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From Unstructured Data to Actionable Intelligence
There’s content everywhere, but
not the information you need.
Content analysis can organize
a pile of text into a richly
accessible repository.
Ramana Rao
From Unstructured
Data to Actionable
Intelligence
B
usinesses create huge amounts of potentially valuable content in the form of
documents such as e-mail messages,
drafts, project plans and reports, operational memos, customer reports, invention proposals, and research notes. However, organizations typically use such documents once and
then lose them, despite the savings they could
realize by reusing them. Internal documents
might constitute companies’ most ineffectively
utilized asset today.
The problem afflicts individual knowledge
workers as well as entire organizations.The individual typically suffers from what we call information overload—one poor soul, awash in rising
tides of content and data. Statistics indicate that
workers waste many hours searching for, sorting,
and assessing information, incurring a significant
organizational productivity cost. For example,
International Data Corp. (IDC) estimates that an
enterprise with 1,000 knowledge workers loses a
minimum of $6 million a year in the time workers
spend searching for—and not finding—needed
information (The High Cost of
Not Finding Information, IDC,
Apr. 2003, IDC #29127).
But there’s an even larger
concern, a long-term instituUnstructured—
tional problem.Workers usually
Not!
don’t waste their time fighting
systems or tasks that don’t pay
Out of the Box on
off. People almost always move
Search and Browse
on when they can’t find useful
information quickly. And they
Inside
1520-9202/03/$17.00 © 2003 IEEE
are unlikely to grind away at digesting poorly
organized or apparently featureless piles of documents. Thus, organizations don’t draw on reservoirs of information that could influence a
particular decision, task, or project. Ultimately,
this leads to uninformed decisions, overlooked
risks, and lost opportunities.
Solving this problem requires an approach to
organizing and cataloging content that is more
active than current methods. In particular, using
content effectively requires knowing more about
the content—having access to information codified as document and collection metadata.
Current systems often automatically capture various process-based metadata—for example, file
system attributes such as author, title, size, creation date, and so on. Much more useful, and
much rarer, is metadata about the document’s
actual content—for example, content summaries,
topics covered, and people or companies mentioned. The “Unstructured—Not!” sidebar explores this topic further.
This article explains two key technologies for
generating metadata about content—automatic
categorization and information extraction.These
technologies, and the applications that metadata
makes possible, can transform an organization’s
reservoir of unstructured content into a wellorganized repository of knowledge. With metadata available, a company’s search system can
move beyond simple dialogs to richer means of
access that work in more situations. Information
visualization, for example, uses metadata and our
innate visual abilities to improve access. Besides
Published by the IEEE Computer Society
November ❘ December 2003 IT Pro
29
KNOWLEDGE
MANAGEMENT
along with other data fields. All these metadatapowered applications can improve your company’s
use of its information resources.
Unstructured—Not!
Neither content nor knowledge work is truly unstructured.
Content, despite often being called “unstructured data,” is
shaped—first, by intrinsic aspects of representation and
expression and, second, by the social context in which
it is produced and consumed.
Consider a physical magazine. You would hardly
call it unstructured. You can riffle through it
quickly, rattling off observations: Here’s the table
of contents. Here’s the editor-in-chief. Oh, I hate
that color, but what a nice dress. This is an ad, this
is a feature article, and this one is really confused.
Our problem, as humans, starts not with one magazine, but with the stacks of unread magazines that
pile up over months. Even worse are the piles of electronic
documents on intranets and the Internet.
The term unstructured data refers to the difficulty of applying IT to routing and accessing content—slicing and dicing
and manipulating it in all the ways typical of data stored in
rows and columns in relational databases. The implied challenge is devising methods for extracting the latent structure
embedded in content. Without access to such a structure,
computers can’t assist us in dealing with our piling-up volumes of information.
Meanwhile, automation has focused on certain kinds of
highly structured routine work that aligns well with databases. Yet, workplace anthropologists commonly point out
that even after automation, these jobs involve much more
“unstructure” than the boss knows or, perhaps, wants to
know. Knowledge work, on the other hand, has much more
structure in it than current tools support.
We access information for various purposes and in various ways according to our purpose. Sometimes we’re surveying an area of knowledge, trying to get a general
understanding of what it’s about or what’s available. At other
times we’re searching for specific answers.
