government sector or public sector all are used email for communication with their clients. They can freely send number of mail to their client without disturbing them. Now a day email communication is also a way of advertising, some mail is also spam, lots of social mails are there. Categorization and handling lots of email is an important task for the researches, as they all are working in this field by using the Natural language processing and ontology extraction process. User get frustrated for handling lots of mails and reading those for finding there is any important mail, sometime user delete lots of mail without reading and in that case may be some important mail which contain the important information may be about meeting, seminar etc. is also deleted. For avoiding these scenarios here auto updation of schedule calendar procedure is proposed by the author. Concept extraction and clustering of concept is done based on fuzzy logic, similar mail pattern is grouped in a same cluster if similarity is less than threshold value a new cluster is defined for that. From the extracted concept author establish the relationship between them and generate the result. Computation overhead is also calculated for different set of mails and finds that it takes very less time in computing large email data set

Classification of Under-Resourced Language Documents Using English Ontology

Automatic documents classification is an important task due to the rapid growth of the number of electronic documents, which aims automatically assign the document to a predefined category based on its contents. The use of automatic document classification has been plays an important role in information extraction, summarization, text retrieval, question answering, e-mail spam detection, web page content filtering, automatic message routing , etc.Most existing methods and techniques in the field of document classification are keyword based, but due to lack of semantic consideration of this technique, it incurs low performance. In contrast, documents also be classified by taking their semantics using ontology as a knowledge base for classification; however, it is very challenging of building ontology with under-resourced language. Hence, this approach is only limited to resourced language (i.e. English) support. As a result, under-resourced language written documents are not benefited such ontology based classification approach. This paper describes the design of automatic document classification of under-resourced language written documents. In this work, we propose an approach that performs classification of under-resourced language written documents on top of English ontology. We used a bilingual dictionary with Part of Speech feature for word-by-word text translation to enable the classification of document without any language barrier. The design has a concept-mapping component, which uses lexical and semantic features to map the translated sense along the ontology concepts. Beside this, the design also has a categorization component, which determines a category of a given document based on weight of mapped concept. To evaluate the performance of the proposed approach 20-test documents for Amharic and Tigrinya and 15-test document for Afaan Oromo in each news category used. In order to observe the effect of incorporated features (i.e. lemma based index term selection, pre-processing strategies during concept mapping, lexical and semantics based concept mapping) five experimental techniques conducted. The experimental result indicated that the proposed approach with incorporation of all features and components achieved an average F-measure of 92.37%, 86.07% and 88.12% for Amharic, Afaan Oromo and Tigrinya documents respectively. Keywords: under-resourced language, Multilingual, Documents or text Classification, knowledge base, Ontology based text categorization, multilingual text classification, Ontology. DOI: 10.7176/CEIS/10-6-02 Publication date:July 31st 201

The role of ontology in information management

The question posed in this thesis is how the use of ontologies by information systems affects their development and their performance. Several aspects about ontologies are presented, namely design and implementation issues, representational languages, and tools for ontology manipulation. The effects of the combination of ontologies and information systems are then investigated. An ontology-based tool to identify email message features is presented, and its implementation and execution details are discussed. The use of ontologies by information systems provides a better understanding about their requirements, reduces their development time, and supports knowledge management during execution time

Aplicación de la ingeniería ontológica para representar la trazabilidad de un correo electrónico

Si bien la Forensia Digital avanzó en concordancia con la tecnología, aún se debe trabajar para que los resultados periciales se presenten no como información técnica sino sistemáticamente y semánticamente en el marco de la causa judicial. Resulta conveniente contar con un marco de referencia común para todos los actores judiciales. Este trabajo propone una ontología para el análisis forense de correos electrónicos, focalizándose en la representación de la trazabilidad de un correo electrónico. Se toma como caso de estudio el análisis de la cabecera de un correo electrónico y el camino de recorrido inverso de dos destinatarios del correo de ejemplo, estableciendo el modelo ontológico que permite seguir esa trazabilidad.Sociedad Argentina de Informática e Investigación Operativa (SADIO

Aplicación de la ingeniería ontológica para representar la trazabilidad de un correo electrónico

