Information retrieval in documents using semantic criteria

The central problem of automatic retrieval from unformatted text is that computational devices are not adequately trained to look for associated information. However for complete understanding and information retrieval, a complete artificial intelligence would have to be built. This paper describes a method for achieving significant information retrieval by using a semantic search engine. The underlying semantic information is stored in a network of clarified words, linked by logical connections. We employ simple scoring techniques on collections of paths in this network to establish a degree of relevance between a document and a clarified search criterion. This technique has been applied with success to test examples and can be easily scaled up to search large documents.

Service level agreements framework for digital libraries quality of service management

No framework that provides practical approach for the assessment of quality of service level offered by digital libraries exists.This thesis aims to address this gap. The thesis develops a service level agreement (SLA) based mechanisms for the digital libraries to measure and assess the quality of service they deliver to their customers. An SLA is a formal contract between the digital content providers and the digital library consumers.

Algorithms and applications of preference based ranking for information retrieval

In this thesis, the author designed three sets of preference based ranking algorithms for information retrieval and provided the corresponsive applications for the algorithms. The main goal is to retrieve recommended, high similar and valuable ranking results to users.

An parallel information retrieval method for e-commerce

An online transaction always retrieves a large amount of information before making decisions. Currently, the parallel methods for retrieving such information can only provide a similar performance to serial methods. In this paper we first perform an analysis to determine the factors that affect the performance of exiting methods, i.e., HQR and EHQR, and show that the several of these factors are not considered by these methods. Motivated by this, we propose a new dispatch scheme called AEHQR, which takes into account the features of parallel dispatching. In addition, we provide cost models that determine the optimal performance achievable by any parallel dispatching method. Using experimental comparison, we illustrate that the AEHQR is significantly outperforms the HQR and EHQR under all conditions.

Impact of image organizations on multimedia document retrieval

In this paper we compare ranking effectiveness of heterogeneous multimedia document retrieval when different image organizations are used for formulating queries. The quality of image queries depends on the organization of images used to make queries which in turn significantly impacts retrieval precision. CBIR (content based information retrieval) needs an effective and efficient organization of images including user interface which must be part of the configuration parameters of image retrieval research.

Analyzing inconsistency toward enhancing integration of biological molecular databases

The rapid growth of biological databases not only provides biologists with abundant data but also presents a big challenge in relation to the analysis of data. Many data analysis approaches such as data mining, information retrieval and machine learning have been used to extract frequent patterns from diverse biological databases. However, the discrepancies, due to the differences in the structure of databases and their terminologies, result in a significant lack of interoperability. Although ontology-based approaches have been used to integrate biological databases, the inconsistent analysis of biological databases has been greatly disregarded. This paper presents a method by which to measure the degree of inconsistency between biological databases. It not only presents a guideline for correct and efficient database integration, but also exposes high quality data for data mining and knowledge discovery.

Modeling intelligent agents for web-based information gathering

The recent emergence of intelligent agent technology and advances in information gathering have been the important steps forward in efficiently managing and using the vast amount of information now available on the Web to make informed decisions. There are, however, still many problems that need to be overcome in the information gathering research arena to enable the delivery of relevant information required by end users. Good decisions cannot be made without sufficient, timely, and correct information. Traditionally it is said that knowledge is power, however, nowadays sufficient, timely, and correct information is power. So gathering relevant information to meet user information needs is the crucial step for making good decisions. The ideal goal of information gathering is to obtain only the information that users need (no more and no less). However, the volume of information available, diversity formats of information, uncertainties of information, and distributed locations of information (e.g. World Wide Web) hinder the process of gathering the right information to meet the user needs. Specifically, two fundamental issues in regard to efficiency of information gathering are mismatch and overload. The mismatch means some information that meets user needs has not been gathered (or missed out), whereas, the overload means some gathered information is not what users need. Traditional information retrieval has been developed well in the past twenty years. The introduction of the Web has changed people's perceptions of information retrieval. Usually, the task of information retrieval is considered to have the function of leading the user to those documents that are relevant to his/her information needs. The similar function in information retrieval is to filter out the irrelevant documents (or called information filtering). Research into traditional information retrieval has provided many retrieval models and techniques to represent documents and queries. Nowadays, information is becoming highly distributed, and increasingly difficult to gather. On the other hand, people have found a lot of uncertainties that are contained in the user information needs. These motivate the need for research in agent-based information gathering. Agent-based information systems arise at this moment. In these kinds of systems, intelligent agents will get commitments from their users and act on the users behalf to gather the required information. They can easily retrieve the relevant information from highly distributed uncertain environments because of their merits of intelligent, autonomy and distribution. The current research for agent-based information gathering systems is divided into single agent gathering systems, and multi-agent gathering systems. In both research areas, there are still open problems to be solved so that agent-based information gathering systems can retrieve the uncertain information more effectively from the highly distributed environments. The aim of this thesis is to research the theoretical framework for intelligent agents to gather information from the Web. This research integrates the areas of information retrieval and intelligent agents. The specific research areas in this thesis are the development of an information filtering model for single agent systems, and the development of a dynamic belief model for information fusion for multi-agent systems. The research results are also supported by the construction of real information gathering agents (e.g., Job Agent) for the Internet to help users to gather useful information stored in Web sites. In such a framework, information gathering agents have abilities to describe (or learn) the user information needs, and act like users to retrieve, filter, and/or fuse the information. A rough set based information filtering model is developed to address the problem of overload. The new approach allows users to describe their information needs on user concept spaces rather than on document spaces, and it views a user information need as a rough set over the document space. The rough set decision theory is used to classify new documents into three regions: positive region, boundary region, and negative region. Two experiments are presented to verify this model, and it shows that the rough set based model provides an efficient approach to the overload problem. In this research, a dynamic belief model for information fusion in multi-agent environments is also developed. This model has a polynomial time complexity, and it has been proven that the fusion results are belief (mass) functions. By using this model, a collection fusion algorithm for information gathering agents is presented. The difficult problem for this research is the case where collections may be used by more than one agent. This algorithm, however, uses the technique of cooperation between agents, and provides a solution for this difficult problem in distributed information retrieval systems. This thesis presents the solutions to the theoretical problems in agent-based information gathering systems, including information filtering models, agent belief modeling, and collection fusions. It also presents solutions to some of the technical problems in agent-based information systems, such as document classification, the architecture for agent-based information gathering systems, and the decision in multiple agent environments. Such kinds of information gathering agents will gather relevant information from highly distributed uncertain environments.

