What is IOA?

What is Information Organization and Access?

www.aiim.org/training

How to find information?
Option 1:
Search
65,400,000 hits when searching for “good red wine”
Little or no metadata / taxonomy

How to find information?
Option 2:
Browsing
Ability to find the wine you need via 9 different categories
Requires metadata / taxonomy

What is IOA?
IOA, or Information Organization and Access, consists of a content preparation process and a content search and access process
During the preparation content is captured, prepared, enriched, and indexed;
During the access process, someone searches for and accesses content

Parts of IOA
Information Organization
Content Architecture
Structure and composition of a repository, information collection, or individual document
Content Intelligence
Enriching content with additional information
Information Access
Search and Retrieval
Querying information sets and obtaining documents
Findability
Enhancing access to the right information

How to organize information to improve access?

What is Content Intelligence?
Adding “meaning” to information by structuring, classifying, and/or labeling the content so it is more findable by both people and technology
In short, enriching the content
Metadata
“ Data about the data”
Usually a discreet component
Classification of content
Taxonomy
Law for categorizing information

Metadata Fundamentals
Metadata consists of statements we make about resources to help us find, identify, use, manage, evaluate, and preserve them -- and perhaps dispose of them
Metadata building blocks
The basic unit of metadata is a statement
A statement consists of a property (aka, element) and a value
e.g., The shirt has a color (property), which is blue (value)
Metadata statements describe resources that can be used by content technologies
e.g., Display all information that is about blue products

Categorizing Metadata Asset metadata – Who: Creator, Publisher, Contributor, Type, Format, Identifier Subject metadata – What, Where & Why: Subject, Title, Description, Coverage Relational metadata – Links between and to: Source, Relation Use metadata – When & How: Date, Language, Rights Source: Taxonomy Strategies, LLC

Where Value Emerges Asset metadata – Who: Creator, Publisher, Contributor, Type, Format, Identifier Subject metadata – What, Where & Why: Subject, Title, Description, Coverage Relational metadata – Links between and to: Source, Relation Use metadata – When & How: Date, Language, Rights More efficient content processing Better navigation & discovery Source: Taxonomy Strategies, LLC

Taxonomy and Content Management
Taxonomies often act as a “great unifier” in the area of content technologies and enable them to work together
Many content management systems depend on solid metadata and taxonomy in order to add significant value
Taxonomy is a key enabler for ECM
Essential for organizing any large content corpus
Required for meaningful records management
Critical to effective findability
Ideal way to represent logical hierarchy
How you choose to design the taxonomy in the repository, and how the system you choose can use a taxonomy, greatly influence the business value you can realize

Understanding taxonomies
A taxonomy is a classification scheme
Such as the way that an individual classifies the content of their e-mail inbox, a personal cd collection, or the contents on an iPod
A taxonomy is a knowledge map
Reflects how it’s owner conceives a given body of content (a knowledge domain), for purposes of browsing, navigating, discovering, and sharing that information
A taxonomy is semantic
Indicating the relationships between concepts, such as the relationships between a car and a steering wheel, in that the steering wheel is a “part of” a car
Source: Organising Knowledge (Patrick Lambe, 2007)

Representations of taxonomies
Lists
Trees
Hierarchies
Polyhierarchies
Matrices
Facets
System Maps
© AIIM | All rights reserved Source: Organising Knowledge (Patrick Lambe, 2007) List Matrices Facets System Maps

What is a Vocabulary?
Vocabularies represent potential metadata values
Vocabularies can be controlled or uncontrolled
Controlled vocabularies: metadata must come from a set list (e.g. “Province”)
Uncontrolled vocabularies: metadata can be applied free-form (e.g. “Town”)
“ Taxonomies” are a particular type of controlled vocabulary
But not all controlled vocabularies are taxonomies
We’ll discuss taxonomies in the next module

Why Use Controlled Vocabularies?
It’s important to control vocabulary so your searchers don’t have to
Standards need to be set to minimize confusion among taggers/indexers
Enforces terminological consistency
Reduces spelling mistakes
Enables interoperability

What is a Thesaurus?
Thesaurus : is a networked collection of controlled vocabulary terms, using associative rather than strict hierarchical relationships
Often used to control synonyms across vocabularies or taxonomies
But more generally can identify the relationships among terms
E.g. Equal to, Related to, Opposite of
Some examples from a hypothetical domain
Lettuce = Greens = Fris ée (a.k.a, ‘a synonym ring’)
Coriander is related to Cilantro
Thesauri can be enormously useful in an enterprise setting
When different units have different taxonomies where systems need to cross-walk
When the enterprise cannot agree on a common vocabulary

What is an Ontology?
The formal definition of ontology is " the specification of one's conceptualization of a knowledge domain ”
Semantic technologies are typically centered around ontologies
Ontologies:
Resemble faceted taxonomies and often subsume thesauri, but employ richer semantic relationships among terms and attributes
Apply rules specifying terms and relationships
Do more than just control vocabulary
Are a knowledge representation
Thus, an ontology for salad would contain the structure for how it relates to everything, from ingredients to growers to the rodents that might eat it, and how a salad is different in Japan vs. Italy

Ontology Example Capturing all the uses of ice cream… A complete ontology would account for more relationships and properties. Source: Roz Chast, The New Yorker

What’s a Topic Map?
A topic map is a visual representation of a knowledge domain
Topic maps are an ISO standard for the representation and interchange of knowledge, with an emphasis on the findability of information. The standard is formally known as ISO/IEC 13250:2003
The topics that populate the map are an ontology – topic maps are thus ontology-driven – like the ice cream example

Folksonomy : the anti-controlled vocabulary. Collaborative vocabularies for tagging content, rarely with any sort of control
Relevance between metadata and content may be determined by users in a democratic fashion
four users define an object as being “green”
one user defines an object as being “aqua”
relevance can be defined as " more green than aqua ”
Over time, clusters emerge and communities typically self-organize around them
“ Wisdom of the crowd”
Typically arise in Web-based communities where individuals to share content, then create and use tags (e.g., blogs)
Applied to enterprise use cases when there is a critical mass of taggers to make it worthwhile
Can be a useful “bottom-up” approach to developing taxonomies
What is a Folksonomy?

