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HCIR Tables of Contents: 07080910111213

Proceedings of the Workshop on Human-Computer Interaction and Information Retrieval

Fullname:Proceedings of the Workshop on Human-Computer Interaction and Information Retrieval
Location:MIT, Cambridge, Massachusetts
Dates:2007-Oct-23
Standard No:hcibib: HCIR07
Papers:28
Pages:55
Links:Conference Website and Proceedings | Papers | Posters | Full Proceedings
  1. Papers
  2. Posters/Demos

Papers

Partitioning the Web: Shaping Online Consumer Choice BIBA 5
  Jolie M. Martin; Michael I. Norton
Imagine you want to spend the weekend with your partner in some city at a nice hotel, with reasonable prices, located near the water. Because you have not been to this city before, you decide to visit one of the many online websites that aggregate information about different hotels, allowing you to view ratings for each hotel's various attributes, narrowing the options until you pick your eventual winner. This research explores the ways in which online vendors structure this search process by categorizing the attributes available for viewing, thereby impacting both the search process and, more importantly, consumers' ultimate purchases. For instance, if you happened to visit a website that displayed the ratings given by prior guests to hotel value, we suggest you might be likely to overweight this criteria and underweight your other key criteria -- proximity to the water. On the other hand, if the website showed ratings for location, you would be apt to give this criterion more weight, perhaps changing your decision from a cheap option far from the water to a more expensive option closer to the water. In both cases, though your underlying preference (a reasonably-priced hotel near the water) has not changed, the ways in which information is partitioned changes the search process and your stated preference: your ultimate selection of hotel.
Record Relationship Navigation: Implications for Information Access and Discovery BIBA 6
  Christina Anderson
Record relationship navigation extends the faceted navigation model by using relationships between records as a basis for information exploration, rather than creating an asymmetric relationship between records and the facets used to describe them. While record relationship navigation can significantly enhance an end user's discovery experience, it also raises a number of information access questions, including what function a search box should serve, the meaning behind summary analytics (such as record counts), and what exactly constitutes a record.
Faceted Browsing, Dynamic Interfaces, and Exploratory Search: Experiences and Challenges BIBA 7-9
  Robert Capra; Gary Marchionini
The Relation Browser (RB) is a graphical interface for exploring information spaces, developed by the Interaction Design Lab at the University of North Carolina at Chapel Hill for use in research on how to support users' needs to understand and explore information. In this abstract, we describe the Relation Browser, results of recent studies, and the design goals for the next-generation RB in current development. At the workshop, we will demonstrate the current RB and a prototype of our next-generation RB.
Idea Navigation: Structured Browsing for Unstructured Text BIBA 10-11
  Robin Stewart; Gregory Scott; Vladimir Zelevinsky
In this paper, we demonstrate how document search interfaces could be enhanced by extending faceted browsing to the subject -- verb -- object representation of ideas. Our user study demonstrated that such an interface is understandable to first-time users and useful for solving realistic search tasks that are poorly supported by existing systems.
GK: A post-search information retrieval system BIBA 12-13
  Joseph Barillari
This abstract introduces GK, a web-based software system for research support. GK is designed to help users store, organize, and navigate large quantities of text, currently targeting but by no means limited to biomedical research.
A Knowledge-Based Search Engine Powered by Wikipedia BIBA 14-15
  David Milne; Ian H. Witten; David M. Nichols
This paper describes Koru: a new search interface that offers effective domain independent knowledge-based information retrieval [1]. Koru exhibits an understanding of the topics involved in both queries and documents, allowing them to be matched more accurately. It helps users express queries more precisely and evolve them interactively. This understanding is mined from the vast investment of manual effort and judgment that is Wikipedia. This open, constantly evolving encyclopedia yields manually-defined yet inexpensive structures that can be specifically tailored to expose the topics, terminology and semantics of individual document collections. This paper describes a brief overview of Koru and the knowledge base it extracts. A more detailed description and evaluation of the system can be found elsewhere [2].
Visual Text Analysis by Lay Users: The Case of "Many Eyes" BIBA 16-17
  Fernanda B. Viégas; Martin Wattenberg
Many Eyes (http://www.many-eyes.com) is a public web site that provides "visualization as a service," allowing users to upload data sets, visualize them, and comment on each other's visualizations. Among the visualization techniques offered by the site are two aimed at unstructured text: a "tag cloud" view and a "word tree," a type of visual concordance view. Both techniques have seen heavy usage. Our talk will break into two parts. First, we will introduce and demonstrate the two text visualization techniques. Second, we will describe the patterns of usage we have observed, particularly around political speeches and testimony and artistic expression.
