Agent-based Similarity-aware Web Document Pre-fetching

This paper presents an agent-based similarity-aware Web document pre-fetching scheme that is built on the similarity-aware Web caching architecture. A set of agents are employed to carry out certain duties such as document similarity detection, identification of relevant access patterns, document prediction and network traffic monitoring for document pre-fetching. Preliminary simulations have been conducted to evaluate the proposed scheme, and the results have shown that the new pre-fetching scheme outperforms existing Web-document pre-fetching algorithm

Discovering the Impact of Knowledge in Recommender Systems: A Comparative Study

Recommender systems engage user profiles and appropriate filtering techniques to assist users in finding more relevant information over the large volume of information. User profiles play an important role in the success of recommendation process since they model and represent the actual user needs. However, a comprehensive literature review of recommender systems has demonstrated no concrete study on the role and impact of knowledge in user profiling and filtering approache. In this paper, we review the most prominent recommender systems in the literature and examine the impression of knowledge extracted from different sources. We then come up with this finding that semantic information from the user context has substantial impact on the performance of knowledge based recommender systems. Finally, some new clues for improvement the knowledge-based profiles have been proposed.Comment: 14 pages, 3 tables; International Journal of Computer Science & Engineering Survey (IJCSES) Vol.2, No.3, August 201

Biologically Motivated Distributed Designs for Adaptive Knowledge Management

We discuss how distributed designs that draw from biological network metaphors can largely improve the current state of information retrieval and knowledge management of distributed information systems. In particular, two adaptive recommendation systems named TalkMine and @ApWeb are discussed in more detail. TalkMine operates at the semantic level of keywords. It leads different databases to learn new and adapt existing keywords to the categories recognized by its communities of users using distributed algorithms. @ApWeb operates at the structural level of information resources, namely citation or hyperlink structure. It relies on collective behavior to adapt such structure to the expectations of users. TalkMine and @ApWeb are currently being implemented for the research library of the Los Alamos National Laboratory under the Active Recommendation Project. Together they define a biologically motivated information retrieval system, recommending simultaneously at the level of user knowledge categories expressed in keywords, and at the level of individual documents and their associations to other documents. Rather than passive information retrieval, with this system, users obtain an active, evolving interaction with information resources.Comment: To appear in Design Principles for the Immune System and Other Distributed Autonomous Systems. i. Cohen and L. Segel (Eds.). Oxford University Pres

The application of workflows to digital heritage systems

Digital heritage systems usually handle a rich and varied mix of digital objects, accompanied by complex and intersecting workflows and processes. However, they usually lack effective workflow management within their components as evident in the lack of integrated solutions that include workflow components. There are a number of reasons for this limitation in workflow management utilization including some technical challenges, the unique nature of each digital resource and the challenges imposed by the environments and infrastructure in which such systems operate. This thesis investigates the concept of utilizing Workflow Management Systems (WfMS) within Digital Library Systems, and more specifically in online Digital Heritage Resources. The research work conducted involved the design and development of a novel experimental WfMS to test the viability of effective workflow management on the complex processes that exist in digital library and heritage resources. This rarely studied area of interest is covered by analyzing evolving workflow management technologies and paradigms. The different operational and technological aspects of these systems are evaluated while focusing on the areas that traditional systems often fail to address. A digital heritage resource was created to test a novel concept called DISPLAYS (Digital Library Services for Playing with Antiquity and Shared Heritage), which provides digital heritage content: creation, archival, exposition, presentation and interaction services for digital heritage collections. Based on DISPLAYS, a specific digital heritage resource was created to validate its concept and, more importantly, to act as a test bed to validate workflow management for digital heritage resources. This DISPLAYS type system implementation was called the Reanimating Cultural Heritage resource, for which three core components are the archival, retrieval and presentation components. To validate workflow management and its concepts, another limited version of these reanimating cultural heritage components was implemented within a workflow management host to test if the workflow technology is a viable choice for managing control and dataflow within a digital heritage system: this was successfully proved

A Context Centric Model for building a Knowledge advantage Machine Based on Personal Ontology Patterns

