executive brieï¬ng: text mining for business intelligence

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Executive Briefing: Text Mining for Business

IntelligenceSteven O. Kimbrough

Acknowledgements: Ulku Oktem, John Ranieri

2006-10-20File: textmining-businessintelligence-foils.*

INSEAD – UNILEVER workshop “Scenario Planning and Scanning on Biofuels and Related Commodities” 19-20 Oct 2006.

Goals for the Briefing• Provide a background briefing on text

mining for such purposes as business intelligence, environmental scanning, and product application discovery

• Emphasizing: concepts over products

• Concepts more advanced than products

• Aims: Secure intuitions, prepare for design

• Assuming: Familiarity with use of data for these purposes (business intelligence, etc.)

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Briefing in Three PartsA. Background on obtaining information from

text.

B. New ideas in text mining.

• Vaim concept.

C. Design exercise.

• High-level specification of requirements for business intelligence and scanning systems

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Points to Be Made, Part A

• Importance of text as a source of information for scanning and intelligence

• Limitations of Internet search engines and, in general, Information Retrieval (IR) systems.

Remember: this is a briefing. We shall be brief.

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Why Bother with Text?• Text is noisy and difficult to handle. Why not stick

with data? We know about data.

• Answer: There is much valuable information in textual sources that is not in fact available as data. It’s huge. Examples:

• News stories

• Regulatory filings

• Patents

• The World Wide Web

• et cetera

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Hasn’t Google Solved the Information Retrieval Problem?

1. No. Search engines (on the Internet or not) have important shortcomings, especially in our context.

2. No. Information Retrieval is only one technique for getting information from text. There are other techniques and they are valuable (and Internet search engines don’t tend to use them).

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The problem of getting information from text is called Information Retrieval (IR). Aren’t Internet search engines the last word on IR?

1. State-of-the-Art in Search Engines

• Impressive strengths and surprising successes

• But, all are record-oriented

• Inherent limitations?

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Information Retrieval Limitations

• Classical IR• Record-oriented. “Find me the documents relevant

to such-and-such.”• If we wish to find a few relevant documents, then

performance may be OK.• If we wish to be comprehensive (not to say

exhaustive), then performance is known to be poor.• ***Not well-suited for exploration, when you don’t

know exactly what you are looking for***• Recall/precision tradeoff• ***Futility point and defeat by scale***

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Google et alia?

• Bring something new to the table• PageRank algorithm and exploitation of the

link structure on the Web• Seeks: “authoritative pages”• Roughly, high rank on query Q = pointed

to by many pages that match to Q• Again, good for finding a few documents

about Q, if there are “authoritative pages” on Q. And if not?

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Illustration• Example query, Google or otherwise

• “light weight” and “heat resistant”

• Note: hits and responses

• Limited to documents on the web i.e., publicly available, not corporate archives

• Terminology: Hits, responses: records

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Limitation 1:

Powerful retrieval yields unmanageable numbers of hits. Ranking helpful, but of limited value.

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Limitation 2: Minimal support for telling you about something you don’t know and may find interesting

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Limitation 3: Many undifferentiated hits (except by rank). A listing of records.

2. Other Techniques• Think: repository + access methods• Data repositories:

• Relational database• Data warehouse• OLAP “cubes”

• Document repositories (much more complex):• (Special) collections of documents• Indexes of documents (in collections)• Classifications, taxonomies, ontologies• Cross-indexes (associations among

documents)• Extracted information

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2. Other Techniques• Think: repository + access methods• Data access methods

• SQL• OLAP, ROLAP, MOLAP, HOLAP• etc.

• Document access methods• Search, via Information Retrieval methods• Data access methods, after deriving data

from texta. Based largely on counts, of words,

documents, etc.b. From data obtained by IE methods

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Summary Framework

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Data Text

Record-oriented OLTP

(relational database)

IR

(inverted file)

Pattern-oriented OLAP & Data Mining

(multidimensional data “cube”)

Text Mining

(? To be determined... next)

Text Mining, Concept• A good quote:

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http://www.ischool.berkeley.edu/~hearst/text-mining.html

Text Mining is the discovery by computer of new, previously unknown information, by automatically extracting information from different written resources. A key element is the linking together of the extracted information together to form new facts or new hypotheses to be explored further by more conventional means of experimentation.

Text mining is different from what we're familiar with in web search. In search, the user is typically looking for something that is already known and has been written by someone else. The problem is pushing aside all the material that currently isn't relevant to your needs in order to find the relevant information.

