Brief origins of the organization
of information
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Large amounts of information became
difficult to store and retrieve.
Although the classes used vary wildly
across cultures, grouping based on the
class level is nearly universal.
Organizational structures provide the
context in which humans transform
information into knowledge.
It’s not just handy, it’s essential.
Humans classify “with a pronouncedly
mental scalpel that helps us carve discrete
mental slices out of reality” because
“reality is not made up of insular chunks
unambiguously separated from one
another by sharp divides, but, rather, of
vague, blurred-edge essences that often
spill over into one another.”
-Eviatar Zerubavel (1991)from The fine line:
Making distinctions in everyday life
“Cognitive scientists have noticed that much of our
mental commerce with an environment deals with
classes of things rather than with unique events
and objects.”
-Mark Stefik (1995) from Introduction to
knowledge systems
For example, the people seen below could probably all
be placed in both the class “Cognitive Scientists” and
the class “Nerds”. Can you think of other possible
classes? Possible relationships? Clinical vs.
academic cognitive scientists? Beards and nerds?
Why consider classification and
taxonomy together?
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Both are methods for grouping objects or
ideas sharing useful, although
sometimes superficial, similarities
Both group to make retrieval easier
Both are very basic and pervasive
elements of information architecture
It is often difficult to tell them apart
It is often unnecessary to tell them apart
Why tell them apart then?
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To become knowledgeable about the
different limitations and possibilities in
their interaction
Differential demand on and payoff for
users
It is important to understand the specific
qualities by which each can achieve
organizational objectives
Specific qualities presented as
keywords and key-dichotomies
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Organization
Retrieval
Controlled
vocabulary/thesauri
Ambiguous vs. Exact
Searching vs.
Browsing
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Content-based vs.
User-based
Descriptive vs.
Navigational
Precision vs. Recall
Structures vs.
Applications
Concise vs. Broad
Classifications, Taxonomies,
and Ontologies - Classifications
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Relationships expressed are not essential, but
are based on arbitrary, external attributes
(color, genre, format, geography, subject,
alphabetical order)
Created broadly from the top-down, based on
conceptual frameworks
Created by subject experts
Usually don’t change significantly after their
creation
Generally applicable to specific domains
Classifications, Taxonomies,
and Ontologies - Taxonomies
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Relationships expressed are usually essential, based on
internal properties of the related pieces of information
Created concisely from the bottom-up from actual
content
Created by multidisciplinary teams
Are process-oriented, and so are updated frequently
Oftentimes can be used and reused in different
situations and environments
Relationships commonly represented hierarchically
Can be include many classifications connected together
Example of internal properties
of taxonomic relationship
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All zippers are clothes fasteners
Not all clothes fasteners are
zippers
Because of the essential nature
of their relationship, zippers is
a sub-class of clothes fasteners,
and clothes fasteners is a
superordinate class of zippers
Taxonomic Hieracrhy
Clothes Fasteners
Belts
Zippers
Buttons
Classifications, Taxonomies,
and Ontologies - Ontologies
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Like taxonomies, relationships
expressed are also essential
Scope is more overarching due to
inclusion of supplemental information
• Descriptions and definitions of concepts and
their corresponding relationships
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Can include many sub-class taxonomies
connected together
Classifications, Taxonomies,
and Ontologies
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Classifications guide users to a body of
information
Taxonomies guide users through a body
of information
Ontologies guide users in becoming
proficient in the retrieval of and
understanding of a particular body of
information
Classification
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To classify something is to identify it as a
member of a known class
On the Web, information architects
organize classification schemes into
either exact or ambiguous schemes
Classification problems begin with data
and identify predetermined classes as
solutions
Exact classification schemes
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Items are categorized mutually
exclusively
Useful to users who know exactly what
they are looking for
By definition, are easier to create and
maintain than ambiguous schemes
Alphabetical, chronological, geographical
Alphabetical schemes
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Directories and lists
User must have a good idea of what they
are searching for and be able to spell it
On the Web, usually utilized deeper in
the scheme inside of sub-sites
Chronological schemes
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Have an intuitive advantage for users
because they are organized in the same
linear scheme in which humans
experience the dimension of time
Yearbooks, historical sites, and news
headline sites
Ebay offers results organized by a few
different types of chronologies
Geographical schemes
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Have intuitive appeal to rich spatial faculties
and needs of users in their experience of
reality
Geographical divisions coincide with governing
bodies which restrict and encourage behaviors
through law and language
Requires knowledge of geographical divisions
and map reading on the part of the user
Ambiguous classification
schemes
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Items are categorized into intellectually
meaningful groups
Useful to users who don’t know quite what
information they are searching for
Facilitate iterative, serendipitous learning
Audience-based, Subject-based, Task-based
Each should be based on scheme specific
research and development processes (e.g.
