This paper presents a novel framework for Visual Exploratory Search of Relationship Graphs on Smartphones (VESRGS) that is composed of three major components: inference and representation of semantic relationship graphs on the Web via meta-search, visual exploratory search of relationship graphs through both querying and browsing strategies, and human-computer interactions via the multi-touch i...

As archives are opening up and publishing their content online, the general public can now directly access archive collections. To support access, archives typically provide the public with their internal search tools that were originally intended for professional archivists. We conduct a small-scale user study where non-professionals perform exploratory search tasks with a search tool original...

Although a large number of datasets gathered as Linked Open Data (LOD) is better for data sharing and re-using, the datasets themselves become more difficult to understand. Since each dataset has its own data structure, we need to understand datasets individually. In addition, since the entities in datasets are interconnected, we need to understand the interconnections between datasets. In othe...

During exploratory search sessions, users often experience difficulty finding and re-finding high-quality results that are pertinent to their search goals. Various studies have found that search sessions are unsuccessful, end prematurely, and/or lead to user frustration. This paper describes a framework and prototypical system called Conceptual Mile Markers (CMM) that reduces the “time to value...

Traditional text-based image retrieval does not fully support queries on semantic relationships between two entities. To better help the exploratory search on image collections, this paper presents a system for automatically extracting the relations between entities by analyzing the sentence dependency on the descriptions of the images. Our results demonstrate that using the extracted relations...

We present our ongoing work on an interactive exploratory information retrieval system designed to explain to the user the reasons for its predictions and help the user to build a mental model to predict how the system will behave. Users indicate which documents interest them and reinforcement learning is used to model the user by allowing the system to trade o↵ between exploration and exploita...

We present a mobile touchable application for guided exploration of web content and online topic graph extraction that has been successfully implemented on a tablet, i.e. an Apple iPad, and on a mobile device/phone, i.e. Apple iPhone or iPod. Starting from a user’s search query a set of web snippets is collected by a standard search engine in a first step. After that the snippets are collected ...

Evaluation of interactive search systems has always been time-consuming and complex, which probably explains the relative low level of interest from IR researchers for this type of evaluation in the past. Yet the limitations of batch-style system evaluations cannot be ignored anymore. We present some case studies of evaluations in interactive settings. Several of these evaluations offer valuabl...

We present a mobile touchable application for online topic graph extraction and exploration of web content. The system has been implemented for operation on a tablet computer, i.e. an Apple iPad, and on a mobile device, i.e. Apple iPhone or iPod touch. The topics are extracted from web snippets which are determined by a standard search engine. We consider the extraction of topics as a specific ...

In this extended abstract, we discuss user controllability and system predictability in the context of exploratory search. When a user is directing a search engine by interactively refining a probabilistic user model using uncertain relevance feedback, usability problems regarding controllability and predictability may arise. By interpreting user’s actions as setting a goal for an optimization ...