Folksonomy Example Source: flickr.com

The importance of Findability

What is Findability?
Findability is the quality of being locatable or navigable
At the core of IOA is the findability of information. Information should be easy to discover or locate
Information access is about helping users find documents that satisfy their information needs
Remember, someone may be looking for something they’ve never seen or touched before
Advanced information organization techniques can support findability
Thesauri, Ontologies, Topic Maps and Semantic Networks
Faceted search and navigation

Access via Browse
Browsing is usually the first option for users seeking information or documents
Desktop and enterprise file systems
Content management system repositories
Intranets and Websites
If users can’t find via browse, then they resort to search
Some users will go straight to search
This is partly generational

Effective Browsing
Browsing effectiveness is highly dependent on
navigational structure
folder labeling
the location of the content
In short: depends on how organized the content is…
Content technologies typically use “virtual folders” to represent different classifications
These allow for multiple paths to the same content
In contrast: physical file system forces documents to a single “place”
Ideally content should be cross-referenced , but not duplicated

Access via Search
Search is an application or tool for finding information via search term
Search is omnipresent, and essential
But: there is much ignorance about how search engines work
Most end-users shouldn’t need to know; they just assume “magic”
Advanced display techniques can blur the line between search and browse
Search is not a magic bullet or effective panacea for lack of information organization
Better-organized information will yield more effective search results

How Enterprise Subsystems Work Together Source: CMS Watch

What Is An Effective Search Result?
When a user finds what they are seeking
Or not…
Seekers may find more than one answer
Two ways to measure results effectiveness:
Precision and recall are frequently traded off in actual search implementations
“ Tuning” for one can reduce the other…
Precision Percentage of all returns in a results set that are relevant to the query
Recall Percentage of relevant documents that were actually returned in the results set

The Myriad of Search Choices
Vendors recognise the importance of search
Beware of how they push enterprise search as the answer to an organization’s need for a single, unified window into everything the organization knows at any point in time
The ultimate knowledge management machine simply does not exist: the typical enterprise search system does not contain “all” the organization's content
Limitations on available information include:
Security considerations
Inability to integrate specialized content
Difficulty reconciling structured and unstructured content
Cost, time, and difficulty required to incorporate diverse content repositories

Current Trends in Search As search sector changes, distinctions among different “flavors” of search technology, features, and functions become more difficult to make. Source: CMS Watch

Federated Search
The modern enterprise is not a monolith
Multiple information repositories
Multiple search engines
Need to search across information domains from a single query interface
Federated search approaches are designed to accomplish this
Sometimes called “meta search”
Two approaches to federated search
Use the same search technology across information sets, but create separate indexes and merge results
Use multiple search technologies, passing query over heterogeneous indexes, and synthesizing multiple result sets (more common)

Federated Search Example
Has seen success on the public web
No security issues around public info
Limited set of file types
Better metadata can improve results merging
Example: “Merlot”
Meta search engine for education resources on the public web ( www.merlot.org )

Challenges of Federated Search
Federated search within the enterprise tends to be much harder
Multiple indexes mean multiple security systems to resolve
Different index and query approaches across search systems may skew results
Often prohibitive performance problems
Results must be de-duped, transferred, merged, and ranked

The Case for Text Mining
Enterprises looking for better findability face two vexing challenges:
How to yield metadata from large quantities of information?
How to turn “search” into more powerful navigation and discovery?
Text Mining offers one answer
Text mining is partly a more attractive marketing term for auto-classification – a term that aligns with the concept of “data mining”
But text mining takes auto-classification one step further through the discovery of more sophisticated patterns in text
However, there are many different approaches to text mining
Text mining is sometimes called “text analytics” or “content intelligence”

How Text Mining Works
Prior to indexing content, information is “discovered” or derived from a corpus of content
The goal of text mining is to glean information from data, find patterns, and “separate signal from noise”
It does this by attempting to extract “entities” and “relationships” from text
Relevant information is usually derived through the divining of patterns and trends
Text is then parsed (sometimes adding and removing certain pieces of text for the purposes of an index)
Typical text mining tasks can include auto-classification, clustering, concept/entity extraction, auto-categorization (production of taxonomies), document summarization, and entity relation modeling (i.e., learning relations between entities, as in an ontology)
Different text mining tools tend to excel at one or just a handful of these approaches

A Clustering Example Source: cwi.nl & Inxight

A Clustering Example Source: London Natural History Museum & Inxight

IOA Strategy IOA as a Practice IOA as a Project IOA Master For more information: AIIM IOA Certificate Program www.aiim.org/training

Find, Inventory, Analyze Content Metadata Taxonomy Ontologies and Topic Maps Content Modelling Introduction to Access Search Techniques Topics in Findability User Experience Of IOA Parts of IOA For more information: AIIM IOA Practitioner www.aiim.org/training

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What is IOA?

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