Presented at Documentation and Training East 2007 by Seth Earley -- Search is not just a plug in or a utility. While "just Googling" for information works on the web, there are numerous reasons why this is not always the best approach for intranets and individual web sites. This slide deck explores the role of information architecture and discusses 5 important strategies for improving search including tuned search, metadata and tagging, faceted search, term expansion and disambiguation, and results clustering.

1. Improving Findability: The Role of
Information Architecture in Effective
Search
DocTrain East – October 18th, 2007
Seth Earley
781-444-0287
Seth@earley.com
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2. Seth Earley, Founder, Earley & Associates, Inc.
16 person consulting firm working with enterprises to develop
knowledge and content management systems and taxonomy,
metadata and search strategies
Co-author of Practical Knowledge Management from IBM Press
14 years experience building taxonomies for content and
knowledge management systems, 20+ years experience in
technology
Founder of the Boston Knowledge Management Forum
Former adjunct professor at Northeastern University
Founder of Search Community of Practice :
http://tech.groups.yahoo.com/group/SearchCoP
Founder of Taxonomy Community of Practice:
http://finance.groups.yahoo.com/group/TaxoCoP
Host monthly conference calls of case studies on search and
taxonomy
Recently acquired taxonomy management tool company
(www.wordmap.com)
Precise access to information, enabled by consistent organisation
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3. Agenda
Search and the hype cycle, search as a utility
Basic premises
The challenge of search
Taxonomy, metadata & content management
5 taxonomy & search strategies you should know!
Faceted search
Tagging
Clustering
Tuned search
Disambiguation
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4. Search as Utility
“search as a utility has become deeply ingrained
into people's everyday lives.“ – Study by
Nielsen/Net Ratings
“search software, hardware, and support bundle
or search appliance has become very popular
since being introduced in early 2002quot; – Goebel
Group
These are misleading concepts. Search is used as a utility, but
contexts vary so widely that “plugging search in” does not always
produce satisfactory results.
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5. Search and the Hype Cycle
Different ‘flavors’ of Search are at various levels of maturity
2. At the Peak
2
Enterprise IM
4
Information Retrieval and
Search — Advanced
5
Smart Enterprise Suites
Wikis
Content Integration
1
Taxonomy
3
Corporate Blogging
1. On the Rise
Corporate Semantic Web
Desktop Portals
Content-Process Fusion
Desktop Search
5. Entering the
Personal Knowledge Networks
Plateau
Information Extraction
4. Climbing the Slope Virtual Workplace
3. Sliding Into the Trough Knowledge
Web Conferencing
Management
Public Semantic Web MMS
Automated Text Enterprise Content
Categorization Management
Expertise Location and Presence
Management
Folksonomies
E-Learning Suites Source: http://www.gartner.com
Shared Workspaces
Records Management
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6. Basic Premises
Premise 1 – All of search is about metadata
Need to understand the relationship of taxonomy and metadata
Premise 2 – The line between search and navigation is
blurring
Faceted search looks like navigation, guided navigation is
search
Premise 3 – Search needs to be designed as an application,
not an appliance
Design of any application requires attention to user context
Premise 4 – Search needs to be integrated into processes,
not added on
Relevant search is context specific, context depends on process
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7. Basic Premises
Premise 5 – We need to understand work processes, user
tasks and user context in order to make search effective
Users search for information in order to accomplish a goal
Premise 6 – Taxonomy, metadata and information
architecture are all aspects of search
These are all an attempt to surface information for users in the
context of their objectives
Premise 7 – Search algorithms, no matter how
sophisticated, intelligent and complex will never obviate the
need for some level of structured tagging
Premise 8 – Taxonomy strategy needs to be tightly linked to
search strategy (and to content strategy)
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8. Basic Premises
Premise 9 – Metadata is either implicit in content
or explicitly applied to content
Implicit metadata can take many forms – inherent
structure of a piece of content or even the source or
context of content
Premise 10 – Search is messy
Relevant results are in the eye of the beholder, language
is imprecise, meaning is vague
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9. “…search terms are short,
ambiguous and an approximation
of the searchers real information
need…”
Source: http://research.microsoft.com/~ryenw/papers/WhiteCONTEXT2002.pdf
Ryen W. White, Joemon M. Jose and Ian Ruthven
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10. What is the right balance?