Personal Information Management, Personal Information Retrieval? BIBA 18-20
  Michael Bernstein; Max Van Kleek; David R. Karger; mc schraefel
Traditional information retrieval has focused on the task of finding information or documents in a largely unknown space such as the Web or a library collection. In this paper we propose that the space of Personal Information Management (PIM) holds a great number of problems and untapped potential for research at the intersection of HCI and IR. In this position paper we focus on the problem of information scraps, or unstructured notes and thoughts, as a particularly interesting space for future research in HCI and IR.
Collaborative Exploratory Search BIBA 21-22
  Jeremy Pickens; Gene Golovchinsky
INTRODUCTION Most modem information retrieval (search) systems are geared toward helping a user quickly and effectively find a particular item. That item may be a document, a geographic location, a factoid, etc. This approach is good when a single piece of information can fulfill the user's information need.
   In many situations, however, multiple items and/or richer overviews of the entire information space are necessary. In support of this, exploratory search systems have been developed. Exploratory search systems typically blend a variety of information seeking tools and tactics (e.g., querying, browsing, document clustering, etc.) to help the user better understand the range of available information. Such tactics are combined in opportunistic ways as users' understanding of their information needs evolves [Bates, l989].

Posters/Demos

Codifier: A Programmer-Centric Search User Interface BIBA 23-24
  Andrew Begel
Search tools have transformed knowledge discovery by exposing information from previously hidden repositories to the workers who need it. Search engines like Google and Live.com provide search capabilities via a simple one-line text query box, and present results in a paged HTML list. When the repository being searched contains structured information with extractable metadata (e.g. program source code), it can be advantageous to index the metadata and use it to enable queries that are more task-centric and suitable for an domain-specific audience.
   Codifier is a programmer-centric search user interface that enables software developers to ask domain-specific questions related to programming languages and software.
Authority Facets: Judging Trust in Unfamiliar Content BIBA 25
  Peter Bell
How do people judge trust in content that has no provenance, like the stamp of an editorial process or audit trail? Most digital content lacks the authority of traditional publication models, and in content created through social collaboration, like the Wikipedia, it even lacks a clear single author. Nevertheless, informal content is widely consumed, albeit with healthy skepticism.
Mapping the Design Space of Faceted Search Interfaces BIBA 26-27
  Bill Kules
Faceted search, guided search, and categorized overviews are becoming accepted techniques to support complex information seeking tasks like exploratory search. There are a growing number of applications that use these techniques for library catalogs, web search, shopping, image collections, and other domains (Antelman, Lynema, & Pace, 2006; Hearst et al., 2002; Tunkelang, 2006; Yee, Swearingen, Li, & Hearst, 2003). Design guidelines for the application of these techniques are starting to emerge (Hearst, 2006; Kules & Shneiderman, to appear), but there is no systematic description of the design space for faceted search interfaces. An understanding of the design space will aid designers by alerting them to design options and decisions they should address. It will aid researchers by suggesting a framework for guidelines as well as additional areas of study. In particular, it may help understand the actions, tactics and strategems (Bates, 1990) supported by faceted search interfaces.
   The objective of this paper is to begin identifying and structuring a set of dimensions of the design space for categorized overviews of search results. This paper proposes a set of dimensions for the design space of faceted search interfaces and two structures for meaningfully organizing them. These dimensions and the organizing structures emerged from analysis of recent literature and applications from several domains.
Images as Supportive Elements for Search BIBA 28
  Giridhar Kumaran; Xiaobing Xue
The dominant paradigm of search today is heavily biased towards textual interfaces. Users enter textual queries, and navigate to potentially relevant content guided by short textual snippets offering summaries of retrieved information. This interaction paradigm is not only quite successful in practice but also provides an opportunity for improved techniques that are potentially even easier and effective. Our work focuses on that part of the interactive retrieval process where users are offered textual cues to guide them towards relevant content. By using images in lieu of text, we believe we can provide a user experience that is not only more effective, but also more efficient.
Searching Conversational Speech BIBA 29-30
  Mark Maybury
This paper summarizes two MITRE efforts to address the speech search challenge. We first describe Audio Hot Spotting (AHS) and then Cross Language Automated Speech Recognition (CLASR).