Sometimes we wander with a vague sense that something
important is on the verge of crystallizing in our understanding. Other times we’re skiing a series of tight turns, narrowing in on our target. It is this range of purpose and context
that we can better address by providing a richer set of information access tools based on exploiting metadata.
better access, metadata enables intelligent switching in the
content flows of various organizational processes—for
example, making it possible to automatically route the
right information to the right person.A third class of metadata applications involves mining text to extract features
for analysis using the statistical approaches typically
applied to structured data. For example, if you turn the text
fields in a survey into data, you can then analyze the text
30
IT Pro November ❘ December 2003
AUTOMATIC CATEGORIZATION
Portals and content management systems often
claim to provide metadata, but they in fact rely
on humans to provide any metadata beyond
what they automatically capture as they
store or route documents. Although this
approach can work well in structured work
flows, it is untenable in loosely structured
knowledge work, especially as organizations
focus on knowledge workers’ productivity.
A categorization system can generate collection catalogs or directories automatically.
A categorization system creates and maintains a
hierarchical structure of categories—called a taxonomy—and assigns documents to the categories.
A typical application is to use the taxonomy as a
navigable directory for a high-value collection of
private content—that is, you can create something
like Yahoo for your own content.
Taxonomies typically blend classification schemes
(such as the Dewey decimal system) and controlled
vocabularies (such as Library of Congress subject
headings) used in systems for cataloging and indexing library collections.They arrange subjects or topics
into a hierarchy from general to specific categories.
For example,you might see Art at the top,Postmodern
Neon Art in the middle, and American Postmodern
Neon Art in the 21st Century at the bottom.
A taxonomy’s effectiveness and design depend on
many factors. It’s easy to get lost in a thicket of theoretical concerns, but remember that a taxonomy’s
ultimate evaluation occurs at the point of use.Thus,
who the users are, what they will be doing, and what
they know and understand are much more important than abstract properties of knowledge.
General collections and tasks aren’t likely to be the
highest-value applications for enterprise categorization in the near term. For example, would the pharmaceutical company Pfizer want its intranet to
present its internal content in a news-oriented view
of the world as would Reuters or Dow-Jones? Or
would it do better to organize this content in a view
of the world according to Pfizer? Perhaps it would
ultimately want both, but the latter holds more obvious
value.After all, a privileged understanding in a focused area
of knowledge or practice is what creates a competitive
advantage.
So, a general-purpose taxonomy would probably be less
useful than appropriately specialized or even private taxonomies. Focused taxonomies are likely to make finergrain discriminations within topics in more specialized
collections, and are also likely to better match the
language and the purposes of specialized users and
uses. For example, the Medical Subject Heading
(MeSH) taxonomy, which has evolved and been
used over many years in medical research, is more
suited than a general taxonomy for organizing
Pfizer’s reports for its thousands of drug researchers.
Beyond defining and managing a taxonomy, a
categorization system must accurately assign
documents to one or more categories. Most of
the leading products do this using a mixture
of several methods, including linguistic
analysis, statistical inference, machine learning, and rule-based processing. Debates
about the relative value of the different
methods and the best ways of blending them will
likely continue. For the moment, other aspects
besides core accuracy will probably make the critical difference in your choice of categorization system. Functionality for managing taxonomies across
their full life cycle and integration of the categorization systems into enterprise environments are
particularly important. These features can have a
major impact on the total cost of ownership and the
likelihood of success during deployment.
Once the categorization system has assigned a
document to categories, the system can use the category tags or codes for several purposes:
Out of the Box on
Search and Browse
The two most common paradigms for finding documents are search and browse. With years of Internet use
behind us, the two approaches’ strengths and weaknesses are familiar: Search is precise yet brittle.
Browse is robust but vague.
Searching works quite efficiently when it works at
all. If you know what you want and how to describe
it, and if you are looking in the right place, nothing
works better. But how often do all these conditions
coincide? Search is as precise as a scalpel or laser, but
can it alone help you discover or understand the disease?
Meanwhile, browse has essentially the opposite profile.You
can always browse, but it’s not clear what you’re doing. You
scan a page, you read a little, you click, you’re somewhere
else, you aren’t sure where you are, you lose track of time.
Rather than seeing search and browse as two alternatives,
think of them as two ends of the spectrum. The structuring
and access technologies described in this article extend,
blend, or mix the best of search and browse in various ways.
Categorization by providing a nested hierarchy of queries
from general to specific provides the ease of browsing while
letting you get more and more precise, as with advanced
search. Information extraction enables dialogs that offer
menus of precise terms you might not know or remember.
And visualization gives you a way of understanding thousands of categories or documents at a time, along with a way
to navigate quickly to relevant content.
• End-user browsing. As the analogy with Yahoo
suggests, a hierarchical directory allows browsing
while gaining some of the precision and control
of searching. It effectively turns a search’s command line interface into a hierarchical menu of
topics. Browsing the taxonomy imparts an overall sense
of what’s available and how it’s organized, and then
allows drilling down to specific topics of interest.
