Supporting Exploratory Search Tasks Through Alternative Representations of Information

Information seeking is a fundamental component of many of the complex tasks presented to us, and is often conducted through interactions with automated search systems such as Web search engines. Indeed, the ubiquity of Web search engines makes information so readily available that people now often turn to the Web for all manners of information seeking needs. Furthermore, as the range of online information seeking tasks grows, more complex and open-ended search activities have been identified. One type of complex search activities that is of increasing interest to researchers is exploratory search, where the goal involves "learning" or "investigating", rather than simply "looking-up". Given the massive increase in information availability and the use of online search for tasks beyond simply looking-up, researchers have noted that it becomes increasingly challenging for users to effectively leverage the available online information for complex and open-ended search activities. One of the main limitations of the current document retrieval paradigm offered by modern search engines is that it provides a ranked list of documents as a response to the searcher’s query with no further support for locating and synthesizing relevant information. Therefore, the searcher is left to find and make sense of useful information in a massive information space that lacks any overview or conceptual organization. This thesis explores the impact of alternative representations of search results on user behaviors and outcomes during exploratory search tasks. Our inquiry is inspired by the premise that exploratory search tasks require sensemaking, and that sensemaking involves constructing and interacting with representations of knowledge. As such, in order to provide the searchers with more support in performing exploratory activities, there is a need to move beyond the current document retrieval paradigm by extending the support for locating and externalizing semantic information from textual documents and by providing richer representations of the extracted information coupled with mechanisms for accessing and interacting with the information in ways that support exploration and sensemaking. This dissertation presents a series of discrete research endeavour to explore different aspects of providing information and presenting this information in ways that both extraction and assimilation of relevant information is supported. We first address the problem of extracting information – that is more granular than documents – as a response to a user's query by developing a novel information extraction system to represent documents as a series of entity-relationship tuples. Next, through a series of designing and evaluating alternative representations of search results, we examine how this extracted information can be represented such that it extends the document-based search framework's support for exploratory search tasks. Finally, we assess the ecological validity of this research by exploring error-prone representations of search results and how they impact a searcher's ability to leverage our representations to perform exploratory search tasks. Overall, this research contributes towards designing future search systems by providing insights into the efficacy of alternative representations of search results for supporting exploratory search activities, culminating in a novel hybrid representation called Hierarchical Knowledge Graphs (HKG). To this end we propose and develop a framework that enables a reliable investigation of the impact of different representations and how they are perceived and utilized by information seekers

Contributions to the science of controlled transformation

writing completed in april 2013My research activities pertain to "Informatics" and in particular "Interactive Graphics" i.e. dynamic graphics on a 2D screen that a user can interact with by means of input devices such as a mouse or a multitouch surface. I have conducted research on Interactive Graphics along three themes: interactive graphics development (how should developers design the architecture of the code corresponding to graphical interactions?), interactive graphic design (what graphical interactions should User Experience (UX) specialists use in their system?) and interactive graphics design process (how should UX specialists design? Which method should they apply?) I invented the MDPC architecture that relies on Picking views and Inverse transforms. This improves the modularity of programs and improves the usability of the specification and the implementation of interactive graphics thanks to the simplification of description. In order to improve the performance of rich-graphic software using this architecture, I explored the concepts of graphical compilers and led a PhD thesis on the topic. The thesis explored the approach and contributed both in terms of description simplification and of software engineering facilitation. Finally, I have applied the simplification of description principles to the problem of shape covering avoidance by relying on new efficient hardware support for parallelized and memory-based algorithms. Together with my colleagues, we have explored the design and assessment of expanding targets, animation and sound, interaction with numerous tangled trajectories, multi-user interaction and tangible interaction. I have identified and defined Structural Interaction, a new interaction paradigm that follows the steps of the direct and instrumental interaction paradigms. I directed a PhD thesis on this topic and together with my student we designed and assessed interaction techniques for structural interaction. I was involved in the design of the "Technology Probes" concept i.e. runnable prototypes to feed the design process. Together with colleagues, I designed VideoProbe, one such Technology Probe. I became interested in more conceptual tools targeted at graphical representation. I led two PhD theses on the topic and explored the characterization of visualization, how to design representations with visual variables or ecological perception and how to design visual interfaces to improve visual scanning. I discovered that those conceptual tools could be applied to programming languages and showed how the representation of code, be it textual or "visual" undergoes visual perception phenomena. This has led me to consider our discipline as the "Science of Controlled Transformations". The fifth chapter is an attempt at providing this new account of "Informatics" based on what users, programmers and researchers actually do with interactive systems. I also describe how my work can be considered as contributing to the science of controlled transformations

