The information retrieval challenge of human digital memories

Today people are storing increasing amounts of personal information in digital format. While storage of such information is becoming straight forward, retrieval from the vast personal archives that this is creating poses significant challenges. Existing retrieval techniques are good at retrieving from non-personal spaces, such as the World Wide Web. However they are not sufficient for retrieval of items from these new unstructured spaces which contain items that are personal to the individual, and of which the user has personal memories and with which has had previous interaction. We believe that there are new and exciting possibilities for retrieval from personal archives. Memory cues act as triggers for individuals in the remembering process, a better understanding of memory cues will enable us to design new and effective retrieval algorithms and systems for personal archives. Context data, such as time and location, is already proving to play a key part in this special retrieval domain, for example for searching personal photo archives, we believe there are many other rich sources of context that can be exploited for retrieval from personal archives

Using Windmill Expansion for Document Retrieval

SEMIOTIKS aims to utilise online information to support the crucial decision–making of those military and civilian agencies involved in the humanitarian removal of landmines in areas of conflict throughout the world. An analysis of the type of information required for such a task has given rise to four main areas of research: information retrieval, document annotation, summarisation and visualisation. The first stage of the research has focused on information retrieval, and a new algorithm, “Windmill Expansion” (WE) has been proposed to do this. The algorithm uses retrieval feedback techniques for automated query expansion in order to improve the effectiveness of information retrieval. WE is based on the extraction of human–generated written phases for automated query expansion. Top and Second Level expansion terms have been generated and their usefulness evaluated. The evaluation has concentrated on measuring the degree of overlap between the retrieved URLs. The less the overlap, the more useful the information provided. The Top Level expansion terms were found to provide 90% of useful URLs, and the Second Level 83% of useful URLs. Although there was a decline of useful URLs from the Top Level to the Second Level, the quantity of relevant information retrieved has increased. The originality of SEMIOTIKS lies in its use of the WE algorithm to help non–domain specific experts automatically explore domain words for relevant and precise information retrieval

Library As Place: Being Human in a Digital World

Despite the increasingly digital nature of information retrieval, both users and computers continue to occupy physical space, and the library – as place – offers an essential location for inspiration. In an age when one might assume that the digital negates the physical, a finite place can root the individual within space regarding both composition and information retrieval. In this seeking for the essentially human element of the physical book within space, we may also discover a need for the library as place

An affect-based video retrieval system with open vocabulary querying

Content-based video retrieval systems (CBVR) are creating new search and browse capabilities using metadata describing significant features of the data. An often overlooked aspect of human interpretation of multimedia data is the affective dimension. Incorporating affective information into multimedia metadata can potentially enable search using this alternative interpretation of multimedia content. Recent work has described methods to automatically assign affective labels to multimedia data using various approaches. However, the subjective and imprecise nature of affective labels makes it difficult to bridge the semantic gap between system-detected labels and user expression of information requirements in multimedia retrieval. We present a novel affect-based video retrieval system incorporating an open-vocabulary query stage based on WordNet enabling search using an unrestricted query vocabulary. The system performs automatic annotation of video data with labels of well defined affective terms. In retrieval annotated documents are ranked using the standard Okapi retrieval model based on open-vocabulary text queries. We present experimental results examining the behaviour of the system for retrieval of a collection of automatically annotated feature films of different genres. Our results indicate that affective annotation can potentially provide useful augmentation to more traditional objective content description in multimedia retrieval

Integrating memory context into personal information re-finding

Personal information archives are emerging as a new challenge for information retrieval (IR) techniques. The user’s memory plays a greater role in retrieval from person archives than from other more traditional types of information collection (e.g. the Web), due to the large overlap of its content and individual human memory of the captured material. This paper presents a new analysis on IR of personal archives from a cognitive perspective. Some existing work on personal information management (PIM) has begun to employ human memory features into their IR systems. In our work we seek to go further, we assume that for IR in PIM system terms can be weighted not only by traditional IR methods, but also taking the user’s recall reliability into account. We aim to develop algorithms that combine factors from both the system side and the user side to achieve more effective searching. In this paper, we discuss possible applications of human memory theories for this algorithm, and present results from a pilot study and a proposed model of data structure for the HDMs achieves

Modeling social information skills

In a modern economy, the most important resource consists in\ud human talent: competent, knowledgeable people. Locating the right person for\ud the task is often a prerequisite to complex problem-solving, and experienced\ud professionals possess the social skills required to find appropriate human\ud expertise. These skills can be reproduced more and more with specific\ud computer software, an approach defining the new field of social information\ud retrieval. We will analyze the social skills involved and show how to model\ud them on computer. Current methods will be described, notably information\ud retrieval techniques and social network theory. A generic architecture and its\ud functions will be outlined and compared with recent work. We will try in this\ud way to estimate the perspectives of this recent domain

User Models for Information Systems: Prospects and Problems

Expert systems attempt to model multiple aspects of human-computer interaction, including the reasoning of the human expert, the knowledge base, and characteristics and goals of the user. This paper focuses on models of the human user that are held by the system and utilized in interaction, with particular attention to information retrieval applications. User models may be classified along several dimensions, including static vs. dynamic, stated vs. inferred, and short-term vs. longterm models. The choice of the type of model will depend on a number of factors, including frequency of use, the relationship between the user and the system, the scope of the system, and the diversity of the user population. User models are most effective for well-defined tasks, domains, and user characteristics and goals. These user-system aspects tend not to be well defined in most information retrieval applications.published or submitted for publicatio