• Document routing. Whereas the purpose of end user
browsing is to let the user access a pool of past content,
this application’s purpose is to process the stream of new
content and trigger appropriate actions or responses
within the enterprise.The system can notify a user automatically as it assigns documents to categories that relate
to that user’s work. It can route documents to the appropriate employee for processing.
• Enhanced search and browse. A system can blend a search
function with a taxonomy in several ways that enhance
searching or enable a tighter interplay between searching and browsing. Showing search results organized by
the categories of the matching documents helps a user
more quickly digest the results. Search results can also
become a point of departure for browsing, because a document’s category will probably contain other relevant
documents that might not have matched the query. A
related technique that has proven popular with Internet
directories is to search for categories and then to navigate
up and down from the matching categories. The “Out of
the Box on Search and Browse” sidebar discusses the
strengths and weaknesses of search and browse.
INFORMATION EXTRACTION
Information extraction systems extract elements of
information from documents and collections. This technique resembles what we do as we scan an article to assess
its relevance to our goals. Based on linguistic analysis and
scanning for patterns among words and phrases, extraction identifies many of the things people can quickly find
in text without reading for deeper meaning. Two levels of
extraction are common:
• Entity extraction focuses on identifying “named entities”
such as people, organizations, products, and places.Also
important are other special noun groups including large
noun phrases that indicate topics or concepts, as well as
entities such as dates, quantities, and measurements.
Even this basic level of identification can dramatically
November ❘ December 2003 IT Pro
31
KNOWLEDGE
MANAGEMENT
Figure 1. Two basic types of content
visualizations: wide widgets (a)
and content terrain maps (b).
(a)
(b)
improve higher-level capabilities including categorization, search, and automatic summarization.
• Fact extraction spreads out from entities and topics, connecting and contextualizing them in relationships, thus
expressing facts.
The simplest level of fact extraction is to determine the
roles that various entities play and the relationships among
them. For example, this type of extraction could determine
that a particular person is the CEO of one company and
also a board member for another company. This form of
link creation lets us quickly use facts in documents as a
way of understanding connections in the larger world.
Information extraction techniques can also link and contextualize topics in various ways. That a document concerns two topics together might be the key reason to select
it. The fact that two topics appear more and more regularly together in documents might suggest a synthesis or
the emergence of an important new perspective.
Another important class of facts to identify is significant
events or event classes. For example, the announcement
of a merger and acquisition, along with the roles played by
organizations as acquirer and acquired, would be information of this type.
Extraction can provide a rich stream of additional tags
that a content management system or database can store
as metadata accompanying documents to support various
32
IT Pro November ❘ December 2003
purposes. Further, fact extraction can
enable more specialized forms of
querying or action triggering:
• Document preview. Extracted entities
and facts—when displayed in search
results, directory entries, or other places
that reference documents in user inter
faces—can provide clues to a particular document’s usefulness to a specific
task. By seeing a little bit more, but not
too much, users can decide efficiently
whether to examine the whole document or complete their task using just
the preview.
• Content packaging and routing. The
richer set of tags that extraction
obtains can enable more sophisticated
forms of packaging and routing. For
example, these tags might let a publisher slice and dice its content into
new channels or product offerings.This
creates opportunities for new revenue
streams and enhances the experiences
of existing customers, increasing their
loyalty and their revenue potential
over time.
• Surveillance. Information extraction
schemes can scan new documents for new events of
interest. For example, a financial industry application
might detect mergers and acquisitions or management
personnel changes in new streams or Web sites. Security
agencies could use information extraction to detect suspicious patterns of activity in intelligence information.
• Answering questions. We can often express our specific
questions as queries structured over roles, relationships,
and entities. Combining search with fact extraction on
results sets provides a way of answering many types of
questions using a document collection—or at least of
zeroing in on the most relevant content.
INFORMATION VISUALIZATION
Information visualization involves using visual techniques to increase the bandwidth of our interaction with
information. In many ways, you might think of visualization
as supercharged browsing, but current Web browsing and
directory navigation often require much more reading and
mechanical effort than visualization would entail.
Visualization takes advantage of our evolved preattentive, automatic skills for processing large amounts of visual
information and for staying oriented in space and retaining spatial information. For example, in an array of thousands of points, we can quickly spot a single red one. We
can see small discontinuities or changes of texture or shade.
Groups, parallel structures, and shapes pop out at us.And
we are quite good at remembering that a certain sentence
was in the upper-left corner or how high it was located on
the page. Taking advantage of these innate skills is the
design opportunity of visualization.