Content can be created in structured or unstructured
contexts
It’s value can vary depending on audience, context or
process
Some content is extremely nuanced and requires more
precise access (according to audience or task, solution,
etc…)
Search can be based on inherent structure and content of a
document (implicit metadata) or on information applied to
that content (explicit metadata)
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11. Different tools are appropriate depending upon degree
of collaboration and creation versus structured access
More
Less
Structured
Structured
Knowledge Creation Knowledge Access/Reuse
Chaotic Processes Controlled Processes
Online Records Mgt
Wikki’s
Email Workflow
Collaborative
Learning Systems
systems
Workspaces
Instant
Blogs
Messages Instructor Doc Mgt
Content Mgt
Discussions
Led Systems
Courses
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12. Relative value
Lower Value Higher Value
(Easier to access)
(More difficult to access)
Unfiltered Reviewed/Vetted/Approved
Lower Cost Higher Cost
Formal Tagging/Organizing Processes
Best
External News Interim Example Benchmarks
Practices
deliverables deliverables
Message Discussion Success Approved
Content
text postings Stories Methods
Repositories
Structured tagging
Social tagging
(taxonomy)
(“folksonomy”)
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13. IA: The intersection of taxonomies,
metadata and content objects
Taxonomy: system for organizing and classifying
content
Metadata: information about our content,
housekeeping, as well as semantic and structural
information
Content Objects: groups of metadata that are
assembled into components that are then
assembled into pages or documents
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14. Goals of a taxonomy
Allow for knowledge discovery
Improve usability of applications as well as
learnability of applications
Reduce the cost of delivering services, developing
products and conducting operations
Improve operational efficiencies by allowing for
reuse of information rather than recreation
Improve search results and applicability (both
precision and recall)
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15. Precision versus recall
Precision versus recall
We have a repository, execute a search and retrieve a result set
Results
Relevant items
in a database
But – not every relevant document is retrieved and not all results are relevant
This is quantified as “recall” and “precision”
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16. The role of metadata
It is the “is –ness” of a piece of content
And the “about- ness” of a piece of content
This is a Product Description
It is about the Motorola Razr
Information Architecture is the organizing
Information Architecture is the organizing
principle behind metadata and how that
principle behind metadata and how that
information is surfaced to the user
information is surfaced to the user
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17. Content models
Content is structured with body information and a
wrapper that formats and tags that information
Also called a “content object model”*
Title
Simple content object model
Description
*Content model refers to overall framework
Content object model refers to a specific model for a set
of document types
I.e., an overall “Content Model” includes multiple
Content Object Models”
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18. FAQ
Product
“is – ness” Press release
Specification
Promotion
Title Doc_ID
Doc_Type
Author Date
Product_Name
“about – ness”
Features
Metadata for a
product page in a
Category
content management
system
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19. Content modeling – Policy example
Standard Header
Title Subject Doc_ID
Author Date
Policy content type Content_ID
Date
Content_ID
Date
Customer Service content type
Content_ID
Date
Claims processing content type
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20. © 2007 20
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21. © 2007 21
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22. Why the metadata tutorial?
One word: faceted search
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23. Faceted Search/Guided
Navigation
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24. Navigational taxonomy
Challenge is there is no “one way”
to navigate that is correct.
Taxonomy can be a hierarchical
grouping of navigational nodes Is this the “correct” way?
on a web site
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25. Navigational taxonomy
Motorola.com
Modems &
Mobile phones 2-way radios
gateways
Camera Bluetooth
phones phones
Bluetooth
accessories
Sunglasses Headsets
Or is this one “correct”? Or is this one?
