Supporting Creativity with Search Tools
Bill
Kules,
Introduction
Is searching
creative? Searching and information seeking are part of the creative process. An
architect looking for “seed” ideas for a new project may search an architecture
database. Novelists, journalists and artists may similarly search the web for
new ideas. A historian will explore archival material for a research project.
Even graduate students may employ search as they refine and narrow their
research topic. Information seeking models of the writing process acknowledge
the creative elements, identifying specific stages for topic exploration and
formation (Kuhlthau, 1992). Advertising art directors search for images
as part of their creative process (Garber & Grunes, 1992). Engineers and software developers search
for creative solutions to technical problems, too.
Developers of search
tools have traditionally focused on searches in which the objective is clearly
defined, such as known-item or fact searches. Typical web search engines and
databases are now very effective at satisfying these searches with a simple
ranked list of results. In the context of a creative task, however, the
information need may be only partially specified or ambiguous, and the searcher
may not be familiar with terminology in the domain or collection being
searched.
Four kinds of
information have been proposed as aiding the creative process (Bawden, 1986): Interdisciplinary information, peripheral
information, speculative information, and exceptions and inconsistencies. Creative
searches can embody (at least) the following four characteristics. Within the
characteristics we propose techniques that may support creative search by
helping searchers encounter these types of information:
- Generative goals – Rather than finding a
specific document or fact, the goal of the search can be to learn about a
topic area, develop a question, generate ideas, identify unusual items
(“outliers”) or even look for conflict or inconsistency. Queries may be
ambiguous or partially defined.
- Meaningful and stable categories
- Variable categories, including clustering/clustered
overviews
- Cross-context – Searches may extend
across domains or collections. Indeed the searcher may deliberately search
in unfamiliar domains to gain alternative perspectives on a topic or
discover previously unnoticed connections.
- Literature visualization
- Literature linking
- Exploratory and iterative – Searchers
interactively explore the search results by browsing, filtering or other
techniques. They issue multiple, successive queries as their information
need evolves, possibly across multiple sessions. They exhibit
“berry-picking” or “information foraging” behavior as they collect useful
bits of information, follow promising threads and identify new information
sources.
- History mechanisms
- Workspaces
- Enhanced bookmarks
·
Serendipity,
non-linearity – Serendipitious findings can provide valuable insight for the
creative searcher
Because of the variety of creative tasks, this list is certainly incomplete, and no single task is likely to embody all characteristics.
Review of Search
Interfaces
This section considers
the first two bullets and illustrates features that we propose will support
creative search. For a more comprehensive review of search visualization interfaces,
see Hearst (1999).
1.1.Ranked Search Results
Typical web search
engines are optimized to support known item and fact search by ranking
documents according to query relevance, link analysis, popularity or
combinations of several metrics. Google does provide a link to “similar pages,”
which allows users to quickly find documents that satisfy a similarity metric.
Figure 1. The Google interface shows the top search results for
the query “urban sprawl site:gov” as a ranked list (www.google.com).
1.2.Organizing by Meaningful and Stable Categories
Interactive
overviews of categorized web search results using meaningful and stable
classifications can support user exploration, understanding of large result
sets, and discovery. The categories can be drawn from large thesauri,
glossaries, or ontologies. Alternatively, they can be based on simple
categorization schemes such as document type, country codes or ranges of
document size. They support the “search and browse” process that is typical of
exploratory searches by providing a consistent organizing structure for keyword
searches. When used to filter ranked search results (Figure 2), users found pages of interest deeper within the
results and noticed more unexpectedly missing results – that is categories with
no associated results – compared to a control interface (Kules & Shneiderman, submitted).
Figure 2. This overview+detail interface shows the top 200 results for the query “urban sprawl site:gov.” They have been categorized into a two-level government hierarchy, which is used to present a categorized overview on the left. The Interior Department, which has 20 results, has been expanded and the National Park Service has been selected. The effect on the right side is to show just the three results from the Park Service (Kules & Shneiderman, submitted).
Figure 3. Flamenco uses
multiple sets of hierarchical categories (“hierarchical faceted metadata”) to
organize guide browsing and searching. In this figure, the user has filtered
architectural images to show images that represent both building materials and circulation
elements (Yee, Swearingen, Li, & Hearst, 2003).
Figure 4. Exalead
produces categorized overviews using topical categories as well as categories
based on geography and document type. (www.exalead.com)
Figure 5.
Figure 6. Cat-a-Cone supports search and browse in MeSH, a
very large hierarchy of terms used to classify medical research reports. The
hierarchy is displayed as a cone tree, which users can interactively navigate
through. They can also issue a query, whereupon the tree is pruned to show only
categories with matching documents (Hearst & Karadi, 1997).
Figure 7. GRIDL uses categorical variables to organize search
results on a two-dimensional grid. Here the user has organized computer science
documents along according to the ACM classification (vertical) and year of
publication (horizontal). At each grid point clusters of color-coded dots
represent the documents and show a third attribute, the document type. If there
are more than 49 documents in a grid point a bar chart summarizes the by
document type. Users see the entire result set and can then click on labels to
move down a level in the hierarchy.
Multiple sets of
categories can be used to support conjunctive filters while retaining the
benefit of stable organization, and helping users avoid feeling “lost” in the
information space (English, 2002).
Categorical metadata
can be represented using graphical displays. Search results can be displayed on
an abstract map (a two-dimensional space) based on a hierarchy of categories (Figure 5). Three-dimensional displays have been used to
visualize very large hierarchies
A matrix can be used
to organize results along two categorical or numeric dimensions (Kunz, 2003; Kunz & Botsch, 2002; Shneiderman,
Feldman, Rose, & Grau, 2000).
