Most information retrieval technologies are designed to facilitate information discovery. However, much knowledge work involves finding and re-using previously seen information. We describe the design and evaluation of a system, called Stuff I’ve Seen (SIS), that facilitates information re-use. This is accomplished in two ways. First, the system provides a unified index of information that a person has seen, whether it was seen as email, web page, document, appointment, etc. Second, because the information has been seen before, rich contextual cues can be used in the search interface. The system has been used internally by more than 230 employees. We report on both qualitative and quantitative aspects of system use. Initial findings show that time and people are important retrieval cues. Users find information more easily using SIS, and use other search tools less frequently after installation.

We have witnessed great interest and a wealth of promise in content-based image retrieval as an emerging technology. While the last decade laid foundation to such promise, it also paved the way for a large number of new techniques and systems, got many new people involved, and triggered stronger association of weakly related fields. In this article, we survey almost 300 key theoretical and empirical contributions in the current decade related to image retrieval and automatic image annotation, and in the process discuss the spawning of related subfields. We also discuss significant challenges involved in the adaptation of existing image retrieval techniques to build systems that can be useful in the real world. In retrospect of what has been achieved so far, we also conjecture what the future may hold for image retrieval research.

This paper presents a modified diary study that investigated how people performed personally motivated searches in their email, in their files, and on the Web. Although earlier studies of directed search focused on keyword search, most of the search behavior we observed did not involve keyword search. Instead of jumping directly to their

Abstract. The Semantic Web Initiative envisions a Web wherein information is offered free of presentation, allowing more effective exchange and mixing across web sites and across web pages. But without substantial Semantic Web content, few tools will be written to consume it; without many such tools, there is little appeal to publish Semantic Web content. To break this chicken-and-egg problem, thus enabling more flexible informa-tion access, we have created a web browser extension called Piggy Bankthat lets users make use of Semantic Web content within Web content as users browse the Web. Wherever Semantic Web content is not available, Piggy Bank can invoke screenscrapers to re-structure information within web pages into Semantic Web format. Through the use of Semantic Web technologies, Piggy Bank provides direct, immediate benefits to users in their use of the existing Web. Thus, the ex-istence of even just a few Semantic Web-enabled sites or a few scrapers already benefits users. Piggy Bank thereby offers an easy, incremental upgrade path to users without requiring a wholesale adoption of the Semantic Web’s vision. To further improve this Semantic Web experience, we have created Semantic Bank, a web server application that lets Piggy Bank users share the Semantic Web information they have collected, enabling collaborative efforts to build so-phisticated Semantic Web information repositories through simple, everyday’s use of Piggy Bank. 1

Facet browsing has become popular as a user friendly interface to data repositories. We extend facet browsing of Semantic Web data in five ways. First, users are able to select and navigate through facets of resources of any type and to make selections based on properties of other, semantically related, types. We address a disadvantage of hierarchy-based navigation by adding a keyword search interface with semantic autocompletion. The interface of our browser, /facet, allows the inclusion of facet-specific display options that go beyond the hierarchical navigation that characterizes current facet browsing. Finally, the browser works on any RDFS dataset without any additional configuration.

Data on the Semantic Web is semi-structured and does not follow one fixed schema. Faceted browsing [23] is a natural technique for navigating such data, partitioning the information space into orthogonal conceptual dimensions. Current faceted interfaces are manually constructed and have limited query expressiveness. We develop an expressive faceted interface for semi-structured data and formally show the improvement over existing interfaces. Secondly, we develop metrics for automatic ranking of facet quality, bypassing the need for manual construction of the interface. We develop a prototype for faceted navigation of arbitrary RDF data. Experimental evaluation shows improved usability over current interfaces.

The combined efforts of human volunteers have recently extracted numerous facts from Wikipedia, storing them as machine-harvestable object-attribute-value triples in Wikipedia infoboxes. Machine learning systems, such as Kylin, use these infoboxes as training data, accurately extracting even more semantic knowledge from natural language text. But in order to realize the full power of this information, it must be situated in a cleanly-structured ontology. This paper introduces KOG, an autonomous system for refining Wikipedia’s infobox-class ontology towards this end. We cast the problem of ontology refinement as a machine learning problem and solve it using both SVMs and a more powerful joint-inference approach expressed in Markov Logic Networks. We present experiments demonstrating the superiority of the joint-inference approach and evaluating other aspects of our system. Using these techniques, we build a rich ontology, integrating Wikipedia’s infobox-class schemata with WordNet. We demonstrate how the resulting ontology may be used to enhance Wikipedia with improved query processing and other features.

Web search services are among the most heavily used applications on the World Wide Web. Perhaps because search is used in such a huge variety of tasks and contexts, the user interface must strike a careful balance to meet all user needs. We describe a study that used eye tracking methodologies to explore the effects of changes in the presentation of search results. We found that adding information to the contextual snippet significantly improved performance for informational tasks but degraded performance for navigational tasks. We discuss possible reasons for this difference and the design implications for better presentation of search results. Author Keywords Web search, eye tracking, contextual snippets, user studies.

We posit that a semistructured data model o#ers the right balance of rich structure and flexible (or lack of) schema allowing naive end users to record information in whatever form makes it easy for them to manage. We describe our Haystack system, which exposes the richness and flexibility of the data model while o#ering the user natural, traditional interfaces that shield them from the specifics of schemas, tuples, and database queries. We outline research challenges that remain to be addressed.

Given time and location information about digital photographs we can automatically generate an abundance of related contextual metadata, using off-the-shelf and Web-based data sources. Among these are the local daylight status and weather conditions at the time and place a photo was taken. This metadata has the potential of serving as memory cues and filters when browsing photo collections, especially as these collections grow into the tens of thousands and span dozens of years. We describe the contextual metadata that we automatically assemble for a photograph, given time and location, as well as a browser interface that utilizes that metadata. We then present the results of a user study and a survey that together expose which categories of contextual metadata are most useful for recalling and finding photographs. We identify among still unavailable metadata categories those that are most promising to develop next.