Si bien la Forensia Digital avanzó en concordancia con la tecnología, aún se debe trabajar para que los resultados periciales se presenten no como información técnica sino sistemáticamente y semánticamente en el marco de la causa judicial. Resulta conveniente contar con un marco de referencia común para todos los actores judiciales. Este trabajo propone una ontología para el análisis forense de correos electrónicos, focalizándose en la representación de la trazabilidad de un correo electrónico. Se toma como caso de estudio el análisis de la cabecera de un correo electrónico y el camino de recorrido inverso de dos destinatarios del correo de ejemplo, estableciendo el modelo ontológico que permite seguir esa trazabilidad.Sociedad Argentina de Informática e Investigación Operativa (SADIO

Aplicación de la ingeniería ontológica para representar la trazabilidad de un correo electrónico

Si bien la Forensia Digital avanzó en concordancia con la tecnología, aún se debe trabajar para que los resultados periciales se presenten no como información técnica sino sistemáticamente y semánticamente en el marco de la causa judicial. Resulta conveniente contar con un marco de referencia común para todos los actores judiciales. Este trabajo propone una ontología para el análisis forense de correos electrónicos, focalizándose en la representación de la trazabilidad de un correo electrónico. Se toma como caso de estudio el análisis de la cabecera de un correo electrónico y el camino de recorrido inverso de dos destinatarios del correo de ejemplo, estableciendo el modelo ontológico que permite seguir esa trazabilidad.Sociedad Argentina de Informática e Investigación Operativa (SADIO

MapReduce based RDF assisted distributed SVM for high throughput spam filtering

This thesis was submitted for the degree of Doctor of Philosophy and was awarded by Brunel UniversityElectronic mail has become cast and embedded in our everyday lives. Billions of legitimate emails are sent on a daily basis. The widely established underlying infrastructure, its widespread availability as well as its ease of use have all acted as catalysts to such pervasive proliferation. Unfortunately, the same can be alleged about unsolicited bulk email, or rather spam. Various methods, as well as enabling architectures are available to try to mitigate spam permeation. In this respect, this dissertation compliments existing survey work in this area by contributing an extensive literature review of traditional and emerging spam filtering approaches. Techniques, approaches and architectures employed for spam filtering are appraised, critically assessing respective strengths and weaknesses. Velocity, volume and variety are key characteristics of the spam challenge. MapReduce (M/R) has become increasingly popular as an Internet scale, data intensive processing platform. In the context of machine learning based spam filter training, support vector machine (SVM) based techniques have been proven effective. SVM training is however a computationally intensive process. In this dissertation, a M/R based distributed SVM algorithm for scalable spam filter training, designated MRSMO, is presented. By distributing and processing subsets of the training data across multiple participating computing nodes, the distributed SVM reduces spam filter training time significantly. To mitigate the accuracy degradation introduced by the adopted approach, a Resource Description Framework (RDF) based feedback loop is evaluated. Experimental results demonstrate that this improves the accuracy levels of the distributed SVM beyond the original sequential counterpart. Effectively exploiting large scale, ‘Cloud’ based, heterogeneous processing capabilities for M/R in what can be considered a non-deterministic environment requires the consideration of a number of perspectives. In this work, gSched, a Hadoop M/R based, heterogeneous aware task to node matching and allocation scheme is designed. Using MRSMO as a baseline, experimental evaluation indicates that gSched improves on the performance of the out-of-the box Hadoop counterpart in a typical Cloud based infrastructure. The focal contribution to knowledge is a scalable, heterogeneous infrastructure and machine learning based spam filtering scheme, able to capitalize on collaborative accuracy improvements through RDF based, end user feedback. MapReduce based RDF Assisted Distributed SVM for High Throughput Spam Filterin

Unificação e expansão da interface de pesquisa no webmail

Estágio realizado na PortugalmailTese de mestrado integrado. Engenharia Informática e Computação. Faculdade de Engenharia. Universidade do Porto. 200

Peer coaching: To what extent can it support the development of professional attributes required to be a teacher?

Students on a science PGCE course were introduced to peer coaching. This article describes the structures developed to enhance student teachers’ professional attributes and then reports the results. The students were given questionnaires to ascertain to what extent they felt they had developed their professional attributes as a result of being involved in peer coaching. The questionnaire design provided both qualitative and quantitative data. The evidence indicates that the peer coaching procedures had a positive impact on student teachers’ professional development. Data was analysed and has been used to draw conclusions to inform peer coaching in an education setting