Flat graphical, conceptual modeling techniques are widely accepted as visually effective ways in which to specify and communicate the conceptual data requirements of an information system. Conceptual schema diagrams provide modelers with a picture of the salient structures underlying the modeled universe of discourse, in a form that can readily be understood by and communicated to users, programmers and managers. When complexity and size of applications increase, however, the success of these techniques in terms of comprehensibility and communicability deteriorates rapidly.

Formulating non-trivial queries in relational languages such as SQL and QBE can prove daunting to end users. ConQuer is a conceptual query language that allows users to formulate queries naturally in terms of elementary relationships, operators such as “and”, “or”, “not ” and “maybe”, contextual for-clauses and object-correlation, thus avoiding the need to deal explicitly with implementation details such as relational tables, null values, outer joins, group-by clauses and correlated subqueries. While most conceptual query languages are based on the Entity-Relationship approach, ConQuer is based on Object-Role Modeling (ORM), which exposes semantic domains as conceptual object types, allowing queries to be formulated via paths through the information space. As a result of experience with the first implementation of ConQuer, the language has been substantially revised and extended to become ConQuer–II, and a new tool, ActiveQuery, has been developed with an improved interface. ConQuer-II’s new features such as arbitrary correlation and subtyping enable it to be used for a wide range of advanced conceptual queries. Introduction and Related Work

The promises of network-accessible information are increasingly difficult to achieve. These difficulties are due to a variety of causes, such as, the rapid growth in the volume of network-available information and the increasing complexity, diversity and terminological fluctuations of the different information sources available. This paper presents a conceptual architecture for the organisation information space across collections of component systems in multi-databases that provides serendipity, exploration and contextualisation support so that users can achieve logical connections between concepts they are familiar with and schema terms employed in multi-database systems. Large-scale searching for multi-database schema information is guided by a combination of lexical, structural and semantic aspects of schema terms in order to reveal more meaning both about the contents of a requested information term and about its placement within the distributed information space. 1

We propose and evaluate an adaptive approach towards content-based image retrieval (CBIR), which is based on the Ostensive Model of developing information needs. We use ostensive relevance to capture the user’s current need and tailor the retrieval accordingly. Our approach supports content-assisted browsing, by incorporating an adaptive query learning scheme based on implicit user feedback. Textual and colour features are employed to characterise images, which are combined using the Dempster-Shafer theory of evidence combination. Results from a user-centred, task-oriented evaluation show that the ostensive interface is preferred over a traditional interface with manual query facilities. Its strengths lie in its ability to adapt to the user’s need, and its very intuitive and fluid way of operation.

Abstract. We discuss an adaptive approach towards Content-Based Image Retrieval. It is based on the Ostensive Model of developing information needs—a special kind of relevance feedback model that learns from implicit user feedback and adds a temporal notion to relevance. The ostensive approach supports content-assisted browsing through visualising the interaction by adding user-selected images to a browsing path, which ends with a set of system recommendations. The suggestions are based on an adaptive query learning scheme, in which the query is learnt from previously selected images. Our approach is an adaptation of the original Ostensive Model based on textual features only, to include content-based features to characterise images. In the proposed scheme textual and colour features are combined using the Dempster-Shafer theory of evidence combination. Results from a user-centred, work-task oriented evaluation show that the ostensive interface is preferred over a traditional interface with manual query facilities. This is due to its ability to adapt to the user’s need, its intuitiveness and the fluid way in which it operates. Studying and comparing the nature of the underlying information need, it emerges that our approach elicits changes in the user’s need based on the interaction, and is successful in adapting the retrieval to match the changes. In addition, a preliminary study of the retrieval performance of the ostensive relevance feedback scheme shows that it can outperform a standard relevance

Abstract. The problems of Content-Based Image Retrieval (CBIR) systems can be attributed to the semantic gap between the low-level data representation and the high-level concepts the user associates with images, on the one hand, and the time-varying and often vague nature of the underlying information need, on the other. These problems can be addressed by improving the interaction between the user and the system. In this paper, we sketch the development of CBIR interfaces, and introduce our view on how to solve some of the problems of the studied interfaces. To address the semantic gap and long-term multifaceted information needs, we propose a “retrieval in context ” system. EGO is a tool for the management of image collections, supporting the user through personalisation and adaptation. We will describe how it learns from the user’s personal organisation, allowing it to recommend relevant images to the user. The recommendation algorithm is detailed, which is based on relevance feedback techniques. 1

Effective information disclosure in the context of databases with a large conceptual schema is known to be a non-trivial problem. In particular the formulation of ad-hoc queries is a major problem in such contexts. Existing approaches for tackling this problem include graphical query interfaces, query by navigation, query by construction, and point to point queries. In this report we propose an adoption of the query by navigation mechanism that is especially geared towards the InfoAssistant product. Query by navigation is based on ideas from the information retrieval world, in particular on the stratified hypermedia architecture.

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The eectiveness and eciency of searches for relevant documents strongly depend on key features of the descriptor language supported by the retrieval system. Eectiveness, for instance, is limited by the expressiveness of the descriptors. In addition, system eciency is proportional to tractability. Furthermore, user eort is relative to comprehensibility and compactness. From a formulation point of view, compactness allows a high degree of integration, combining several occurrences of an analogous concept in a single expression. A proper descriptor language for information discovery should thus nd a workable balance between these features. Boolean index expressions (BIEs) are proposed as balanced descriptors for the communication of information needs. Compared to many other descriptor languages, an advantage of BIEs is their compactness, oering the opportunity to convey much information in a succinct way. The goal of this article is to investigate compactness of BIEs. For i...

In this paper we study interfaces for image retrieval systems. Current image retrieval interfaces are limited to providing query facilities and result presentation. The user can inspect the results and possibly provide feedback on their relevance for the current query. Our approach, in contrast, encourages the user to group and organise their search results and thus provide more fine-grained feedback for the system. It combines the search and management process, which – according to our hypothesis – helps the user to conceptualise their search tasks and to overcome the query formulation problem. An evaluation, involving young design-professionals and different types of information seeking scenarios, shows that the proposed approach succeeds in encouraging the user to conceptualise their tasks and that it leads to increased user satisfaction. However, it could not be shown to increase performance. We identify the problems in the current setup, which when eliminated should lead to more effective searching overall.