ImageSieve: Exploratory search of museum archives with named entity-based faceted browsing

Over the last few years, faceted search emerged as an attractive alternative to the traditional "text box" search and has become one of the standard ways of interaction on many e-commerce sites. However, these applications of faceted search are limited to domains where the objects of interests have already been classified along several independent dimensions, such as price, year, or brand. While automatic approaches to generate faceted search interfaces were proposed, it is not yet clear to what extent the automatically-produced interfaces will be useful to real users, and whether their quality can match or surpass their manually-produced predecessors. The goal of this paper is to introduce an exploratory search interface called ImageSieve, which shares many features with traditional faceted browsing, but can function without the use of traditional faceted metadata. ImageSieve uses automatically extracted and classified named entities, which play important roles in many domains (such as news collections, image archives, etc.). We describe one specific application of ImageSieve for image search. Here, named entities extracted from the descriptions of the retrieved images are used to organize a faceted browsing interface, which then helps users to make sense of and further explore the retrieved images. The results of a user study of ImageSieve demonstrate that a faceted search system based on named entities can help users explore large collections and find relevant information more effectively

Exploratory Analysis of Highly Heterogeneous Document Collections

We present an effective multifaceted system for exploratory analysis of highly heterogeneous document collections. Our system is based on intelligently tagging individual documents in a purely automated fashion and exploiting these tags in a powerful faceted browsing framework. Tagging strategies employed include both unsupervised and supervised approaches based on machine learning and natural language processing. As one of our key tagging strategies, we introduce the KERA algorithm (Keyword Extraction for Reports and Articles). KERA extracts topic-representative terms from individual documents in a purely unsupervised fashion and is revealed to be significantly more effective than state-of-the-art methods. Finally, we evaluate our system in its ability to help users locate documents pertaining to military critical technologies buried deep in a large heterogeneous sea of information.Comment: 9 pages; KDD 2013: 19th ACM SIGKDD Conference on Knowledge Discovery and Data Minin

Improving Primo Usability and Teachability with Help from the Users

In the aftermath of a consortium migration to a shared cloud-based resource management and discovery system, a small college library implemented a web usability test to uncover the kinds of difficulties students had with the new interface. Lessons learned from this study led to targeted changes, which simplified aspects of searching, but also enhanced the librarians’ ability to teach more effectively. The authors discuss the testing methods, results, and teaching opportunities, both realized and potential, which arose from implementing changes

An image retrieval system based on explicit and implicit feedback on a tablet computer

Our research aims at developing a image retrieval system which uses relevance feedback to build a hybrid search /recommendation system for images according to users’ inter ests. An image retrieval application running on a tablet computer gathers explicit feedback through the touchscreen but also uses multiple sensing technologies to gather implicit feedback such as emotion and action. A recommendation mechanism driven by collaborative filtering is implemented to verify our interaction design

From Keyword Search to Exploration: How Result Visualization Aids Discovery on the Web

A key to the Web's success is the power of search. The elegant way in which search results are returned is usually remarkably effective. However, for exploratory search in which users need to learn, discover, and understand novel or complex topics, there is substantial room for improvement. Human computer interaction researchers and web browser designers have developed novel strategies to improve Web search by enabling users to conveniently visualize, manipulate, and organize their Web search results. This monograph offers fresh ways to think about search-related cognitive processes and describes innovative design approaches to browsers and related tools. For instance, while key word search presents users with results for specific information (e.g., what is the capitol of Peru), other methods may let users see and explore the contexts of their requests for information (related or previous work, conflicting information), or the properties that associate groups of information assets (group legal decisions by lead attorney). We also consider the both traditional and novel ways in which these strategies have been evaluated. From our review of cognitive processes, browser design, and evaluations, we reflect on the future opportunities and new paradigms for exploring and interacting with Web search results

Telescope Bibliographies: an Essential Component of Archival Data Management and Operations

Assessing the impact of astronomical facilities rests upon an evaluation of the scientific discoveries which their data have enabled. Telescope bibliographies, which link data products with the literature, provide a way to use bibliometrics as an impact measure for the underlying data. In this paper we argue that the creation and maintenance of telescope bibliographies should be considered an integral part of an observatory's operations. We review the existing tools, services, and workflows which support these curation activities, giving an estimate of the effort and expertise required to maintain an archive-based telescope bibliography.Comment: 10 pages, 3 figures, to appear in SPIE Astronomical Telescopes and Instrumentation, SPIE Conference Series 844