In text mining, the goal is to discover heretofore unknown information, something that no one yet knows and so could not have yet written down.

So, How Do We Do It?

• How can we make discoveries in text?

• “Read it” doesn’t count as an answer.

• But, what’s nice about text is that you can read it (unlike pure data).

• Challenge: Automated support for making discoveries from text.

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text.


• Vaim concept.

C. Design exercise.


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In Brief on the Vaim ConceptThree distinctive aspects:

• P Pattern-oriented (versus record-oriented) system. Why? Focus on making discoveries, rather than retrieving a particular document. Requires categorization of documents (and data).

• D Data, which is derived from text. Measures of indicators in categories.

• M Mashing together (cross-indexing) of information from different sources.

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Points to Be Made, Part BVaim concept: value-added information mash

Focal interest: deriving indicators from texts

Examples

Concept of indicators

Comments on uses; vision

But first....

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Data Are Key

To decision making and its support

In addition to standard issues:

Obtaining data from new sources (texts)

Integrating (mashing) information from multiple sources (vaim, document sources)

Our vaim, and MV:Biofuels, work directly addresses these two issues

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Data =

Measures of variables in categories

Example: average temperature at the earth’s surface, 1950-1999

http://carto.eu.org/article2480.html

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Variables: Average temperature at the earth’s surface

Categories: Years 1950-99

Measures (or Values): As plotted, by category

Text

Data = Measures ofVariables inCategories

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Indicators & Indices

Indicator: A variable, e.g., surface temperature, stock price, number homes sold, ... thought to indicate, to carry information reliably (enough).

Index: An aggregation of indicators; it is also an indicator.

Examples: Index of Leading Economic Indicators; Dow Jones Industrial Average

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Indicators & Indices

Very widely accepted and used as inputs to managerial decision making

Further examples: Social indicators (http://unstats.un.org/unsd/demographic/products/socind/default.htm), economic indicators, ecological indicators

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MV:Biofuels• Begins with two simple ontologies: a list of

n-grams of import in the biofuels space, and a list of companies in biofuels

• Obtain documents from multiple sources. Minimally:

• Patents (presently, just US patents)

• Web pages of biofuels companies

• News articles indexed on Factiva

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Index & Cross Index

• n-grams in patents, Web pages, news articles

• companies in patents, Web pages, news articles

• patents and news articles by date

• patents by patent classification scheme

The cross indexing effects the mashing.

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Examples for Illustration

• Data charted: Two themes in biofuels

• Data charted: Four firms, four indicators, pertaining to biofuels

• Discussion of energy efficiency

Comparing Trends: Two Themes in Biofuels

1999 2000 2001 2002 2003

gov’t requirements sustainability

Sustainability gains in importance; government regulations do not.

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Associations: Comparison of 4 firms on 4 indicators

biodiesel vegetable oil biofuel sustainability

4 biofuels firmsNeste OilUS BioenergyBiodiesel InternationalCargill

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Discussion of Energy Efficiency by Year

Year Indicator Value1999 9552000 9192001 10472002 10682003 987

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Top Firms by Discussion of Energy Efficiency (1999-2003)

Firm Indicator Value

Toyota 80Canon 72

General Electric 48U. Cal. Regents 40

Matsushita 34

Fuji Xerox 31

... ...

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Top Firms by Discussion of Energy Efficiency and Biofuels (1999-2003)

Firm Indicator Value

Dakota Ag Energy, Inc 2

Squirrel Holdings, Ltd 1

Valmet Fibertech AB 1

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Classification of Patents Discussing Energy Efficiency and Biofuels

Firm No. Class Description

Dakota Ag Energy, Inc 2 435 Chemistry: molecular

biology and microbiology

Valmet Fibertech AB 1 34 Drying and gas or vapor

contact with solids

Squirrel Holdings, Ltd 1 429

Chemistry: electrical current producing

apparatus, product and process

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Classification of High-Value Indicators

Class Indicator Description62 309 Refrigeration

429 153 Chemistry: electrical current producing apparatus

347 148 Incremental printing of symbolic information

219 129 Electric heating

... ... ...

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Energy Efficiency Related PatentsIn Chemicals-Related Companies

0

5

10

15

20

25

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Air Prod.Paraxair 3M Du PontKimberly

# of PatentsRelevancy

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Energy Efficiency Related Patents

In Chemicals-Related Topics

chem of inorganic

chem:electronic

chem app & proc

drying

gas sep

heat exch

0 20 40 60 80 100

# of patents

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These Are All Examples of Data Derived from Text

• Many other innovative types of queries are possible. See the paper for more examples.

