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Exploratory Search in DifferentInformation Architecturesby Tingting Jiang and Sherry Koshman

Information Architecture

Tingting Jiang is a Ph.D. student at the School of Information Sciences, University ofPittsburgh. Her research interests include exploratory search, information architectureand social tagging. She can be reached at tij4<at>pitt.edu.Sherry Koshman is an assistant professor at the School of Information Sciences,University of Pittsburgh. Her research interests include investigating user interaction withinformation visualization tools and online information structures. She teaches graduatecourses in information architecture, information visualization and information technology.She can be contacted at skoshman<at>sis.pitt.edu.

T he ASIS&T 2008 IA Summit, Experiencing Information,emphasized users who want to know, do or share something. A userexperiences information by creating, organizing, browsing and

searching for information. These actions contribute to the notion ofexploratory search that can be described as an information process in whichthe importance of a search system’s query-document matching power isdiminished in favor of the user assuming a more assertive role in makingdecisions about the search results and the next steps toward fulfilling theirinformation needs [1]. A straightforward and common way to distinguish anexploratory search system is to examine the presentation of search results.Typically some browsing facilities to supplement or replace the popular list-based result pages are introduced and they feature grouping as a primarymechanism for search result display.

In a less dense space with information organized into perceptiblegroups, users are able to more easily detect and concentrate on the mostrelevant information for each search session in order to determine thedirection of the next search step. In studying this concept further, weinvestigate the information architectures of current exploratory search

systems and identify four primary organizing strategies to assist users insearching and browsing information. They are hierarchical classification,faceted categorization, clustering and social tagging.

Hierarchical ClassificationHierarchical classification refers to a system of fixed classes organized

in a hierarchical enumerative structure, offering a structural representationof a vocabulary that is typically applied to formal and stable resources. Thisstructure is a familiar one found in many formal classification systems. Anonline example, CitiViz (http://feathers.dlib.vt.edu/CitiViz/index.html), avisual search project for computing literature, is a case in point [2]. Eachsearch result does not appear individually; it appears under theclassification(s) that it belongs to and together with other related results.Only the classifications that contain search results will be included for eachquery. It is effective in reducing the considerable volume of results to asmall number of groups that are rational and familiar to us, greatlylessening the burden of browsing. Specifically in CitiViz, users are allowedto manipulate the classifications by deleting unwanted headings.

As we all know, it requires a high investment in time, money andexpertise to create non-overlapping classes and arrange them hierarchically,thus not many domains can afford a well-built comprehensive hierarchicalclassification system. Even if it is affordable, not every field can be properlyreduced to a few classes with clear edges. Once established, the system willbecome the authority and it may not be responsive to changes. As a result,for certain information domains, such as user-contributed content, suchclassification hardly applies at all since old vocabularies wither and newones grow at such a fast pace.

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Faceted CategorizationFaceted categorization, for the purpose of exploratory search, represents

different attributes of an information collection by offering small categoricalhierarchies that correspond to concepts contained within the repository [3].The subject-oriented ACM classification discussed above cannot satisfyusers who want to explore the literature by author, year or method. Bytaking multiple facets into consideration, faceted categorization bettermanifests the fact that different people understand the world differently.

Faceted categories have become a standard information architecture thatis readily implemented across many sites, especially online shopping sites.For instance, in finding a PC laptop on eBay, users are enabled to constraintheir search by brand, processor speed, memory, hard drive capacity, screensize and condition. The interface is flexible because information can beretrieved along any number of facets in any order. In addition, facetedhierarchical categories need much less manual work to develop, comparedto one large classification system.

ClusteringClustering has become a popular technique of grouping search results.

For each query, the clustering algorithm will generate a unique taxonomy ofthe search results based on their content. Result items are assigned toclusters in the taxonomy right after they are retrieved and before presentedto the searcher [4]. The clustering approach appeals to many informationproviders in comparison to hierarchical classification and facetedcategories. Most importantly, everything is done automatically. Secondly, adynamic taxonomy with clusters generated in real time eliminates thecomplexity and cost of building and maintaining fixed.

Depending on the algorithm, the taxonomy may have only one level ormultiple levels. And as the algorithms evolve, there could be multipletaxonomies created for each query from different aspects. WebClust inFigure 1a is one of the few search engines adopting the simplest form, thatis, one taxonomy and one level. Carrot2 is a little more complex, with threefacets (topics/sources/sites) introduced and a one-level taxonomy for eachone (Figure 1b). Including the same facets, Clusty in Figure 1c offers

hierarchies that can be expanded to an unpredictable number of levels.Unfortunately, the disadvantages of clustering such as the mislabeling,misplacing or overlapping of clusters within the taxonomy are sometimesevident. The robustness of a cluster-based post-retrieval taxonomy isdefeated by the previously discussed pre-determined architectures.