Throughout the industrial era societal advancement could be attributed in large part to introduction a plethora of electromechanical machines all of which exploited a key concept known as Mechanical Advantage. In the post-industrial era exploitation of knowledge is emerging as the key enabler for societal advancement. With the advent of the Internet and the Web, while there is no dearth of knowledge, what is lacking is an efficient and practical mechanism for organizing knowledge and presenting it in a comprehensible form appropriate for every context. This is the fundamental problem addressed by my dissertation.;We begin by proposing a novel architecture for creating a Knowledge Advantage Machine (KaM), one which enables a knowledge worker to bring to bear a larger amount of knowledge to solve a problem in a shorter time. This is analogous to an electromechanical machine that enables an industrial worker to bring to bear a large amount of power to perform a task thus improving worker productivity. This work is based on the premise that while a universal KaM is beyond the realm of possibility, a KaM specific to a particular type of knowledge worker is realizable because of the limited scope of his/her personal ontology used to organize all relevant knowledge objects.;The proposed architecture is based on a society of intelligent agents which collaboratively discover, markup, and organize relevant knowledge objects into a semantic knowledge network on a continuing basis. This in-turn is exploited by another agent known as the Context Agent which determines the current context of the knowledge worker and makes available in a suitable form the relevant portion of the semantic network. In this dissertation we demonstrate the viability and extensibility of this architecture by building a prototype KaM for one type of knowledge worker such as a professor

WAQS : a web-based approximate query system

The Web is often viewed as a gigantic database holding vast stores of information and provides ubiquitous accessibility to end-users. Since its inception, the Internet has experienced explosive growth both in the number of users and the amount of content available on it. However, searching for information on the Web has become increasingly difficult. Although query languages have long been part of database management systems, the standard query language being the Structural Query Language is not suitable for the Web content retrieval. In this dissertation, a new technique for document retrieval on the Web is presented. This technique is designed to allow a detailed retrieval and hence reduce the amount of matches returned by typical search engines. The main objective of this technique is to allow the query to be based on not just keywords but also the location of the keywords within the logical structure of a document. In addition, the technique also provides approximate search capabilities based on the notion of Distance and Variable Length Don\u27t Cares. The proposed techniques have been implemented in a system, called Web-Based Approximate Query System, which contains an SQL-like query language called Web-Based Approximate Query Language. Web-Based Approximate Query Language has also been integrated with EnviroDaemon, an environmental domain specific search engine. It provides EnviroDaemon with more detailed searching capabilities than just keyword-based search. Implementation details, technical results and future work are presented in this dissertation

Text mining with the WEBSOM

The emerging field of text mining applies methods from data mining and exploratory data analysis to analyzing text collections and to conveying information to the user in an intuitive manner. Visual, map-like displays provide a powerful and fast medium for portraying information about large collections of text. Relationships between text items and collections, such as similarity, clusters, gaps and outliers can be communicated naturally using spatial relationships, shading, and colors. In the WEBSOM method the self-organizing map (SOM) algorithm is used to automatically organize very large and high-dimensional collections of text documents onto two-dimensional map displays. The map forms a document landscape where similar documents appear close to each other at points of the regular map grid. The landscape can be labeled with automatically identified descriptive words that convey properties of each area and also act as landmarks during exploration. With the help of an HTML-based interactive tool the ordered landscape can be used in browsing the document collection and in performing searches on the map. An organized map offers an overview of an unknown document collection helping the user in familiarizing herself with the domain. Map displays that are already familiar can be used as visual frames of reference for conveying properties of unknown text items. Static, thematically arranged document landscapes provide meaningful backgrounds for dynamic visualizations of for example time-related properties of the data. Search results can be visualized in the context of related documents. Experiments on document collections of various sizes, text types, and languages show that the WEBSOM method is scalable and generally applicable. Preliminary results in a text retrieval experiment indicate that even when the additional value provided by the visualization is disregarded the document maps perform at least comparably with more conventional retrieval methods.reviewe