• Key elements: expertise embodied in ontologies, which are used to index and cross index the document collections; Data = (i) Measures of (ii) Variables (Indicators) in (iii) Categories

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Summary

• The (natural) magic of data

• Leads to: Can we get (useful) data from texts?

• Not “IR” or “IE”, but cheerfully accepting contributions from both fields

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Vision

• Texts, widely (& internationally) collected, categorized, indexed

• Patents, regulatory filings, regulations, technical reports, news articles, ...

• A “new world” of readily-available indicators, derived from these texts, evaluated, and used for decision making along with traditional data sources.


text.


• Vaim concept.

C. Design exercise.


42But first... Paul Kleindorfer

So Now a Design Methodology• Phase 1: Requirements. Two key questions:

• What are the indicators to track?• In what categories are the indicators to

be matched?• Phase 2: Requirements validation.

• How can these requirements be met?• NB Possibility of surrogate variables• NB IID (iterative & incremental design)

is required• How to monitor and revise?

• Are the indicators indicating?• What’s new and different?

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Phase 1: Build a Specification Table (Example)

• Rows: Variables. Columns: Categories.

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Years IntellectualProperties

Products Firms

OntologyTerms 1999-2005

“sustainability”, “biofuels”

OntologyTerms

All productsTerms: a, b, c, and d

energy efficiency

1996-2006 All firms

Phase 2: How Can It Be Done?

• Energy efficiency for Years x Firms• Annual reports and news articles are

dated. Obtain a list of firms. Obtain annual reports and/or news articles about the firms. Measure and report “energy efficiency” and related terms in these documents.

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Row 3 of the table.


• “sustainability”, “biofuels” for Years by IP• Patents and patent applications are

publicly available. They are dated (and dates can be extracted). Measure and report on “sustainability”, “biofuels” and related terms in these documents.

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Row 1 of the table.


• Terms a, b, c and d for Products.• Obtain a product classification scheme.

For each taxon in the classification scheme, obtain descriptive texts. Measure and report on a, b, c and d, and related terms, in these documents.

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Row 2 of the table.

In Summary, Part C

• The fundamentals are not new. To scan seriously, to undertake business intelligence seriously, you first identify what variables (indicators) and what categories your are interested in. Then you get the data.

• The news here is that in getting the data, collections of text may well be key sources.

• Should we try it?

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Backup Slides

• Only if needed

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Information MashingInformation Mashing. Don't you just love that term? It's one of the major goals of Sunlight and while we've been working on it for the past couple of months we have a ways to go before it happens in any substantial way. Our goal is simple: integrate in a user-friendly way individual data sets (like campaign contributions, lobbyists and government contracts) that makes the whole larger than the sum of its parts. We'd like to create something we've dubbed an “Accountability Matrix.” A website where, with one click you can look up a major donor and see not just their campaign contributions, but also their lobbying expenditures, the names of members who've flown on their private jet, the names of former congressional staffers they've hired, and so on. In a nutshell, we want to make information more liquid and more accessible to the public.

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Great Idea...but...• Needs to be generalized

• Vaim: value-added information mash

• Add linking, special knowledge processes, etc.

• Needs to be operationalized

• Main subject of this talk now

• Focus: Deriving indicators from texts

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In the interests of time...

See accompanying draft paper, “On Deriving Indicators from Texts”

Will now summarize our points/position

Under construction: MV:Biofuels (minimal vaim)

But first...

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Requirements

• (For deriving data from texts)

• “Mashing and mining”

• Mashing: integrate information from multiple sources

• Mining: as in “data mining”; find patterns in the data; pattern-oriented v. record-oriented

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Just what are data?

• Answer: Measures of indicators in categories

• Measures: induce numbers

• Indicators: numbers of what? Weight, height, count

• Categories: slice and dice the universe; patterns are constituted by measures of indicators across categories

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How can we categorize texts?

• By classification schemes: firms, products, materials; broadly: ontologies (biofuels n-grams in MV:Biofuels)

• By source: Where did the docs come from?

• By conditions; WHERE, HAVING

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News

• We did it (as a prototype) and it works, with MV:Biofuels

• Future:

• Demonstrate & explore practical usage and value

• Extend: Broader and deeper; more, more, faster, faster

executive brieï¬ng: text mining for business intelligence

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