Contributions to the cornerstones of interaction in visualization: strengthening the interaction of visualization

Visualization has become an accepted means for data exploration and analysis. Although interaction is an important component of visualization approaches, current visualization research pays less attention to interaction than to aspects of the graphical representation. Therefore, the goal of this work is to strengthen the interaction side of visualization. To this end, we establish a unified view on interaction in visualization. This unified view covers four cornerstones: the data, the tasks, the technology, and the human.Visualisierung hat sich zu einem unverzichtbaren Werkzeug für die Exploration und Analyse von Daten entwickelt. Obwohl Interaktion ein wichtiger Bestandteil solcher Werkzeuge ist, wird der Interaktion in der aktuellen Visualisierungsforschung weniger Aufmerksamkeit gewidmet als Aspekten der graphischen Repräsentation. Daher ist es das Ziel dieser Arbeit, die Interaktion im Bereich der Visualisierung zu stärken. Hierzu wird eine einheitliche Sicht auf Interaktion in der Visualisierung entwickelt

Implicit feedback for interactive information retrieval

Searchers can find the construction of query statements for submission to Information Retrieval (IR) systems a problematic activity. These problems are confounded by uncertainty about the information they are searching for, or an unfamiliarity with the retrieval system being used or collection being searched. On the World Wide Web these problems are potentially more acute as searchers receive little or no training in how to search effectively. Relevance feedback (RF) techniques allow searchers to directly communicate what information is relevant and help them construct improved query statements. However, the techniques require explicit relevance assessments that intrude on searchers’ primary lines of activity and as such, searchers may be unwilling to provide this feedback. Implicit feedback systems are unobtrusive and make inferences of what is relevant based on searcher interaction. They gather information to better represent searcher needs whilst minimising the burden of explicitly reformulating queries or directly providing relevance information. In this thesis I investigate implicit feedback techniques for interactive information retrieval. The techniques proposed aim to increase the quality and quantity of searcher interaction and use this interaction to infer searcher interests. I develop search interfaces that use representations of the top-ranked retrieved documents such as sentences and summaries to encourage a deeper examination of search results and drive the information seeking process. Implicit feedback frameworks based on heuristic and probabilistic approaches are described. These frameworks use interaction to identify needs and estimate changes in these needs during a search. The evidence gathered is used to modify search queries and make new search decisions such as re-searching the document collection or restructuring already retrieved information. The term selection models from the frameworks and elsewhere are evaluated using a simulation-based evaluation methodology that allows different search scenarios to be modelled. Findings show that the probabilistic term selection model generated the most effective search queries and learned what was relevant in the shortest time. Different versions of an interface that implements the probabilistic framework are evaluated to test it with human subjects and investigate how much control they want over its decisions. The experiment involved 48 subjects with different skill levels and search experience. The results show that searchers are happy to delegate responsibility to RF systems for relevance assessment (through implicit feedback), but not more severe search decisions such as formulating queries or selecting retrieval strategies. Systems that help searchers make these decisions are preferred to those that act directly on their behalf or await searcher action

Social Feedback: Social Learning from Interaction History to Support Information Seeking on the Web