Facilitating file retrieval on resource limited devices

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.The rapid development of mobile technologies has facilitated users to generate and store files on mobile devices. However, it has become a challenging issue for users to search efficiently and effectively for files of interest in a mobile environment that involves a large number of mobile nodes. In this thesis, file management and retrieval alternatives have been investigated to propose a feasible framework that can be employed on resource-limited devices without altering their operating systems. The file annotation and retrieval framework (FARM) proposed in the thesis automatically annotates the files with their basic file attributes by extracting them from the underlying operating system of the device. The framework is implemented in the JME platform as a case study. This framework provides a variety of features for managing the metadata and file search features on the device itself and on other devices in a networked environment. FARM not only automates the file-search process but also provides accurate results as demonstrated by the experimental analysis. In order to facilitate a file search and take advantage of the Semantic Web Technologies, the SemFARM framework is proposed which utilizes the knowledge of a generic ontology. The generic ontology defines the most common keywords that can be used as the metadata of stored files. This provides semantic-based file search capabilities on low-end devices where the search keywords are enriched with additional knowledge extracted from the defined ontology. The existing frameworks annotate image files only, while SemFARM can be used to annotate all types of files. Semantic heterogeneity is a challenging issue and necessitates extensive research to accomplish the aim of a semantic web. For this reason, significant research efforts have been made in recent years by proposing an enormous number of ontology alignment systems to deal with ontology heterogeneities. In the process of aligning different ontologies, it is essential to encompass their semantic, structural or any system-specific measures in mapping decisions to produce more accurate alignments. The proposed solution, in this thesis, for ontology alignment presents a structural matcher, which computes the similarity between the super-classes, sub-classes and properties of two entities from different ontologies that require aligning. The proposed alignment system (OARS) uses Rough Sets to aggregate the results obtained from various matchers in order to deal with uncertainties during the mapping process of entities. The OARS uses a combinational approach by using a string-based and linguistic-based matcher, in addition to structural-matcher for computing the overall similarity between two entities. The performance of the OARS is evaluated in comparison with existing state of the art alignment systems in terms of precision and recall. The performance tests are performed by using benchmark ontologies and the results show significant improvements, specifically in terms of recall on all groups of test ontologies. There is no such existing framework, which can use alignments for file search on mobile devices. The ontology alignment paradigm is integrated in the SemFARM to further enhance the file search features of the framework as it utilises the knowledge of more than one ontology in order to perform a search query. The experimental evaluations show that it performs better in terms of precision and recall where more than one ontology is available when searching for a required file.Education Commission of Pakistan and the University of Engineering & Technology, Peshawa

Topic Maps : a bibliometric study

Topic Maps is an international standard (ISO/IEC 13250) to describe and encode knowledge structures and associating them with relevant information resources. This thesis seeks to investigate what has been written about Topic Maps from year 2000 to 2011, as well as finding out the research and publication trend in Topic Maps. This study was based on quantitative methodology, which was bibliometric analysis. The data was collected from Scopus and Web of Knowledge databases. Search keywords used are “topic map”, “topic maps” and “ISO/IEC 13250”. A total of 356 publications (265 conference papers, 91 journal articles) from 2001 to 2011 taken into data analysis. The findings revealed that Topic Maps researchers had a preference to present their findings in conference rather than in journal. The authorship pattern was more towards coauthorship. Most researchers were coauthored locally, as international collaboration was very low. Computer science and library and information science related journals were the favourite publishing venue. Majority of the conferences were computer science and education related. The focus of the topic maps was on data integration and interoperability (2001-2004), information theory (2005 – 2008), knowledge and intelligent based system (2009 – 2011). Also, there were five themes identified, namely content management, repository, ontology, information architecture, retrieval and navigation, and semantic web. The future research areas will possibly be collaborative e-learning system, knowledge visualization system, visualization construction, semantic metadata creation from a relational database, knowledge navigation and retrieval improvement, intelligent topic map, distributed knowledge management based on extended topic maps, knowledge service system, knowledge representation modeling, and multi granularity and multi-level knowledge.Joint Master Degree in Digital Library Learning (DILL