Multi-model adaptive spatial hypertext

Information delivery on the Web often relies on general purpose Web pages that require the reader to adapt to them. This limitation is addressed by approaches such as spatial hypermedia and adaptive hypermedia. Spatial hypermedia augments the representation power of hypermedia and adaptive hypermedia explores the automatic modification of the presentation according to user needs. This dissertation merges these two approaches, combining the augmented expressiveness of spatial hypermedia with the flexibility of adaptive hypermedia. This dissertation presents the Multi-model Adaptive Spatial Hypermedia framework (MASH). This framework provides the theoretical grounding for the augmentation of spatial hypermedia with dynamic and adaptive functionality and, based on their functionality, classifies systems as generative, interactive, dynamic or adaptive spatial hypermedia. Regarding adaptive hypermedia, MASH proposes the use of multiple independent models that guide the adaptation of the presentation in response to multiple relevant factors. The framework is composed of four parts: a general system architecture, a definition of the fundamental concepts in spatial hypermedia, an ontological classification of the adaptation strategies, and the philosophy of conflict management that addresses the issue of multiple independent models providing contradicting adaptation suggestions. From a practical perspective, this dissertation produced WARP, the first MASH-based system. WARPs novel features include spatial transclusion links as an alternative to navigational linking, behaviors supporting dynamic spatial hypermedia, and personal annotations to spatial hypermedia. WARP validates the feasibility of the multi-model adaptive spatial hypermedia and allows the exploration of other approaches such as Web-based spatial hypermedia, distributed spatial hypermedia, and interoperability issues between spatial hypermedia systems. In order to validate the approach, a user study comparing non-adaptive to adaptive spatial hypertext was conducted. The study included novice and advanced users and produced qualitative and quantitative results. Qualitative results revealed the emergence of reading behaviors intrinsic to spatial hypermedia. Users moved and modified the objects in order to compare and group objects and to keep track of what had been read. Quantitative results confirmed the benefits of adaptation and indicated a possible synergy between adaptation and expertise. In addition, the study created the largest spatial hypertext to date in terms of textual content

Studying the Structure of Terrorist Networks: A Web Structural Mining Approach

Because terrorist organizations often operate in network forms where individual terrorists collaborate with each other to carry out attacks, we could gain valuable knowledge about the terrorist organizations by studying structural properties of such terrorist networks. However, previous studies of terrorist network structure have generated little actionable results. This is due to the difficulty in collecting and accessing reliable data and the lack of advanced network analysis methodologies in the field. To address these problems, we introduced the Web structural mining technique into the terrorist network analysis field which, to the best our knowledge, has never been done before. We employed the proposed technique on a Global Salafi Jihad network dataset collected through a large scale empirical study. Results from our analysis not only provide insights for terrorism research community but also support decision making in law-reinforcement, intelligence, and security domains to make our nation safer

Focused Crawler Optimization Using Genetic Algorithm

As the size of the Web continues to grow, searching it for useful information has become more difficult. Focused crawler intends to explore the Web conform to a specific topic. This paper discusses the problems caused by local searching algorithms. Crawler can be trapped within a limited Web community and overlook suitable Web pages outside its track. A genetic algorithm as a global searching algorithm is modified to address the problems. The genetic algorithm is used to optimize Web crawling and to select more suitable Web pages to be fetched by the crawler. Several evaluation experiments are conducted to examine the effectiveness of the approach. The crawler delivers collections consist of 3396 Web pages from 5390 links which had been visited, or filtering rate of Roulette-Wheel selection at 63% and precision level at 93% in 5 different categories. The result showed that the utilization of genetic algorithm had empowered focused crawler to traverse the Web comprehensively, despite it relatively small collections. Furthermore, it brought up a great potential for building an exemplary collections compared to traditional focused crawling methods

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Building domain-specific web collections for scientific digital libraries: A meta-search enhanced focused crawling method

Collecting domain-specific documents from the Web using focused crawlers has been considered one of the most important strategies to build digital libraries that serve the scientific community. However, because most focused crawlers use local search algorithms to traverse the Web space, they could be easily trapped within a limited sub-graph of the Web that surrounds the starting URLs and build domain-specific collections that are not comprehensive and diverse enough to scientists and researchers. In this study, we investigated the problems of traditional focused crawlers caused by local search algorithms and proposed a new crawling approach, meta-search enhanced focused crawling, to address the problems. We conducted two user evaluation experiments to examine the performance of our proposed approach and the results showed that our approach could build domain-specific collections with higher quality than traditional focused crawling techniques

Modelling naturalistic argumentation in research literatures: representation and interaction design issues

This paper characterises key weaknesses in the ability of current digital libraries to support scholarly inquiry, and as a way to address these, proposes computational services grounded in semiformal models of the naturalistic argumentation commonly found in research lteratures. It is argued that a design priority is to balance formal expressiveness with usability, making it critical to co-evolve the modelling scheme with appropriate user interfaces for argument construction and analysis. We specify the requirements for an argument modelling scheme for use by untrained researchers, describe the resulting ontology, contrasting it with other domain modelling and semantic web approaches, before discussing passive and intelligent user interfaces designed to support analysts in the construction, navigation and analysis of scholarly argument structures in a Web-based environment