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26. Motorola.com => United States => Government => Portable Radios
Motorola.com => Portable Radios => United States => Government
Motorola.com
Government Enterprise Consumers
Mobile Portable
computers radios
United
Canada United States
Kingdom
Motorola.com => Government => Portable Radios => United States
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27. Navigating with “facets”
Two way radios
“Facet” is a top level
Portable
category in the taxonomy
Fixed
Mobile
Motorcycle
Product type
Vertical market Target document:
Government P = Portable radio
Manufacturing G = United States
V = Government
Wholesale retail
Country
Vertical market
Canada
United Kingdom
United States
Just three nodes with 5
Geographic
terms each could have 3 to
region
the 5th power (243) possible
combinations
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28. Is it search? Or navigation?
Some people can identify with a very practical use of taxonomies: Online Shopping
Many of the parameters on diamond
Many of the parameters on diamond
Taxonomies allow selection of
Taxonomies allow selection of selection (color, cut, clarity and shape)
selection (color, cut, clarity and shape)
type of processor, amount of
type of processor, amount of pull from a “controlled vocabulary” that
pull from a “controlled vocabulary” that
ram, manufacturers, etc
ram, manufacturers, etc are part of the taxonomy
are part of the taxonomy
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29. Taxo term values
Facets
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30. © 2007 30
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31. Faceted search implies tagged
content with nice structured
metadata…
What if we don’t have a lot of existing metadata? Does
that mean hire bunch of people to enter it in?
Manual tagging is rarely practical with large amounts of
lower value content. Instead, we need to derive implicit
metadata from content
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32. Leveraging metadata
All search leverages metadata
Metadata is either implied/derived from content
or specifically applied to content
Apply taxonomy terms as metadata to a
document so that relevant and consistent search
results are returned when users enter query
terms
ie. Taxonomy drives content tagging. Search engine
leverages tags for more precise results
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33. All search leverages metadata…
…but not all metadata is explicit
Full text search derives metadata about
documents
Creates an index of terms that occur in a
document collection
Associates documents with those index entries
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34. All search leverages metadata…
Occurrence of certain words in a document and the
relative value of those occurrences, including:
Weighting
Relative positioning
Semantic relationships…
…becomes information about the document that is
cached in the index and served by the search
engine
Search algorithms vary in how metadata is
derived and exposed to users.
Relevance ranking, for example, is additional metadata for a result that is
Relevance ranking, for example, is additional metadata for a result that is
‘implied’ or derived based on incoming connections to a piece of content.
‘implied’ or derived based on incoming connections to a piece of content.
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35. Context as metadata
Metadata can be explicit or implicit
Implicit: implied though not directly expressed;
inherent in the nature of something, implied by
context
Explicit: precisely and clearly expressed or
readily observable; leaving nothing to implication
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36. Examples of implicit metadata:
‘Structure’ and format of content – a piece of content may
be ‘unstructured’ and not contain metadata, but it is well
organized.
Example : Newspaper story contains a headline, sub head,
and first paragraph with who, what, where, when, etc.
Clear editorial standards
Context of content – Where did the content come from? If
from a particular web site, file share, data source or intranet
location the domain of knowledge provides context.
How can we disambiguate the term “diamond”?
Sports site – baseball diamond
Commerce site – diamond ring
Sales context for ‘feature’ versus engineering context for
‘feature’
“Adapter” – power cord
“Adapter” – blue tooth headset
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37. Context as metadata
If we maintain context of a piece of information in
our search results, this is equivalent to having
additional metadata on that content
Search results
organized by repository
This is a form of
“federated” search – a
single search term fed
to multiple repositories
Example courtesy of Morrison and Foerster
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38. Structure as metadata
Some content has excellent implicit metadata
News story for example
Has a main topic
Usually a summary of important points at the beginning
Mentions people, places and things that can be ‘extracted’ as
entities
Complies with editorial standards, usually contains a narrow theme
Will get good results from auto categorization and entity
extraction
Some content has poor implicit metadata
Email for example
Usually contains lots of topics
Does not have a theme
Does not comply with editorial standards, can be rambling, poorly
written
Will not get good results from auto categorization and entity
extraction
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39. Who tags content?
