Improve your ability to find critical information1
Improving Findability: The Role of Information Architecture in Effective Search
DocTrain East – October 18th, 2007
Seth Earley
781-444-0287
© 2007 2Improve your ability to find critical information
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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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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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 2002" – 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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Search and the Hype Cycle
Different ‘flavors’ of Search are at various levels of maturity
1. On the Rise� Corporate Semantic Web� Desktop Portals� Content-Process Fusion� Desktop Search � Personal Knowledge Networks� Information Extraction
3. Sliding Into the Trough� Public Semantic Web � Automated Text
Categorization � Expertise Location and
Management � Folksonomies � E-Learning Suites � Shared Workspaces � Records Management
4. Climbing the Slope � Web Conferencing � MMS � Enterprise Content
Management � Presence
2. At the Peak� Enterprise IM� Information Retrieval and
Search — Advanced� Smart Enterprise Suites� Wikis� Content Integration� Taxonomy� Corporate Blogging
5. Entering the Plateau
� Virtual Workplace � Knowledge
Management
1
2
3
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Source: http://www.gartner.com
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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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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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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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“…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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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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More
Structured
Instant
Messages
Wikki’s
Blogs
Discussions
Collaborative
Workspaces
Online
Learning
Instructor
Led
Courses
Content Mgt
Workflow
systems
Doc Mgt
Systems
Records Mgt
Systems
Knowledge Creation Knowledge Access/Reuse
Chaotic Processes Controlled Processes
Different tools are appropriate depending upon degree of collaboration and creation versus structured access
Less
Structured
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Lower Cost Higher Cost
Message
text
External News Example
deliverables
Discussion
postings
Interim
deliverables
Content
Repositories
Success
Stories
Benchmarks
Approved
Methods
Best
Practices
Unfiltered Reviewed/Vetted/Approved
Lower Value Higher Value
Relative value
Formal Tagging/Organizing Processes
(More difficult to access) (Easier to access)
Social tagging (“folksonomy”)
Structured tagging (taxonomy)
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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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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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Relevant items
in a database
We have a repository, execute a search and retrieve a result set
Results
But – not every relevant document is retrieved and not all results are relevant
This is quantified as “recall” and “precision”
Precision versus recallPrecision versus recall
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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 principle behind metadata and how that
information is surfaced to the user
Information Architecture is the organizing principle behind metadata and how that
information is surfaced to the user
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Content models
� Content is structured with body information and a wrapper that formats and tags that information
� Also called a “content object model”*
Title
Description
Simple content object model
*Content model refers to overall frameworkContent 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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Metadata for a product page in a content management
system
Title
DateAuthor
Features
Product_Name
Category
Doc_IDDoc_Type
“is – ness”
“about – ness”
FAQ
Product
Press release
Specification
Promotion
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Content modeling – Policy example
Title
DateAuthor
Subject Doc_ID
Content_ID
Date
Content_ID
Date
Content_ID
Date
Standard Header
Policy content type
Customer Service content type
Claims processing content type
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Improve your ability to find critical information
Why the metadata tutorial?
One word: faceted search
Improve your ability to find critical information
Faceted Search/Guided Navigation
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Navigational taxonomy
Taxonomy can be a hierarchical
grouping of navigational nodes
on a web site
Challenge is there is no “one way”
to navigate that is correct.
Is this the “correct” way?
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Navigational taxonomy
Or is this one “correct”? Or is this one?
Motorola.com
Mobile phonesModems &
gateways2-way radios
Camera
phones
Bluetooth
phones
Bluetooth
accessories
Sunglasses Headsets
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Motorola.com => United States => Government => Portable Radios
Motorola.com => Portable Radios => United States => Government
Motorola.com => Government => Portable Radios => United States
Motorola.com
Government Enterprise Consumers
Mobile
computers
Portable
radios
United
KingdomCanada United States
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Navigating with “facets”
� Two way radios� Portable
� Fixed
� Mobile
� Motorcycle
� Vertical market� Government
� Manufacturing
� Wholesale retail
� Country� Canada
� United Kingdom
� United States
Vertical market
Target document: P = Portable radioG = United States
V = Government
Product type
Geographic
region
“Facet” is a top level category in the taxonomy
Just three nodes with 5
terms each could have 3 to
the 5th power (243) possible
combinations
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Is it search? Or navigation?
Many of the parameters on diamond
selection (color, cut, clarity and shape)
pull from a “controlled vocabulary” that
are part of the taxonomy
Many of the parameters on diamond
selection (color, cut, clarity and shape)
pull from a “controlled vocabulary” that
are part of the taxonomy
Some people can identify with a very practical use of taxonomies: Online Shopping
Taxonomies allow selection of
type of processor, amount of
ram, manufacturers, etc
Taxonomies allow selection of
type of processor, amount of
ram, manufacturers, etc
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Facets Taxo term values
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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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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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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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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 ‘implied’ or derived based on incoming connections to a piece of content.
Relevance ranking, for example, is additional metadata for a result that is ‘implied’ or derived based on incoming connections to a piece of content.
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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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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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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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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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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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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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How are tags derived?/Where do they live?
DEF Company
Support
ABC Company
Agreement
ABC shall provide first level technicalsupport to all Licensed Product end usersand/or Sublicensed Productcustomers/users. DEF will provide secondlevel support. DEF shall provide to ABC a
primary and a secondary support person to
act as the primary interface with ABC’stechnical and customer support team. DEFshall 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”
as defined in Exhibit C shall qualify as a Release Condition sufficient to authorize the
Escrow Agent to release to Source Code to
ABC pursuant to Section 7 and the EscrowAgreement.