Detection of setting and subject information in documentary video

Interpretation of video information is a difficult task for computer vision and machine intelligence. In this paper we examine the utility of a non-image based source of information about video contents, namely the shot list, and study its use in aiding image interpretation. We show how the shot list may be analysed to produce a simple summary of the 'who and where' of a documentary or interview video. In order to detect the subject of a video we use the notion of a 'shot syntax' of a particular genre to isolate actual interview sections.

Exploiting side information in locality preserving projection

Even if the class label information is unknown, side information represents some equivalence constraints between pairs of patterns, indicating whether pairs originate from the same class. Exploiting side information, we develop algorithms to preserve both the intra-class and inter-class local structures. This new type of locality preserving projection (LPP), called LPP with side information (LPPSI), preserves the data's local structure in the sense that the close, similar training patterns will be kept close, whilst the close but dissimilar ones are separated. Our algorithms balance these conflicting requirements, and we further improve this technique using kernel methods. Experiments conducted on popular face databases demonstrate that the proposed algorithm significantly outperforms LPP. Further, we show that the performance of our algorithm with partial side information (that is, using only small amount of pair-wise similarity/dissimilarity information during training) is comparable with that when using full side information. We conclude that exploiting side information by preserving both similar and dissimilar local structures of the data significantly improves performance.

Media-independent information representation and retrieval

In this paper we propose a media-independent knowledge indexing and retrieval system as a basis for an information retrieval system. The representation allows for sharing of low level information bearing objects and at the same time allows for maintaining of user-dependent views. The tools for maintenance and manipulation of concepts focus on the user and user's intentions. The aim of the system is to provide a set of flexible tools and let the user structure the knowledge in his or her own way, instead of attempting to build an all-encompassing common sense, or general knowledge representation.

Media independent knowledge indexing and retrieval

Composing a multimedia presentation may require creation or generation of suitable images and video segments, as well as animation, sound, or special effects. Obtaining images or video sequences can be prohibitively expensive when costs of travel to location, equipment, staff, etc, are considered. Those problems can be alleviated with the use of pictorial and video digital libraries, such libraries require methods for comprehensive indexing and annotation of stored items and efficient retrieval tools.

We propose a system based on user oriented perceptions as they influence query formation in image and video retrieval. We present a method based on user dependent conceptual structures for creating and maintaining indexes to images and video sequences.

How do Digital Immigrant Teachers (DITs) learn ICT for the information age?

This study reports about teacher learning and in particular, teachers who have extensive teaching experience but limited ICT knowledge and skills. The Digital Immigrant Teachers (DITs) grew up before digital technologies; they are not frequently confident or comfortable with ICT. Like all immigrants, they have to learn new and creative ways to enhance their survival in the third millennium, where the acceleration of knowledge has allowed communication and application of information to be rapidly disseminated. In order to fully participate in the technologically rich society DITs must actively engage in the construction of authentic and purposeful learning. This research came about as a result of the digital immigrants' struggles to construct and acquire the necessary knowledge and skills to teach in the Knowledge Economy and the Information Age. The experienced present day teachers, as digital immigrants are trying to teach digital natives (Prensky, 2001 & 2003). And in order to assist these teachers in their learning ICT struggle, it is imperative to understand the teacher learning process, and the learning style through which they acquire the knowledge and skills for this new milieu.

Accessing database information using mobile agents

Many real-world applications on the Internet require accessing database information and typical technology employed is client/server plus Web (C/S + Web). Although past few years saw many success applications by using this technique, there are still some drawbacks that need to be overcome. One of the drawbacks is that the transaction often fails if the network connection is unstable. Another disadvantage is high bandwidth requirement and latency. This paper argues that mobile agent technology provide an easy way to overcome the shortcomings in CIS + Web in database access on the Internet. A success case study using mobile agents to admit new students to China's institutions is then presented. The mobile agent was created by using IBM's Aglets Software Development Kit (SDK). Based on the experimental results, it is evident that mobile agent technology is well suited for such applications.

Effectively finding relevant web pages from linkage information

This paper presents two hyperlink analysis-based algorithms to find relevant pages for a given Web page (URL). The first algorithm comes from the extended cocitation analysis of the Web pages. It is intuitive and easy to implement. The second one takes advantage of linear algebra theories to reveal deeper relationships among the Web pages and to identify relevant pages more precisely and effectively. The experimental results show the feasibility and effectiveness of the algorithms. These algorithms could be used for various Web applications, such as enhancing Web search. The ideas and techniques in this work would be helpful to other Web-related researches.