Natural Language Access to Information for Mobile Users BIBA 31-32
  Alexander Ran; Raimondas Lencevicius
Mobile devices store a rich set of structured information. The phone book application contains names, phone numbers, addresses and affiliations of personal contacts. The calendar application contains entries for meetings with participants, meeting location and time. Logs of dialed received calls are stored on the device as well as sent and received messages and emails.
AnalogySpace and ConceptNet BIBA 33
  Rob Speer; Catherine Havasi
We feel that information retrieval would benefit from our work in several ways. First, interfaces used in IR could benefit from the "sanity checking" features that adding common sense to a system provides. In the past, this has been used in speech recognition, predictive text entry, and other UI applications. Secondly, we would like to explore a representation similar to AnalogySpace, or even built on it, for other types of complex data such as those found in IR. We feel that AnalogySpace and principal component analysis shows great potential in reasoning which can extend to other areas.
Normalized Clarity and Guided Query Interpretation BIBA 34
  Daniel Tunkelang
For the past three decades, most research in information retrieval has assumed a ranked retrieval model, in which a query returns a ranking of corpus documents by their estimated relevance to this query. This model maps to the familiar user interface of most commercial and academic search engines.
   Despite its popularity, the ranked retrieval model suffers because it does not provide a clear split between relevant and irrelevant documents. This weakness makes it problematic to obtain even basic analysis of the query results, such as the number of relevant documents, let alone a more complicated one, such as the result quality.
Less Searching, More Finding: Improving Human Search Productivity BIBA 35-36
  William Woods
Finding information and organizing information so that it can be found are two key aspects of any company's knowledge management and knowledge delivery strategy. This talk will describe what I have learned from years of thinking about these problems and from a project I led at Sun Microsystems Laboratories that addressed these problems by combining the respective strengths of humans and computers in a knowledge-based system to help people find information. It explored a new search technology aimed at addressing problems that hinder human search effectiveness, and it developed techniques that provide a user with the necessary information to quickly decide whether a document has the information being sought. Unlike many previous attempts to improve search effectiveness, this system demonstrated a substantial improvement in human search productivity.
Mediating between User Query and User Model with Adaptive Relevance-Based Visualization BIBA 37-38
  Jae-wook Ahn; Peter Brusilovsky
Personalized information retrieval systems [1] seek to adapt search results to long term interests of an individual users represented in a user profile (also known as user model). One of the problems of these systems is how to "fuse" query-based and profile-based document rankings in search result presentation. The traditional solution to this problem, which is applied in several adaptive search systems, is to select a fixed mediation point α between 0 and 1 and to produce a personalized rank by fusing query- and profile-based rankings with coefficients α and (1-α). By manipulating α, the system designers can give more priority to documents similar to the query or documents similar to the profile. This paper presents a more flexible approach to "fusing" query- and profile-based rankings. The idea of this approach is to allow the analysts to dynamically decide whether they are interested in documents which are closer to the query or documents which are closer to the user profile -- with the ability to navigate on a continuum between the query to the user profile and back again.
Characterization of Diagrams and Retrieval Strategies for Them BIBA 39
  Robert Futrelle
There are a few hundred million diagrams available on the web, by rough estimate. They cover every imaginable topic. But quality retrieval of the "diagram you want" is close to impossible, because virtually all current methods rely entirely on the accompanying or referring text to characterize diagram content. Our lab has worked on a variety of aspects of diagrams and their internal content for a number of years, with one of the major goals being how to build IR systems for them. We have published diagram-related papers on machine learning for classification, constraint-grammar-based parsing, ambiguity, summarization, text-diagram interrelations, ontologies, and vectorization of diagram images. Much of our work has been focused on the diagrams that typically appear in papers from the biomedical research literature. This talk will range over the portions of our research most relevant to IR, arguing that many of the topics we have studied need to be kept in mind in building future systems for diagram analysis, representation, interaction, and retrieval.
Human Computation for HCIR Evaluation BIBAK 40-42
  Shiry Ginosar
A novel method for the evaluation of Interactive IR systems is presented. It is based on Human Computation, the engagement of people in helping computers solve hard problems. The Phetch image-describing game is proposed as a paradigmatic example for the novel method. Research challenges for the new approach are outlined.
