Building Knowledge Management System for Researching Terrorist Groups on the Web

Nowadays, terrorist organizations have found a cost-effective resource to advance their courses by posting high-impact Web sites on the Internet. This alternate side of the Web is referred to as the “Dark Web.” While counterterrorism researchers seek to obtain and analyze information from the Dark Web, several problems prevent effective and efficient knowledge discovery: the dynamic and hidden character of terrorist Web sites, information overload, and language barrier problems. This study proposes an intelligent knowledge management system to support the discovery and analysis of multilingual terrorist-created Web data. We developed a systematic approach to identify, collect and store up-to-date multilingual terrorist Web data. We also propose to build an intelligent Web-based knowledge portal integrated with advanced text and Web mining techniques such as summarization, categorization and cross-lingual retrieval to facilitate the knowledge discovery from Dark Web resources. We believe our knowledge portal provide counterterrorism research communities with valuable datasets and tools in knowledge discovery and sharing

Building Web Directories in Different Languages for Decision Support: A Semi-Automatic Approach

Web directories organize voluminous information into hierarchical structures, helping users to quickly locate relevant information and to support decision-making. The development of existing Web directories either relies on expert participation that may not be available or uses automatic approaches that lack precision. As more users access the Web in their native languages, better approaches to organizing and developing non-English Web directories are needed. In this paper, we have proposed a semi-automatic approach to building domain-specific Web directories in different languages by combining human precision and machine efficiency. Using the approach, we have built Web directories in the Spanish business (SBiz) and Arabic medical (AMed) domains. Experimental results show that the SBiz and AMed directories achieved significantly better recall, F value, and satisfaction rating than benchmark directories. These encouraging results show that the approach can be used to build high-quality Web directories to support decision-making

A Framework for Application Specific Knowledge Engines

The amount of information on the Internet has been proliferated rapidly in recent years as new technologies and applications become popular. The broad heterogeneous contents bring us a substantial challenge in the field of knowledge discovery and information retrieval. The objective of this dissertation is to design and implement a systematic framework to help users access huge and various information on the Web by combining different techniques and algorithms in different domains. In this dissertation, we propose an effective Application Specific Knowledge Engine framework to build structured and semantic data repositories, and support keyword search and semantic search. The framework is consistent with the architecture of most search engines. It enhances the general search engines in three ways: various data retrieval ability; semantic data support; and post-retrieval analysis. Various techniques and algorithms that could facilitate knowledge discovery are used in the framework.In the first part, we review different types of data on the Internet and approaches to retrieve various data: structured and unstructured data, online community data, and Peer-to-Peer data. After that we present an overview of the system architecture of the ASKE framework, and especially discuss the core components of the framework in details.The following chapters aim to investigate how the ASKE framework can be applied in two different domains (counter-terrorism and anti-piracy). We present the research in developing a counter-terrorism knowledge portal that incorporates various data collection and post-retrieval analysis. The process of building the portal following ASKE framework is described. The details of the data collections of Web sites and online forums are also reported. In the anti-piracy domain, we mainly discuss building Peer-to-Peer data collection and serving users with customized profiles. A case study of monitoring the movie Watchmen piracy on typical Peer-to-Peer Networks is discussed also.This dissertation has two main contributions. Firstly, it demonstrates how information retrieval, Web mining and other artificial intelligence techniques can be used in heterogeneous environment. Secondly, it provides a feasible framework which can facilitate users to discover knowledge in their specific searching and browsing activities.Embargo: Release after 5/3/201

Exploring the Dark Side of the Web: Collection and Analysis of U.S. Extremist Online Forums

Abstract. Contents in extremist online forums are invaluable data sources for extremism reseach. In this study, we propose a systematic Web mining approach to collecting and monitoring extremist forums. Our proposed approach identifies extremist forums from various resources, addresses practical issues faced by researchers and experts in the extremist forum collection process. Suc

Zhou and Qin et al. Building Knowledge Management System for Researching Terrorist Groups on the Web ABSTRACT

Nowadays, terrorist organizations have found a cost-effective resource to advance their courses by posting high-impact Web sites on the Internet. This alternate side of the Web is referred to as the “Dark Web. ” While counterterrorism researchers seek to obtain and analyze information from the Dark Web, several problems prevent effective and efficient knowledge discovery: the dynamic and hidden character of terrorist Web sites, information overload, and language barrier problems. This study proposes an intelligent knowledge management system to support the discovery and analysis of multilingual terrorist-created Web data. We developed a systematic approach to identify, collect and store up-to-date multilingual terrorist Web data. We also propose to build an intelligent Web-based knowledge portal integrated with advanced text and Web mining techniques such as summarization, categorization and cross-lingual retrieval to facilitate the knowledge discovery from Dark Web resources. We believe our knowledge portal provide counterterrorism research communities with valuable datasets and tools in knowledge discovery and sharing