user and task analyses)
Audience-based classification
schemes
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Makes sense if the informational domain
caters to clearly delineated audiences
Homepage becomes a filter that leads to
sub-sites organized some other scheme
Suggests customization/personalization
Recommendations are sometimes
powerful, sometimes failures
IA research for audience-based
classification schemes
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Map services and applications to their
appropriate group
Discern what types of technology-use are
associated with specific populations
Find points of overlap between audience
categories
User research sessions, usage statistics,
search log analysis, focus groups, critical
incident reports
Subject-based classification
schemes
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Most immediately recognized are the library
classification schemes (DDC, LC)
When used in IA, they generally work best
when hybridized with other types of schemes
Are challenging to implement because different
words, symbols, and idioms mean different
things to different people
Breadth of subjects included should be
decided early on because these parameters
will affect much of the rest of the IA and
content work for the Web site
IA research for subject-based
classification schemes
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Solicit development team to write down
each content item that will be part of site
IA’s perform card sorting exercise to
establish initial subject categories
Take it to the user
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Continually refine
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• Further card sorting
• Survey with questions about navigation
Task-based classification
schemes
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Useful for action and transaction related
Web sites
Rarely drive a Web site on their own, but
are typically embedded deeper as part of
a hybrid scheme
Desire of businesses to remove labor
costs will likely increase their ubiquity
IA research for task-based
classification schemes
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The field of usability arose from the need
to research the success and value of
tools and their applications
Traditional usability tests are a good fit
Analyses of video-taped sessions,
navigation logs, heuristic reviews,
surveys, critical incident reports
Taxonomies
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Information architects have two major types to
utilize: descriptive and navigational
They contrast well and each excels for different
organizational and user needs
Central ideas include creating hierarchies,
controlled vocabularies, and variant/preferred
term and synonym relationships
Build on classifications by supporting
applications and many different types of
content, including images, email, search
engines, process funnels, and site registration
Descriptive taxonomies
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Operate outside of a user’s immediate
awareness
Supplement information retrieval during keyword
searching
IA’s create controlled vocabularies and synonym
rings which they use to maintain consistency
across applications and departments
By analyzing emerging content and search logs,
IA’s maintain currency and map alternative
terminology used by searchers back to the
preferred form
Controlled vocabularies in
descriptive taxonomies
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Done by attaching tags to content with
metadata derived from controlled
vocabulary usage logs
The resulting thesaurus with related and
variant terms makes a descriptive
taxonomy more robust
Using the controlled vocabulary
to increase recall or
precision
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A user’s search can be expanded to
increase recall by mapping the search
term to its variants
Or a user’s search can be narrowed to
increase precision by mapping a user’s
term to the preferred term in the
controlled vocabulary
More about descriptive
taxonomies
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Created from the bottom-up
Are called descriptive because they are
derived directly from the content that is being
used
Data management vocabularies allow workers
in disparate domains to report information
using the same terminology
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Makes it easier for management to mine information
from this data in the future
Navigational taxonomies
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Have a lot of overlap with exact and
ambiguous classification schemes
In contrast to descriptive taxonomies,
navigational taxonomies command the
user’s conscious awareness
Allow the user to guide the seeking
process themselves by browsing instead
of searching
Navigational taxonomies cont’d
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Created from the top-down based on
mental models of users
Hierarchical structures visually imply
sequences of events and relationships
• These relationships provide context similar to
words in a sentence
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Works best when users are unsure of
what they are seeking
Breadth vs. Depth
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Breadth is how many categories are contained
in each level
Depth refers to how many levels are contained
in the hierarchy
Too broad and shallow causes user too many
choices and not enough content
Too narrow and deep causes user to click
more than they will stand for
It is best to err on the side of broad and
shallow to allow for add-ons and to avoid
restructuring the home page
Summary
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Distinction is more pronounced in theory
than in practice because both are
essentially controlled vocabularies
structured by logical relationships
Generally, as one moves from
classifications to taxonomies to
ontologies, the structures, relationships,
and supplemental descriptions become
more complex
Summary cont’d
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Since humans seem to perform all three
of these innately, it matters less what
they are called than how their elements
can be tailored to specific scenarios to
improve retrieval of information,
consistency of communication, and
creation of knowledge
References
Adams, K. (2000). Immersed in structure: the meaning and function of
taxonomies.Internetworking, 3.2. Retrieved October 25, 2004 from:
http://www.internettg.org/newsletter/aug00/article_structure.html
Brown, J., & Duguid, P. (2002). The social life of information. Boston: Harvard
Business School Press.
Conway, S., & Sligar, C. (2002). Unlocking knowledge assets. Redmond,
Washington: Microsoft Press.
Edols, L. (2001).Taxonomies are what? FreePint, 97, 9-11. Retrieved October 25,
2004 from the FreePint Web site: http://www.freepint.com/issues/041001.pdf
Goodall, G. (2003). Business taxonomies and bibliographic objective: Facetation.
Retrieved October 25, 2004 from:
http://www.deregulo.com/facetation/pdfs/businessTaxomies_goodall.pdf
Nielsen, J. (2001). Designing web usability. Indianapolis, IA: New Riders
Publishing.
Rosenfeld, L., & Morville, P. (2002). Information architecture for the World Wide
Web. Cambridge ; Sebastopol, CA: O'Reilly.
References cont’d
Shank, P. (2004). Get organized or get lost. OnlineLearningMag. Retrieved October
25, 2004 from:
http://www.onlinelearningmag.com/onlinelearning/magazine/article_display.jsp?
vnu_content_id=1108349
Stefik, M. (1995). Introduction to knowledge systems. San Francisco: Morgan
Kaufmann.
Svenonius, E. (2001). The intellectual foundation of information organization.
Cambridge, MA: The MIT Press.
Taylor, Arlene G. (1999). The organization of information. Englewood, CO: Libraries
Unlimited.
van Duyne, D. K., Landay, J. A., & Hong, J. I. (2003). The design of sites.
Cambridge: Addison-Wesley.
van Rees, R. (2003). Clarity in the usage of the terms ontology, taxonomy and
classification. CIB73 2003 Conference Paper. Retrieved October 25, 2004 from
http://vanrees.org/research/papers/cib2003.pdf
Zerubavel, E. (1991). The fine line: Making distinctions in everyday life. New York:
Free Press.