Automated
Based on process
Rules derived depending on source
or use of content (for example:
Policyholder Communications)
Based on content
Learning algorithm or rules based
classifier
Full text search index
Extracted entities
By People
By primary client
Customer tags documents based on
content and purpose
Outsourced to service bureau
Service bureau tags content based
on rules and style guides
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40. Indexing
Full text index is a form of metadata
Search vendors differ in how algorithms derive and surface this
metadata
Having a structured taxonomy adds customer context to the
search index
Context Challenges
Derivation
Application
Surfacing to UI
When we use a taxonomy to access content we have turned it into
an index
Taxonomy is not content specific, has no relevance or significance
Taxonomy can be reused, an index cannot
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41. How are tags derived?/Where do they live?
=
License Agreement
License
Content Type =
Forward Index – Words per document
Organization = Inverted Index – Documents per word
ABC Company
ABC
DEF Company customers section 7
customer support secondary support
Topic = Support customer support team secondary support person
DEF SLA
ABC shall provide first level technical DEF software SLA failure
support to all Licensed Product end users
end users software
and/or Sublicensed Product
escrow agreement. source code
customers/users. DEF will provide second
escrow agent support level
level support. DEF shall provide to ABC a
exhibit c sublicensed product
primary and a secondary support person to
act as the primary interface with ABC’s first level technical support technical support
technical and customer support team. DEF licensed product
shall provide direct technical support to
release condition
ABC for all uses of the DEF Software.
Support level definitions and responsibilities
are set forth in Exhibit C. An “SLA Failure” What would extracted entities look like?
as defined in Exhibit C shall qualify as a
How do we know the difference between “licensed
Release Condition sufficient to authorize the
product end users”, “licensed product” and “end
Escrow Agent to release to Source Code to
ABC pursuant to Section 7 and the Escrow users”?
Agreement.
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42. Search index points to document
1
Forward Index – Words per document
Inverted Index – Documents per word
ABC – 1,2,3,4
Customers - 3
2 customer support – 3,4
customer support team - 1
DEF - 2
DEF software – 2
… etc
3
4
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43. Clustering algorithm groups similar documents
(Dynamic) Clusters are based on
what is important to my audience
and what the user is interested in at
that moment (search context)
These are about
software licensing Search for “SLA” returned
a total of 8 documents
licensed product – 5 items
software
source code
support level
sublicensed product
technical support – 3 items
These are about
customer support
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44. How can content be tagged?
Instead of tagging the document, an index is
001 created that points to the document
GUID =
License
Content Type =
Organization = ABC Company
DEF Company
ABC shall provide first level technical
Topic = Support
support to all Licensed Product end users
and/or Sublicensed Product
customers/users. DEF will provide second
level support. DEF shall provide to ABC a GUID Content type Organization Topic
primary and a secondary support person to
001 License ABC, DEF Support
act as the primary interface with ABC’s
technical and customer support team. DEF
002 SLA ABC Terms
shall provide direct technical support to
ABC for all uses of the DEF Software.
Support level definitions and responsibilities
are set forth in Exhibit C. An “SLA Failure”
How do we leverage an index in search
as defined in Exhibit C shall qualify as a
Release Condition sufficient to authorize the
and navigation?
Escrow Agent to release to Source Code to
ABC pursuant to Section 7 and the Escrow
Agreement.