License
License
Content Type =
=
Organization =
ABCcustomerscustomer supportcustomer support teamDEFDEF softwareend usersescrow agreement.escrow agentexhibit cfirst level technical supportlicensed productrelease condition
section 7secondary supportsecondary support personSLASLA failuresoftwaresource codesupport levelsublicensed producttechnical support
Topic =
What would extracted entities look like?
How do we know the difference between “licensed
product end users”, “licensed product” and “end
users”?
Forward Index – Words per documentInverted Index – Documents per word
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Search index points to document
ABC – 1,2,3,4Customers - 3customer support – 3,4customer support team - 1DEF - 2DEF software – 2… etc
1
2
3
4
Forward Index – Words per documentInverted Index – Documents per word
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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
These are about
customer support
Search for “SLA” returned a total of 8 documents
licensed product – 5 itemssoftwaresource codesupport levelsublicensed producttechnical support – 3 items
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How can content be tagged?
DEF Company
Support
ABC Company
ABC shall provide first level technical support to all Licensed Product end usersand/or Sublicensed Productcustomers/users. DEF will provide second level support. DEF shall provide to ABC a
primary and a secondary support person to
act as the primary interface with ABC’s
technical and customer support team. DEF 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”
as defined in Exhibit C shall qualify as a Release Condition sufficient to authorize the
Escrow Agent to release to Source Code to
ABC pursuant to Section 7 and the Escrow Agreement.
LicenseContent Type =
Organization =
Topic =
TermsABCSLA002
SupportABC, DEFLicense001
TopicOrganizationContent typeGUID
001GUID =
How do we leverage an index in search
and navigation?
Instead of tagging the document, an index is created that points to the document
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Sales ToolsAnalyst Reports
Case Studies
Customer References
FAQ’s
Pricing & Licensing
White Papers
Presentations
Navigation versus Classification
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Sales ToolsAnalyst Reports
By Title
By Topic
By Product
Case Studies
By Customer
By Product
By Solution
By Industry
By Region
Customer References
FAQ’s
Pricing & Licensing
White Papers
Presentations
Best Practices in .NET Development
Building Rich Internet Applications
Data Translations Using XML and XSLT
Navigation versus Classification
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Sales ToolsAnalyst Reports
By Title
By Topic
By Product
Case Studies
By Customer
By Product
By Solution
By Industry
By Region
Customer References
FAQ’s
Pricing & Licensing
White Papers
Presentations
.NET
.Architecture
Distributed Applications
Navigation versus Classification
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Sales ToolsAnalyst Reports
By Title
By Topic
By Product
Case Studies
By Customer
By Product
By Solution
By Industry
By Region
.NETABCCase Studies002
ArchitectureABC, DEFAnalyst Reports
001
TopicCustomerContent typeGUID
Navigation is just another access structure – an entry in
the index – but is different from classification
This is what a search index would look like
that contains metadata
We need to marry the navigational index with
the search index
.NET
Architecture
Topic
Sales Tools\Case Studies
ABCCase Studies002
Sales Tools\Analyst Reports
ABC, DEFAnalyst Reports
001
NodeCustomerContent typeGUID
Navigation versus Classification
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Navigation leverages Classification
Sales ToolsAnalyst Reports
By Title
By Topic
By Product
Case Studies
By Customer
By Product
By Solution
By Industry
By Region
Customer References
FAQ’s
Pricing & Licensing
White Papers
Presentations
Industry
• Government
• Financial Services
• Healthcare
• Manufacturing
• Real Estate
• Retail
• Telecommunications
• Transportation and Distribution
• …
Product
• Web Speed Workshop
• 4GL Development System
• Translation Manager
• Roundtable
• …
Solution
• Business Continuity
• Business Intelligence
• Business Trends
• Deployment
• Development
• Integration
• …
Topic
• .NET
• Architecture
• Collaboration
• Compliance
• Distributed Applications
• Industry Standards
• JAVA
• Messaging
• …
Region
• North America
• EMEA
• Latin America
• Asia Pac
• Worldwide …
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Tuned Search, or “Best Bets”
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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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Best Bets Example – Best Buy
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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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Best Bets Example – SAP.com
� Search on “CRM” or “Customer Relationship Management”
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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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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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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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Disambiguation
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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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Disambiguation methods
� Show related search terms in the search results page.
� Show additional search terms as links, perhaps with a prompt - "You might also be interested in:"
� Expand the query and show the expanded words in the search box
� Expand the query invisibly
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Disambiguation of search results
mobile Mobile data terminalsHandheld computers
Network InfrastructureMobile switches
PhonesFixed mobile car phonesMobile phones
Software applicationsMobile applications
Two way radiosMobile radios
Intelligent video solutionsMobile video enforcerMobile video sharing
MESH SolutionsMulti-radio mobile broadband
Mobile ComputingMobile application
Presenting term in multiple contexts
Improve your ability to find critical information
From Associative Relationships
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“We should get Google”…
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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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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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Configuration process
� “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
See: http://code.google.com/enterprise/documentation/oneboxguide.html
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We still have a context
problem
“Revenue” is an ambiguous
term
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Why doesn’t Google, just use Google?
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Why you will not “just get Google”
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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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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 burdenon 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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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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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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Seth Earley
www.earley.com
781-444-0287
Send an email to [email protected] for a free pass to one of our con calls.
Questions?