Information seeking on the Web has become a central part of many daily activities. Even though information seeking is extremely common, there are many times when these tasks are unsuccessful, because the information found is less than ideal or the task could have been completed more efficiently. In unsuccessful information-seeking tasks, there are often other people who have knowledge or experience that could help improve task success. However, information seekers do not typically look for help from others, because tasks can often be completed alone (even if inefficiently). One of the problems is that web tools provide people with few opportunities to learn from one another’s experiences in ways that would allow them to improve their success. This dissertation presents the idea of social feedback. Social feedback is based on the theory of social learning, which describes how people learn from observing others. In social feedback, observational learning is enabled through the mechanism of interaction history – the traces of activity people create as they interact with the Web. Social feedback systems collect and display interaction history to allow information seekers to learn how to complete their tasks more successfully by observing how other people have behaved in similar situations. The dissertation outlines the design of two social-feedback systems, and describes two studies that demonstrate the real world applicability and feasibility of the idea. The first system supports global learning, by allowing people to learn new search skills and techniques that improve information seeking success in many different tasks. The second system supports local learning, in which people learn how to accomplish specific tasks more effectively and more efficiently. Two further studies are conducted to explore potential real-world challenges to the successful deployment of social feedback systems, such as the privacy concerns associated with the collection and sharing of interaction history. These studies show that social feedback systems can be deployed successfully for supporting real world information seeking tasks. Overall, this research shows that social feedback is a valuable new idea for the social use of information systems, an idea that allows people to learn from one another’s experiences and improve their success in many common real-world tasks

Interactive visualisation tools for supporting taxonomists working practice.

The necessity for scientists and others to use consistent terminology has recently beenregarded as fundamental to advancing scientific research, particularly where data fromdisparate sources must be shared, compared or integrated. One area where there aresignificant difficulties with the quality of collected data is the field of taxonomicdescription. Taxonomic description lies at the heart of the classification of organismsand communication of ideas of biodiversity. As part of their working practice,taxonomists need to gather descriptive data about a number of specimens on aconsistent basis for individual projects. Collecting semantically well-defined structureddata could improve the clarity and comparability of such data. No tools howevercurrently exist to allow taxonomists to do so within their working practice.Ontologies are increasingly used to describe and define complex domain data. As a partof related research an ontology of descriptive terminology for controlling the storageand use of flowering plant description data was developed.This work has applied and extended model-based user interface developmentenvironments to utilise such an ontology for the automatic generation of appropriatedata entry interfaces that support semantically well defined and structured descriptivedata. The approach taken maps the ontology to a system domain model, which ataxonomist can then specialise using their domain expertise, for their data entry needs asrequired for individual projects. Based on this specialised domain knowledge, thesystem automatically generates appropriate data entry interfaces that capture dataconsistent with the original ontology. Compared with traditional model-based userautomatic interface development environments, this approach also has the potential toreduce the labour requirements for the expert developer.The approach has also been successfully tested to generate data entry interfaces basedon an XML schema for the exchange of biodiversity datasets

Interactive visualisation tools for supporting taxonomists working practice.

The necessity for scientists and others to use consistent terminology has recently beenregarded as fundamental to advancing scientific research, particularly where data fromdisparate sources must be shared, compared or integrated. One area where there aresignificant difficulties with the quality of collected data is the field of taxonomicdescription. Taxonomic description lies at the heart of the classification of organismsand communication of ideas of biodiversity. As part of their working practice,taxonomists need to gather descriptive data about a number of specimens on aconsistent basis for individual projects. Collecting semantically well-defined structureddata could improve the clarity and comparability of such data. No tools howevercurrently exist to allow taxonomists to do so within their working practice.Ontologies are increasingly used to describe and define complex domain data. As a partof related research an ontology of descriptive terminology for controlling the storageand use of flowering plant description data was developed.This work has applied and extended model-based user interface developmentenvironments to utilise such an ontology for the automatic generation of appropriatedata entry interfaces that support semantically well defined and structured descriptivedata. The approach taken maps the ontology to a system domain model, which ataxonomist can then specialise using their domain expertise, for their data entry needs asrequired for individual projects. Based on this specialised domain knowledge, thesystem automatically generates appropriate data entry interfaces that capture dataconsistent with the original ontology. Compared with traditional model-based userautomatic interface development environments, this approach also has the potential toreduce the labour requirements for the expert developer.The approach has also been successfully tested to generate data entry interfaces basedon an XML schema for the exchange of biodiversity datasets