Keywords: Interactive IR and HCIR evaluation, Web-based games
Jigsaw: A Visual Index on Large Document Collections BIBA 43-44
  Carsten Görg; John Stasko
Investigative analysts who work with collections of text documents connect embedded threads of evidence in order to formulate hypotheses about plans and activities of potential interest. As the number of documents and the corresponding number of concepts and entities within the documents grow larger, sense-making processes become more and more difficult for the analysts. We have developed a visual analytic system called Jigsaw that represents documents and their entities visually in order to help analysts examine reports more efficiently and develop theories about potential actions more quickly.
Reasoning and Learning in Mixed-Initiative Tasks BIBA 45
  Yifen Huang
Information management becomes more and more challenging with information communicated electronically or retrieved from the web. A human user is often overwhelmed by the amount of information that is easily accessible to them. The approach of Human-Computer Interaction (HCI) designs interfaces to leverage off a user's efforts. On the other hand, Artificial Intelligence (AI) develops more autonomous techniques in information retrieval, data mining, and learning. Although both approaches aim toward the same goal, they have proceeded in the past with too little interaction.
   In a simplified notion, a computer has two capabilities: (1) an inference function that takes inputs, e.g., a set of documents, and produces outputs, e.g., a rank list of these documents, and (2) representation of inference results. The first capability is AI related and the second capability is HCI related. I want to propose a new mixed-initiative framework that involves both approaches and hopefully boosts the quality of the solution jointly. The idea is as the following: in addition to a computer, a user can also give feedback on what representation we show to the user and the inference function can be re-trained based on user feedback. This forms a directed loop from the AI approach to the HCI approach to the user and back to the AI approach.
Resonance: Penalty-Free Deep Look-Ahead With Dynamic Summarization of Result Sets BIBA 46-47
  Blade Kotelly
Providing the ability for someone to be able to see what's around the corner is a hallmark of good HCIR. A great example of this is when there are various refinements that a user could make to refine a set, and next to each refinement is a count which tells the user how many results would be left in the set if they choose that particular refinement.
Visual Concept Explorer BIBA 48-49
  Xia Lin
Visual Concept Explorer (VCE) is a visualization system developed to explore potentials of visual mapping and information retrieval. Implemented as a client-server application, VCE contains a Java-based server that interacts with a very large ontology, the National Library of Medicine's UMLS Knowledge Source (UMLS, 2007) and a live search engine, the free PUBMED search engine available on the Web. Its visual interface is implemented in FLASH with various mapping and interactive functions. Figure 1 is a sample interface screen for searching the keyword "cognition." VCE is available for testing at: http://cluster.cis.drexel.edu/vce/
Navigating Document Networks BIBA 50-51
  Mark Smucker
While much research effort has been expended on innovative user interfaces for information retrieval (IR), deployed IR user interfaces have adopted few innovations. Rather than design another novel user interface tool that users never adopt, we decided that our first step would be to better understand the nature of an adopted tool. In that vein, we are in the process of studying the potential and the actual performance of find-similar, which is a widely adopted tool that allows a user to request documents similar to a given document. Find-similar is a compelling IR interface tool for the very reason that users appear to have adopted it and that it has the potential to provide to users the power long known to be available via relevance feedback. Our hope is that by better understanding find-similar, we'll be able to take that understanding and apply it to other user interface tools that will both be powerful and be adopted by users.
Integrating the "Deep Web" With the "Shallow Web" BIBA 52-53
  Michael Stonebreaker
Public web integration services such as Google and Yahoo provide access to the "shallow web", i.e. those sites that are reachable by a text-oriented crawler. Although this service is very useful, it misses large portion of the information available on the web. Specifically, it misses the "deep web", which is data available behind form-oriented user interfaces. Examples of deep web sites include all airline sites, 411.com, weather.com, and most retail sites. These all require one to issue a query through a form-oriented interface to an underlying data base. Obviously, current crawlers are incapable of accessing the deep web. In this paper, we propose a methodology for integrating the deep web with the shallow web.
Multimodal Question Answering for Mobile Devices BIBA 54-55
  Tom Yeh
In recent years, community-based question answering (QA) services, such as Yahoo Answers! and Naver, have enjoyed growing popularity. These services operate web sites to invite anyone to post open-ended questions for free. The questions can be on a variety of topics ranging from car buying tips to dating advice. They are viewed by millions of users in an online community, some of whom may happen to possess the right expertise to give satisfactory answers to them. Users frequent these sites to seek answers about topics they know little of; at the same time, they often volunteer answers about other topics they happen to be familiar with. It is in the spirit of such reciprocity that people are willing to contribute their expertise and knowledge for the benefits of the whole, without seeking any form of monetary compensation.