Effective visualizations function like maps. Generally,
maps let you survey or assess an entire territory and also
navigate to specific locations. For example, a map would
let you understand the neighborhoods and terrain of San
Francisco and also plan a route from your hotel to the
Moscone Center.
As with maps, different visualization techniques might
emphasize one task or another. Some techniques emphasize big-picture thinking, whereas others might emphasize
the detail and control necessary to navigate tightly or
answer specific questions. Commercial applications are
increasingly applying two types of visualizations to content collections; Figure 1 shows examples of both:
group that stands out. Similarly, Star Tree lets users rapidly move up and down a taxonomy several levels at a time
once they learn how the taxonomy is organized.
• Interpreting items in context. “Spotlighting” search results
on a graphical visualization—using salient visual features
to mark matching items, for example, marking matches
with red dots or flags—is a powerful technique for helping users interpret search results. Spotlights let users
quickly see clusters of matches and thus focus on the
most relevant areas of the collection. Documents that
are close to matches, but that didn’t themselves match,
might in fact be quite relevant. Conversely, users could
either quickly dismiss isolated matches or, because of
their unusual context, embrace them as the most interesting. This type of result marking provides a specific
example of the more general power of visualizations to
help users interpret local features in their context.
• Content terrain maps are directly analogous to geographic terrain maps.The visualization system generates
representations of content sections and subsections, typically using automatic analysis to assign locations and
area based on the properties of documents in the collection, and to render glyphs for subsections or items in the
collection. This type of visualization supports various
forms of interactions with the map surface and with extra
tools that might search or highlight sections or items on
the map. Typical examples are Smart Money’s Map of
the Market (http://www.smartmoney.com/marketmap)
and Antarctica maps (http://www.antarctica.net).
• Wide widgets are like user interface objects that make up
a graphical user interface, but they typically show many
more objects at a time and provide much more effective
spatial management. Usually, this type of visualization
shows an interactive, visual structure that mirrors
some central spine in the information.Typical examples
are the Inxight Star Tree (http://www.inxight.com/
VizServerDemos/demo/orgchart.html) and TheBrain
(http://www.thebrain.com), which are tools for navigating large hierarchies and graphs.
APPLICATIONS FOR UNSTRUCTURED
DATA MANAGEMENT
Visualizations support several uses that synthesize
searching and browsing:
• Collection overview. By showing a rendering of the entire
collection or several levels of the taxonomy at once, a
visualization can help a user quickly get an overall sense
of the collection and its structure.The concreteness of an
effective visual structure has much to offer as a mental
map that can act as a resource for interaction over time.
• Rapid navigation.A visualization can tightly integrate navigation and interaction with pattern or anomaly observation. For example, if you regularly use Map of the Market,
you can identify and easily access the information about
the companies that stand out today or that represent a
Organizations can apply categorization, extraction, and
visualization technologies in several ways to increase the
efficiency of their content use. A typical deployment
involves a number of infrastructure elements, as Figure 2
shows, that support both IT administrators and content
organizers in the full process of deriving metadata from
content and using the metadata in user-facing applications.
The most common applications help users find and
understand documents as the need arises in their broader
activities. In such cases, a user becomes explicitly aware of
his or her information need and then searches or browses
content sources to fulfill that need. Applying categorization, extraction, or visualization can lead to faster, better
end user information access.
Two other general types of applications—routing and
mining—leverage content-based metadata in broader
organizational processes. Routing applications use the
metadata to increase the degree of automation or intelligence in the process of moving content through the organization and its surrounding network. Mining applications
enable the statistical analysis of content collections or flows
to discover patterns or to drive organizational attention.
All three types of applications are becoming common in
industry and market sectors. For an illustration of the range
of potential applications and their organizational benefits,
let’s look at what the technologies I’ve described can offer
three specific sectors—publishing, government intelligence, and life sciences research and development.
Publishing
In the publishing industry, content itself is the primary
product (or the carrier of the service). Not surprisingly,
publishing enterprises invest heavily in human activities
and content technologies to create rich metadata that
enables better access. Enhanced searching and browsing
November ❘ December 2003 IT Pro
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KNOWLEDGE
MANAGEMENT
Figure 2. Enhancing information access with metadata.
Content analysis
Search/browse
Taxonomy browsing
Result clustering
Metadata previews
Entity search/browse
Metadata
databases
Categories
Entities
Facts
Summaries
File systems
Categorization
Clustering
Entity extraction
Fact extraction
Summarization
Search indexing
Search,
Clustering
indexes
Access
E-mail
repositories
Documents
Editorial tools
Vocabularies
Taxonomy
management
Fact patterns
Web
collections
Other
enterprise
repositories
Vocabularies
Taxonomies
Rules
Indexer
IT administration
Document/
content
management
systems
Manage
capabilities can generate greater customer usage.