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45. Navigation versus Classification
Sales Tools
Analyst Reports
Case Studies
Customer References
FAQ’s
Pricing & Licensing
White Papers
Presentations
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46. Navigation versus Classification
Sales Tools Best Practices in .NET Development
Analyst Reports
By Title
By Topic
By Product
Case Studies
By Customer Building Rich Internet Applications
By Product
By Solution
By Industry
By Region
Customer References
Data Translations Using XML and XSLT
FAQ’s
Pricing & Licensing
White Papers
Presentations
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47. Navigation versus Classification
.NET
Sales Tools
Analyst Reports
By Title
By Topic
By Product
Case Studies .Architecture
By Customer
By Product
By Solution
By Industry
By Region
Distributed Applications
Customer References
FAQ’s
Pricing & Licensing
White Papers
Presentations
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48. Navigation versus Classification
GUID Content type Customer Topic
Sales Tools 001 Analyst ABC, DEF Architecture
Analyst Reports Reports
By Title 002 Case Studies ABC .NET
By Topic
By Product This is what a search index would look like
that contains metadata
Case Studies
By Customer We need to marry the navigational index with
the search index
By Product
By Solution
By Industry
By Region Navigation is just another access structure – an entry in
the index – but is different from classification
GUID Content type Customer Topic Node
001 Analyst ABC, DEF Architecture Sales ToolsAnalyst
Reports Reports
002 Case Studies ABC .NET Sales ToolsCase
Studies
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49. Navigation leverages Classification
Topic
• .NET
• Architecture
Sales Tools • Collaboration
Analyst Reports • Compliance
• Distributed Applications
By Title • Industry Standards
• JAVA
By Topic • Messaging
By Product • …
Product
• Web Speed Workshop
Case Studies • 4GL Development System
• Translation Manager
By Customer Solution
• Roundtable • Business Continuity
By Product • … • Business Intelligence
By Solution • Business Trends
• Deployment
By Industry • Development
Industry
By Region • Integration
• Government
• …
• Financial Services
• Healthcare
Customer References • Manufacturing
• Real Estate
Region
FAQ’s • Retail
• North America
Pricing & Licensing • Telecommunications
• EMEA
• Transportation and Distribution
• Latin America
White Papers • …
• Asia Pac
Presentations • Worldwide …
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50. Tuned Search, or “Best Bets”
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51. Tuned Search
What is Tuned Search?
Search terms are defined in a taxonomy and
mapped back to specific locations of information
(ie. Specific web pages).
Eg. A user searching on a broad term like cell
phones would be first pointed to a landing page
(a “best bet”), or presented a box of hand-picked
links above regular search results.
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52. Best Bets Example – Best Buy
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53. Tuned Search “Best Bets”
The same search using just keyword matching
could a have retrieved a list of pages with the
words “phone” or “cell” e.g.
Home phones
Cordless phones
12 cell batteries
Etc.
Reading through pages of possible matches is
time consuming and frustrating
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54. Best Bets Example – SAP.com
Search on “CRM” or “Customer Relationship
Management”
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55. Tuned Search “Best Bets”
How Does a Taxonomy Help?
Using the taxonomy categories as landing pages
assures that users are strategically directed to the
content that is most important.
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56. Tuned search – “Best Bets”
When do I use it?
As a portal or websites grow, the number of
pages with matching keywords increases.
This increases the likelihood of a search query
returning high numbers of results.
Tuned search helps when keyword searching
brings back to many results, and you want to
map common searches to specific, commonly
viewed pages of information.
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57. Tuned Search – “Best Bets”
How is it implemented?
Create a small database of search terms and then
map these terms to landing pages or specific links
Common search terms may be extracted from search
logs
Search engine must be configured to display the
best bets link box or redirect to the landing page
Few search engines provide this capability out of the
box…
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58. Disambiguation
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59. Disambiguation of search results
What is Disambiguation?
If a user enters a broad term (like “mobile”) the
taxonomy can return terms that help the user
select a more precise terms
Includes multiple approaches:
Term expansion
Complex lookups
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60. Disambiguation methods
Show related search terms in the search results
page.