1.3.Organizing by Variable Categories
Variable categories,
produced by clustering search results into dynamically generated categories,
can be used in place of stable categories to produce similar displays of search
results. Clustering has been found helpful for search tasks, although searchers
sometimes fail to understand the clusters or their labels. Variable categories
can be used in overview+detail interfaces (Figures 8 and 9) or visual maps
(Figures 10 and 11).
Figure 8. The metasearch engine Clusty (and its predecessor
Vivisimo) uses a form of automated document clustering that generates
hierarchies of concisely labeled clusters. In this example, the top 208 results
have been clustered, and the cluster labels have been used to generate an
overview with 10 categories initially visible. Users can show more categories
or filter and navigate the results using the expandable outliner
(www.clusty.com).
Figure 9. Findex clusters documents into a flat set of
categories. Here the results from the query “jaguar” have been clustered into
15 categories. The “atari jaguar” category has been selected (Käki, 2005).
Figure 10. Grokker generates hierarchical clusters and displays
those clusters using concentric circles. Users can drill down into clusters to
explore the results.
Figure 11. Kartoo clusters results to produce a topical
overview on the left, and displays the top 12 documents as a visual map of
semantic relationships.
Figure 12. The ET Map is a multi-layer self-organizing map
Figure 13. Themescape uses a topographc map metaphor to plot
keywords extracted from a corpus.
Figure 14. The Harmony Landscape visualizes an information
space on a receding plane.
1.4.Literature visualization
Literature
visualization tools provide overviews of a knowledge domain or field of
research by visualizing bibliographic attributes such as citations between
articles or common themes. Co-citation networks visualize citations between
papers by significant authors in a field. They can graphically illustrate major
topics and sub-fields (Chen, 1999). These maps may help searchers bridge
multiple fields or identify trends.
Figure 15. This co-citation map is derived from a collection of
papers on hypertext. It shows three snapshots the hypertext field.
Relationships between major topics and authors can be seen. (Chen, 1999)
1.5.Literature linking
Literature linking
is a specialized form of creative information seeking that attempts to discover
new connections between two literatures. It has been used to identify hidden
connections in the medical literature between migraines and magnesium by citation
analysis and manual review of terms common in both literatures (Swanson, 1988). The LitLinker system (Pratt & Yetisgen-Yildiz, 2003) is a recent example of this technique. It
provides a user interface that allows searchers to select starting and target
terms within a set of literatures, and interactively explore potential links.
Figure 16. LitLinker (http://litlinker.ischool.washington.edu)
Conclusion
The interfaces described above may help information seekers by doing more than simply displaying ranked lists of search results – by exposing them to information that will help the creative process. The visual presentation of information has a powerful impact on what is perceived, particularly with the information visualization techniques illustrated here, and could prove to be useful tools for the creative information seeker.
References
Bawden, D. (1986). Information systems and the stimulation of
creativity. Journal of Information
Science, 12, 203-216.
Chen, C. (1999). Visualising semantic spaces
and author co-citation networks in digital libraries. Information Processing and Management, 35(3), 401-420.
English, J., Hearst, M., Sinha, R.,
Swearington, K., and Yee, P. (2002). Flexible
Search and Navigation using Faceted Metadata.Unpublished manuscript.
Garber, S. R., & Grunes, M. B. (1992).
The art of search: a study of art directors. Proceedings of the SIGCHI conference on Human factors in computing
systems, 157-163.
Hearst, M. (1999). User interfaces and
visualization. In R. Baeza-Yates & B. Ribeiro-Neto (Eds.), Modern Information Retrieval (pp.
257-323). Reading, MA: Addison-Wesley.
Hearst, M. A., & Karadi, C. (1997).
Cat-a-Cone: an interactive interface for specifying searches and viewing
retrieval results using a large category hierarchy. Proceedings of the 20th annual international ACM SIGIR conference on Research
and development in information retrieval, 246-255.
Käki, M. (2005). Findex: search result
categories help users when document ranking fails, Proceeding of the SIGCHI conference on Human factors in computing
systems. Portland, Oregon, USA: ACM Press.
Kuhlthau, C. (1992). Seeking meaning: a process approach to library and information services.
Norwood, New Jersey: Ablex Publishing.
Kules, B., & Shneiderman, B. (submitted).
Using meaningful and stable categories to support exploratory web search: Two formative
studies.
Kunz, C. (2003). SERGIO - An Interface for
context driven Knowledge Retrieval. Proceedings
of eChallenges, Bologna, Italy, 2003.
Kunz, C., & Botsch, V. (2002). Visual
Representation and Contextualization of Search Results – List and Matrix
Browser. Proceedings of Dublin Core´02.
Pratt, W., & Yetisgen-Yildiz, M. (2003).
LitLinker: capturing connections across the biomedical literature. Proceedings of the international conference
on Knowledge capture, 105-112.
Shneiderman, B., Feldman, D., Rose, A., &
Grau, X. F. (2000). Visualizing Digital
Library Search Results with Categorical and Hierarchial Axes. Paper
presented at the Proc. 5th ACM International Conference on Digital Libraries
(San Antonio, TX, June 2-7, 2000).
Swanson, D. R. (1988). Migraine and
magnesium: eleven neglected connections. Perspect.
Biol. Med., 31, 526-557.
Yee, K.-P., Swearingen, K., Li, K., &
Hearst, M. (2003). Faceted metadata for image search and browsing. Proceedings of the SIGCHI conference on
Human factors in computing systems, 401-408.