Improving Findability: The Role of Information Architecture in Effective Search

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

[email protected]

© 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


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.


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


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


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)


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


“…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


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)


More

Structured

Email

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


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)


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)


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


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


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”


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


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



Improve your ability to find critical information

Why the metadata tutorial?

One word: faceted search


Faceted Search/Guided Navigation


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?


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


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


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


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


Facets Taxo term values



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


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


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


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.


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


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


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


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


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


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


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


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


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


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


Sales ToolsAnalyst Reports

Case Studies

Customer References

FAQ’s

Pricing & Licensing

White Papers

Presentations

Navigation versus Classification



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




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




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 leverages Classification


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 …


Tuned Search, or “Best Bets”


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.


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”


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


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


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


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


From Associative Relationships

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“We should get Google”…


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


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




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


We still have a context

problem

“Revenue” is an ambiguous

term


Why doesn’t Google, just use Google?


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


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.


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


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


Seth Earley

[email protected]

www.earley.com

781-444-0287

Send an email to [email protected] for a free pass to one of our con calls.

Questions?