Furthermore, content metadata supports the publishing industry’s inherent routing-style processes for
generating a broad range of content-based products and
services. Timely, well-targeted content can have
extremely high value for particular audiences and so represents significant revenue opportunities. Content packaging, repackaging, aggregation, syndication, and
integration all have the purpose of more effectively
reaching a greater number of customers and generating
greater value.
The publishing industry is also a leading sector for mining-style opportunities. Many content customers are not
interested in the content per se, but in what it reveals
about, for instance, the market, trends, or broader realities. For example, a customer might purchase content to
support competitive intelligence or corporate licensing
activities. Needs such as these will also generate opportunities for publishers to provide content-mining applications or tools to their customers.
Intelligence and
law enforcement
Government security agencies can be quite effective
after a public event or when they have criminal suspects.
The greater challenge facing these organizations is to
notice and react to warnings and indications prior to an
event—for example, to discover a criminal’s or terrorist’s
plot in time to intervene. Toward this goal, these organizations gather huge amounts of content from public and
classified sources.
In the past, security agencies required analysts to examine documents manually to explore or substantiate theo34
IT Pro November ❘ December 2003
ries. Increasingly, they use automatic extraction technologies to identify entities and link them to one another and to
places, times, and events. Important entity types in this sector include people, places, organizations, weapons, chemical compounds, phone numbers, license plates, vehicles, and
so on. Facts or events involving these entities might include
purchase or sale events that connect people and organizations or that associate particular things or identification
numbers with particular people.
Beyond lifting the burden from personnel, automatic
extraction generally lets these organizations better direct
their attention.Analysts can move beyond the conventional
search to look for specific facts or types of occurrences and
contextualize these results against the background of the
content collection. With extracted information stored as
structured databases, analysts can explore facts and relationships directly to look for significant patterns, trends, or
anomalies. Ultimately, security organizations can apply statistical and mining techniques to automatically trigger
human involvement. These possibilities illustrate various
methods for mixing human attention and automation as
well as theory- and content-driven approaches.
Pharmaceutical Research and Development
Improving the efficiency and effectiveness of the drug
development process is a major business priority for pharmaceutical and biotechnology companies. Drug development typically spans 10 to 15 years and involves contributions
from geneticists, biologists, chemists, chemical engineers,
medical researchers, and other specialists. The current estimated cost of successfully bringing a new drug to approval
is more than $600 million. Revenue from a major drug,
however, typically runs into billions of dollars.
Existing content—including scientific literature and the
pharmaceutical company’s internal content—contains
information that can help improve time to market, decrease
costs of unproductive efforts, and facilitate valuable discoveries.To support reuse and sharing of internal content,
many life sciences companies have deployed search engines
in their content management or portal efforts.As they have
begun to see the limits of traditional search, they have
started to deploy categorization solutions with specialized
taxonomies or vocabularies—for example, MeSH (http://
www.nlm.nih.gov/mesh/meshhome.html) or the Gene
Ontology (http://www.geneontology.org).
Content mining in early stages of drug discovery can help
identify the most promising avenues or cancel work on
paths unlikely to succeed. Both external and internal documents often contain specific information about compounds, genes, proteins, diseases, and symptoms and
establish links among these entities. A drug development
team might use these patterns and statistics, for example,
to understand diseases and mechanisms, to identify promising targets, and to optimize leads. A form of highthroughput information screening can isolate relevant
connections between compounds and proteins, genes,
diseases, and so on.
Content mining can also support strategic decision making in life sciences organizations. For example, manage-
ADVERTISER
/
ment must select therapeutic areas to invest in, understand
the competitive situation in the marketplace, and manage
effective patenting, partnering, and licensing strategies.
Broader clinical, manufacturing, and marketplace activities all produce volumes of textual content including
patents, adverse event reports, competitive analyses, analyst reports, industry news, clinical reports, and internal
memos. Pharmaceutical companies can mine all of this content to support decision making and planning.
T
he library and information sciences discipline has
long appreciated how important organization is to
providing access to information.Without a disciplined
cataloging system, finding and accessing relevant books
become all but impossible. Similarly, your organization’s
content management system might be storing away documents never to be used again. But current commercial content analysis technologies can provide metadata about
collections and documents, enabling applications for better access, routing, and mining of your company’s precious
information. ■
Ramana Rao is the CTO and founder of Inxight Software
and the editor of the monthly newsletter Information Flow
(http://www.ramanarao.com/informationflow/). Contact
him at [email protected]
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