Show additional search terms as links, perhaps
with a prompt - quot;You might also be interested
in:quot;
Expand the query and show the expanded words
in the search box
Expand the query invisibly
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61. Disambiguation of search results
Mobile data terminals
mobile
Handheld computers
Network Infrastructure Presenting
Mobile switches
term in
Phones
multiple
Fixed mobile car phones
contexts
Mobile phones
Software applications
Mobile applications
Two way radios
Mobile radios
Intelligent video solutions
Mobile video enforcer
Mobile video sharing
MESH Solutions
Multi-radio mobile broadband
Mobile Computing
Mobile application
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62. From Associative Relationships
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63. “We should get Google”…
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64. Why you will not “just get Google”
Google leverages linkages on the web that are not
typically duplicated internally in the organization
Search engines cannot infer intent or know what is
important to you in the context of your work task
Information relevance is dependant on who you are and
your level of expertise as well as what you are trying to
accomplish
Not all content is equal - Google is fine for broad search
results or less precise information, may not work as well
if large numbers of documents with finer granularity of
differences
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65. Google’s search appliance is leveraging
taxonomy values
The new “one box” feature allows querying of structured
content via specific keywords
East Coast Sales
Contact: Wick
PO
Revenue by age
Weather
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66. © 2007 66
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67. © 2007 67
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68. Configuration process
See: http://code.google.com/enterprise/documentation/oneboxguide.html
“Define trigger”
“Choose provider”
“Format results”
What does this really mean?
Need to consider taxonomy, metadata and thesaurus
entries, for example a trigger may include equivalent
terms:
lax airport conditions
SFO airport delays
newark airport status
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69. We still have a context
problem
“Revenue” is an ambiguous
term
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70. Why doesn’t Google, just use Google?
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71. Why you will not “just get Google”
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72. Developing a Search Strategy
Search needs to be thought of as an application – not
an afterthought
It’s not possible to ‘bolt it on’ and expect decent
results
Organizations are beginning to recognize search as an
integral application
When developing a search strategy, one size does not
fit all
Enterprise search is different from Web search
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73. Developing a Search Strategy
Find combined set of functionality that will satisfy needs of
different groups within the organization.
This involves identifying common requirements that are good
candidates for standardized solutions.
Identify unique requirements of groups that could place a burden
on the standard search service and where it may be better to
develop a custom extension.
The most effective strategy is one that avoids redundancy and
unnecessary complexity that often happens when systems are
developed and / or integrated in an “ad-hoc” manner.
Identifying the “outliers” up front may be as important as
identifying common issues.
Having a global set of requirements enables prioritization based on
both value and cost.
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74. Community of Practice Calls
Taxonomy Group url:
http://finance.groups.yahoo.com/group/TaxoCoP
Upcoming call topics:
Taxonomies & the Semantic Web
Taxonomy Validation
Proving the ROI
Multi-lingual Taxonomies
Getting Management Buy-In
Taxonomy Tools & Software: Beyond Excel
Taxonomy Project Deliverables: What to Promise and When
Taxonomy CoP Wiki at http://taxocop.wikispaces.com/
Search Group url:
http://tech.groups.yahoo.com/group/SearchCoP
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75. Research Reports and White Papers
Go to http://www.earley.com/Articles.asp
Aligning Business Technology Goals
Deriving a Taxonomy: Assembling Terms for a Consistent Point-of-
View
Indexing & Taxonomies: Finding the Best Way to Organize Online
Content
Knowledge Mapping - A Fast Way to the Heart of the Organization
Making the Business Case for Enterprise Taxonomy
Managing Multiple Facets & Polyhierarchy
Measuring the Success of a Taxonomy Project: Tuning Content
Categories for Continuous Improvement
Retrospective Indexing: Strategies for Cataloging Legacy Content
Taxonomy Metadata & Search
Text Mining: Search's Silver Lining
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76. Questions?
Seth Earley
seth@earley.com
www.earley.com
781-444-0287
Send an email to Info@earley.com for a
free pass to one of our con calls.
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