The use of inference networks to support document retrieval is introduced. A network-basead retrieval model is described and compared to conventional probabilis-tic and Boolean models. 1

The use of inference networks to support document retrieval is introduced. A network-based retrieval model is described and compared to conventional probabilistic and Boolean models. The performance of a retrieval system based on the inference network model is evaluated and compared to performance with conventional retrieval models,

this document, text or image about?' Gradually moving from the left to the right in Figure 3.1, different understandings of this concept evolve

The objective of the paper is to amalgamate theories of text retrieval from various research traditions into a cognitive theory for information retrieval interaction. Set in a cognitive framework, the paper outlines the concept of polyrepresentation applied to both the user's cognitive space and the information space of IR systems. The concept seeks to represent the current user's information need, problem state, and domain work task or interest in a structure of causality. Further, it implies that we should apply different methods of representation and a variety of IR techniques of different cognitive and functional origin simultaneously to each semantic full-text entity in the information space. The cognitive differences imply that by applying cognitive overlaps of information objects, originating from different interpretations of such objects through time and by type, the degree of uncertainty inherent in IR is decreased. Polyrepresentation and the use of cognitive overlaps are associated with, but not identical to, data

In this paper, an introduction and survey over probabilistic information retrieval (IR) is given. First, the basic concepts of this approach are described: the probability ranking principle shows that optimum retrieval quality can be achieved under certain assumptions; a conceptual model for IR along with the corresponding event space clarify the interpretation of the probabilistic parameters involved. For the estimation of these parameters, three different learning strategies are distinguished, namely query-related, document-related and description-related learning. As a representative for each of these strategies, a specific model is described. A new approach regards IR as uncertain inference; here, imaging is used as a new technique for estimating the probabilistic parameters, and probabilistic inference networks support more complex forms of inference. Finally, the more general problems of parameter estimation, query expansion and the development of models for advanced document representations are discussed.

Many users of online and other automated information systems want to take advantage of the speed and power of automated retrieval, while still controlling and directing the steps of the search themselves. They do not want the system to take over and carry out the search entirely for them. Yet the objective of much of current theory and experimentation in information retrieval systems and interfaces is to design systems in which the user has either no or only reactive involvement with the search process. It is argued here that the advanced information retrieval research community is missing an opportunity to design systems that are in better harmony with the actual preferences of many users--sophisticated systems that provide an optimal combination of searcher control and system retrieval power. The user may be provided effective means of directing the search if capabilities specific to the information retrieval process, that is, strategic behaviors normally associated with information searching, are incorporated into the interface. There are many questions concerning 1) the degree of user vs. system involvement in the search, and 2) the size, or chunking, of activities, that is, how much and what type of activity the user should be able to direct the system to do at once. These two dimensions are analyzed and a number of configurations of system capability that combine user and system control are presented and discussed. In the process, the concept of the information search stratagem is introduced, and particular attention is paid to the provision of strategic, as opposed to purely procedural capabilities for the searcher. Finally, certain of the types of user-system relationship are selected as deserving 2 particular attention in future information retrieval system d...

The combination of different text representations and search strategies has become a standard technique for improving the effectiveness of information retrieval. Combination, for example, has been studied extensively in the TREC evaluations and is the basis of the “meta-search” engines used on the Web. This paper examines the development of this technique, including both experimental results and the retrieval models that have been proposed as formal frameworks for combination. We show that combining approaches for information retrieval can be modeled as combining the outputs of multiple classifiers based on one or more representations, and that this simple model can provide explanations for many of the experimental results. We also show that this view of combination is very similar to the inference net model, and that a new approach to retrieval based on language models supports combination and can be integrated with the inference net model.

Video is an effective medium for capturing the events in the real world around us. There exist a vast amount of video materials covering a wide range of applications. However, wide spread use of video in computer applications is often impeded by the lack of effective tools to manage video information systematically. This paper discusses the design and implementation of a frame-based video retrieval and sequencing system (VRSS). The system is designed to support the entire process of video information management: segmenting, indexing, retrieving and sequencing of video data. A semi-automatic tool is developed to divide video sequence into meaningful shots. Each video shot is logged using text descriptions, audio dialogue and cinematic attributes. A twolayered concept-based model is used as the basis to accurately retrieve relevant video shots based on users' free-text query. A cinematic rule-based virtual editing tool is also developed to sequence the video shots retrieved for presentation within a specified time constraint. The system has been tested on a video documentary on the NUS engineering faculty. The results of video retrieval experiments are encouraging.

We present an analysis of information retrieval as an information-seeking activity, supporting people's interactions with text. This analysis suggests that some assumptions underlying the standard model of information retrieval are inappropriate, and we suggest alternative assumptions and discuss their implications for information retrieval system design. It is proposed that information retrieval is most properly considered as information-seeking behavior, that the central process of information retrieval is user interaction with text, and that the user is the central component of the information retrieval system. Possible ways to incorporate this view in the design of information retrieval systems are discussed.

Intelligent Information Retrieval is concerned with the application of intelligent techniques, like for example semantic networks, neural networks and inference nets to Information Retrieval. The eld of research has seen a number of applications of Constrained Spreading Activation (CSA) techniques on domain knowledge networks. However, there has never been any application of these techniques to the World Wide Web. The Web is a very important information resource, but users nd that looking for a relevant piece of information in the Web can be like "looking for a needle in a haystack". We were therefore motivated to design and develop a prototype system, WebSCSA (Web Search by CSA), that applies a CSA technique to retrieve information from the Web using an ostensive approach to querying similar to query-by-example. In this paper we describe the system and its underlying model. Furthermore, we report on an experiment carried out with human subjects to evaluate the e ectiveness of WebSCSA. We tested whether WebSCSA improves retrieval of relevant information on top of Web search engines results and how well WebSCSA serves as an agent browser for the user. The results of the experiments are promising, and show that there is much potential for further research on the use of CSA techniques to search the Web.