Social TaggingTagging refers to individual users assigning meaning to online objects,

including bookmarks, texts, images and videos, in the form of tags orkeywords or metadata. Users can be resource providers or consumers orboth. The social nature of tagging emphasizes that tags are not onlypersonal labels used for categorizing individual collections, but also serveas public clues for others to reach personal collections. Folksonomies resultfrom this bottom-up social tagging process and are distributed classificationsystems that can be exactly described as a flat name space without rigidhierarchies or exclusive categories [5]. Today, almost all the social taggingsystems are relying on tag clouds to represent a folksonomy. Tag clouds

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(a) WebClust(http://www.webclust.com/)

(b) Carrot2(http://demo.carrot2.org/demo-stable/main)

(c) Clusty(http://clusty.com/)

FIGURE 1. Taxonomies of search results in WebClust, Carrot2 and Clusty with ascending complexityFIGURE 1. Taxonomies of search results in WebClust, Carrot2 and Clusty with ascending complexity

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usually display tags inalphabetical order withlittle attention to termrelationships (Figure 2).

It demonstrates avery loose structure, withonly font size implyingthe use frequency of thealphabetically ordered tags.

The advantages anddisadvantages of folksonomies are usually compared with the othertaxonomies mentioned above. The self-driven tagging activities produce twobenefits: the inexpensiveness in terms of creation cost and the responsivenessto changes [6]. Furthermore, a democratic folksonomy gives everyone theopportunity to express his or her personal viewpoints through tags andwelcomes distinct or even contrary ones to co-exist. The other side of thecoin, however, is that liberal and distributed tagging by everyone will leadto the “vocabulary problem” as well as the “basic level” problem [7] [8].

ConclusionExploratory search systems, compared to current mainstream web

search engines, reflect more carefully on the presentation of search results,a critical factor that determines search effectiveness. Realizing theinsufficiency of the linear list of ranked results for sophisticated exploratorytasks, they are devoted to satisfying users’ information needs by enablinggrouping of results. The four major grouping strategies at present, whichconstitute this discussion, are hierarchical classification, facetedcategorization, clustering and social tagging. They give birth to fourdifferent information architectures and each of them bears advantages anddisadvantages. In our plan for future research, an in-depth comparison ofthese architectures is an important step forward that will not only measurethe effectiveness of each one in its applicable information domain, but alsoseek possible solutions to offset their weaknesses. �

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FIGURE 2. A tag cloud from Flickr (www.flickr.com/)Resources Mentioned in the Article

[1] White, R. W., Muresan, G., & Marchionini, G. (2006, August). Evaluating exploratorysearch systems. In R.W.White, G. Muresan, and G. Marchionini (Eds.) Proceedings ofthe ACM SIGIR 2006 Workshop on Evaluating Exploratory Search Systems (EESS2006) Retrieved July 1, 2008, from http://research.microsoft.com/~ryenw/eess/eess2006_proceedings.pdf. Article also retrieved July 1, 2008, fromwww.scils.rutgers.edu/~muresan/Publications/wshsigirWhite2006.pdf

[2] Fox, E. A., Neves, F. D., Yu, X., Shen, R., Kim, S., & Fan,W. (2006). Exploring thecomputing literature with visualization and stepping stones & pathways.Communications of the ACM, 49(4), 53-58.

[3] Hearst, M. A. (2006). Clustering versus faceted categories for informationexploration. Communications of the ACM, 49(4), 59-61.

[4] Vivisimo, Inc. (2006). Tagging vs. clustering in enterprise search. Retrieved June 10,2008, from http://vivisimo.com/html/download-tagging.

[5] Hammond, T., Hannay, T., Lund, B., & Scott, J. (2005). Social bookmarking tools (I).D-Lib Magazine, 11(4).

[6] Chi, E. H., and Mytkowicz, T. (2006). Understanding navigability of social taggingsystems. Retrieved June 10, 2008, from www.viktoria.se/altchi/submissions/submission_edchi_0.pdf.

[7] Furnas, G.W., Landauer, T.K., Gomez, L.M., & Dumais, S.T. (1987). The vocabularyproblem in human-system communication. Communications of the ACM, 30(11),964-971.

[8] Tanaka, J., & Taylor, M. (1991). Object categories and expertise: Is the basic level inthe eye of the beholder? Cognitive Psychology, 23(3), 457-482.