An evaluation of semantic fisheye views for opportunistic search in an annotated image collection

Visual interfaces are potentially powerful tools for users to explore a representation of a collection and opportunistically discover information that will guide them toward relevant documents. Semantic fisheye views (SFEVs) are focus + context visualization techniques that manage visual complexity by selectively emphasizing and increasing the detail of information related to the user's focus and deemphasizing or filtering less important information. In this paper we describe a prototype for visualizing an annotated image collection and an experiment to compare the effectiveness of two distinctly different SFEVs for a complex opportunistic search task. The first SFEV calculates relevance based on keyword-content similarity and the second based on conceptual relationships between images derived using WordNet. The results of the experiment suggest that semantic-guided search is significantly more effective than similarity-guided search for discovering and using domain knowledge in a collectio

The Information-seeking Strategies of Humanities Scholars Using Resources in Languages Other Than English

ABSTRACT THE INFORMATION-SEEKING STRATEGIES OF HUMANITIES SCHOLARS USING RESOURCES IN LANGUAGES OTHER THAN ENGLISH by Carol Sabbar The University of Wisconsin-Milwaukee, 2016 Under the Supervision of Dr. Iris Xie This dissertation explores the information-seeking strategies used by scholars in the humanities who rely on resources in languages other than English. It investigates not only the strategies they choose but also the shifts that they make among strategies and the role that language, culture, and geography play in the information-seeking context. The study used purposive sampling to engage 40 human subjects, all of whom are post-doctoral humanities scholars based in the United States who conduct research in a variety of languages. Data were collected through semi-structured interviews and research diaries in order to answer three research questions: What information-seeking strategies are used by scholars conducting research in languages other than English? What shifts do scholars make among strategies in routine, disruptive, and/or problematic situations? And In what ways do language, culture, and geography play a role in the information-seeking context, especially in the problematic situations? The data were then analyzed using grounded theory and the constant comparative method. A new conceptual model – the information triangle – was used and is presented in this dissertation to categorize and visually map the strategies and shifts. Based on data collected, thirty distinct strategies were identified and divided into four categories: formal system, informal resource, interactive human, and hybrid strategies. Three types of shifts were considered: planned, opportunistic, and alternative. Finally, factors related to language, culture, and geography were identified and analyzed according to their roles in the information-seeking context. This study is the first of its kind to combine the study of information-seeking behaviors with the factors of language, culture, and geography, and as such, it presents numerous methodological and practical implications along with many opportunities for future research

Classifying complex topics using spatial-semantic document visualization: An evaluation of an interaction model to support open-ended search tasks

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.In this dissertation we propose, test and develop a novel search interaction model to address two key problems associated with conducting an open-ended search task within a classical information retrieval system: (i) the need to reformulate the query within the context of a shifting conception of the problem and (ii) the need to integrate relevant results across a number of separate results sets. In our model the user issues just one highrecall query and then performs a sequence of more focused, distinct aspect searches by browsing the static structured context of a spatial-semantic visualization of this retrieved document set. Our thesis is that unsupervised spatial-semantic visualization can automatically classify retrieved documents into a two-level hierarchy of relevance. In particular we hypothesise that the locality of any given aspect exemplar will tend to comprise a sufficient proportion of same-aspect documents to support a visually guided strategy for focused, same-aspect searching that we term the aspect cluster growing strategy. We examine spatial-semantic classification and potential aspect cluster growing performance across three scenarios derived from topics and relevance judgements from the TREC test collection. Our analyses show that the expected classification can be represented in spatial-semantic structures created from document similarities computed by a simple vector space text analysis procedure. We compare two diametrically opposed approaches to layout optimisation: a global approach that focuses on preserving the all similarities and a local approach that focuses only on the strongest similarities. We find that the local approach, based on a minimum spanning tree of similarities, produces a better classification and, as observed from strategy simulation, more efficient aspect cluster growing performance in most situations, compared to the global approach of multidimensional scaling. We show that a small but significant proportion of aspect clustering growing cases can be problematic, regardless of the layout algorithm used. We identify the characteristics of these cases and, on this basis, demonstrate a set of novel interactive tools that provide additional semantic cues to aid the user in locating same-aspect documents

Creating sparks: comparing search results using discriminatory search term word co-occurrence to facilitate serendipity in the enterprise.

Categories or tags that appear in faceted search interfaces which are representative of an information item, rarely convey unexpected or non-obvious associated concepts buried within search results. No prior research has been identified which assesses the usefulness of discriminative search term word co-occurrence to generate facets to act as catalysts to facilitate insightful and serendipitous encounters during exploratory search. In this study, 53 scientists from two organisations interacted with semi-interactive stimuli, 74% expressing a large/moderate desire to use such techniques within their workplace. Preferences were shown for certain algorithms and colour coding. Insightful and serendipitous encounters were identified. These techniques appear to offer a significant improvement over existing approaches used within the study organisations, providing further evidence that insightful and serendipitous encounters can be facilitated in the search user interface. This research has implications for organisational learning, knowledge discovery and exploratory search interface design

Designing digital constructive visualization tools

The emergence of tools that support fast and easy creation of visualizations has made the benefits of Information Visualization (InfoVis) more accessible. The predominant design for visualization authoring tools often includes features such as automated mappings and visualization templates, which make tools effective and easy-to-use. These features, however, still impose barriers to non-experts (i.e., people with no formal training on visualization concepts). The paradigm of Constructive Visualization (ConstructiveVis) has shown potential to overcome some of these barriers, but it has only been investigated through the use of physical tokens that people manipulate to create representations of data. This dissertation investigates how the principles of ConstructiveVis can be applied in the design and implementation of digital constructive visualization tools. This thesis presents the results of several observational studies that uncover how tools that promote a constructive approach to visualization compare to more conventional ones. It also sheds light on what kind of benefits and limitations digital ConstructiveVis brings into non-experts' visualization design process. The investigations here presented lay the foundations for the design of better visualization tools that not only allow people to create effective visualizations but also promote critical reflection on design principles

Adaptive Visualization for Focused Personalized Information Retrieval

The new trend on the Web has totally changed todays information access environment. The traditional information overload problem has evolved into the qualitative level beyond the quantitative growth. The mode of producing and consuming information is changing and we need a new paradigm for accessing information.Personalized search is one of the most promising answers to this problem. However, it still follows the old interaction model and representation method of classic information retrieval approaches. This limitation can harm the potential of personalized search, with which users are intended to interact with the system, learn and investigate the problem, and collaborate with the system to reach the final goal.This dissertation proposes to incorporate interactive visualization into personalized search in order to overcome the limitation. By combining the personalized search and the interac- tive visualization, we expect our approach will be able to help users to better explore the information space and locate relevant information more efficiently.We extended a well-known visualization framework called VIBE (Visual Information Browsing Environment) and implemented Adaptive VIBE, so that it can fit into the per- sonalized searching environment. We tested the effectiveness of this adaptive visualization method and investigated its strengths and weaknesses by conducting a full-scale user study.We also tried to enrich the user models with named-entities considering the possibility that the traditional keyword-based user models could harm the effectiveness of the system in the context of interactive information retrieval.The results of the user study showed that the Adaptive VIBE could improve the precision of the personalized search system and could help the users to find out more diverse set of information. The named-entity based user model integrated into Adaptive VIBE showed improvements of precision of user annotations while maintaining the level of diverse discovery of information