An Automatic Text Mining Framework for Knowledge Discovery on the Web

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An Automatic Text Mining Framework for Knowledge
Discovery on the Web

• Wingyan Chung
• The University of Arizona
• March 30, 2004

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Acknowledgments

• NSF and NIJ Grants
• Dr. Hsinchun Chen, Dr. Jay F. Nunamaker , Dr. J.
  Leon Zhao, Dr. Richard T. Snodgrass, Dr. D.
  Terence Langendoen, Dr. Olivia Sheng
• Dept. of MIS, U. of Arizona
• Artificial Intelligence Lab, U. of Arizona

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Outline

• Introduction
• Literature Review
• Research Formulation and Approach
• Empirical Studies on Business Intelligence
  Applications
• Previous Work
• Building a BI Search Portal for Integrated
  Analysis on Heterogeneous Information
• Using Visualization Techniques to Discover BI
• Automating Business Stakeholder Analysis
• Conclusions, Limitations and Future Directions

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Introduction
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The Internet

• Advances in electronic network and IT support
  ubiquitous access to and convenient storage of
  information
• They have changed human lives fundamentally
  (Negroponte, 2003)
• The role of global electronic network
• Facilitation in communication and transaction
• The Internet emerges as the largest global
  electronic network
• Rapid growth (Lyman Varian, 2000)
• Advantages in information storage and retrieval,
  but

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Problems of the Internet
Information Overload
Convenient storage has made information
exploration difficult
Heterogeneity and unmonitored quality of
information on the Web
Information is unreliable
???
Hard to know all stakeholders
Interconnected nature of the Web complicates
understanding of relationships
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Research Questions
How can we develop an automatic text mining
approach to address the problems of knowledge
discovery on the Web?
How effective and efficient does such an approach
assist human beings in discovering knowledge on
the Web?
What lessons can be learned from applying such an
approach in the context of human-computer
interaction (HCI)?
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Literature Review

• Knowledge and Knowledge Management
• Human-Computer Interaction
• Text Mining for Web Analysis

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Knowledge

• Revealed underlying assumptions in KM
• Implied different roles of knowledge in
  organizations
• Textual knowledge - Most efficient way to store,
  retrieve, and transfer vast amount of information
• Advanced processing needed to obtain knowledge
• Traditionally done by humans
• It is useful to review the discipline of
  Human-Computer Interaction to understand human
  analysis needs

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Human Analysis Needs

• Satisfied when the problem in information seeking
  is solved (Kuhlthau, 1993 Kuhlthau, Spink and
  Cool 1992 Saracevic, Kantor, Chamis and
  Trivison, 1988 Choo et al., 2000)
• Involve value-adding processes
• Information seeking locating useful information
  from large amount of data
• Intelligence generation acquisition,
  interpretation, collation, assessment, and
  exploitation of the information obtained (Davis,
  2002)
• Relationship extraction deriving patterns and
  relationships from data and information

Knowledge Discovery
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Need Automating KD Processes

• Human beings can undertake KD processes by
  applying their experience and knowledge
• But inefficient and not scalable
• Text mining has been identified as a set of
  technologies that can automate the knowledge
  discovery process (Trybula, 1999)
• Stages information acquisition, extraction,
  mining, presentation
• Need more preprocessing when considering KD on
  the Web (more noisy, voluminous, heterogeneous
  sources) Collection building, conversion,
  extraction
• Evolved from work in automatic text processing

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Text Mining Technologies

• For Web KD
• Web mining techniques resource discovery on the
  Web, information extraction from Web resources,
  and uncovering general patterns (Etzioni, 1996)
• Pattern extraction, meta searching, spidering
• Web page summarization (Hearst, 1994 McDonald
  Chen, 2002)
• Web page classification (Glover et al., 2002 Lee
  et al., 2002 Kwon Lee, 2003)
• Web page clustering (Roussinov Chen, 2001 Chen
  et al., 1998 Jain Dube, 1988)
• Web page visualization (Yang et al., 2003
  Spence, 2001 Shneiderman, 1996)
• These techniques and approaches can be used to
  automate important parts of human analyses

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Summary

• Human analyses are precise but not efficient and
  not scalable to the growth of the Web
• A number of text mining techniques exist but
  there has not been a comprehensive approach to
  addressing problems of knowledge discovery on the
  Web, namely,
• Information overload
• Heterogeneity and unmonitored quality of
  information
• Difficulties of identifying relationships on the
  Web
• The HCI aspects of using a text mining approach
  to knowledge discovery on the Web have not been
  widely explored

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Research Formulation and Approach
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Methodology

• System Development (Nunamaker et al., 1991)
• A Multi-methodological Approach
• Conceptual frameworks, Mathematical models
• Observation, Experimentation
• Validation
• Effectiveness (accuracy, precision, recall),
  efficiency (time)
• Information quality (Wang Strong, 1996)
• User satisfaction (subjective ratings and
  comments)

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Domain of Study

• Business intelligence applications
• BI is increasingly becoming an important practice
  in today's organizations
• More than 40 surveyed individuals by Fuld Co.
  have organized BI efforts (Fuld et al., 2002)
• Collecting and analyzing BI have become a
  profession
• SCIP has over 50 chapters worldwide
• A new journal called Journal of Competitive
  Intelligence and Management was launched in 2003
• Vibrant growth of e-commerce calls for better
  approaches to knowledge discovery on the Web
  (Morgan-Stanley, 2003)
• Businesses use the Web to share and disseminate
  information
• Many companies are conducting business using the
  Internet platform (e.g., Amazon.com, EBay.com)
• Our focus is on the first category

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Empirical Studies on Business Intelligence
Applications
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Previous Work (1)

• Building a BI search portal for integrated
  analysis on heterogeneous information
• The portal provides post-retrieval analysis
  (summarization, categorization, meta-searching)
• Conducted a systematic evaluation to test
  CBizPort's ability to assist human analysis of
  Chinese BI
• Results
• Searching and browsing performance comparable to
  regional Chinese SEs
• CBizPort could significantly augment existing SEs
• Subjects strongly favored analysis capability of
  CBizPort summarizer and categorizer

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Previous Work (2)

• Applying Web page visualization techniques to
  discovering BI
• Two browsing methods (Web community and Knowledge
  map) were developed to help visualize the
  landscape of search engine results
• WC uses a genetic algorithm KM uses MDS
• The methods were empirically compared against a
  graphical search engine (Kartoo) and a textual
  result list (RL) display
• Results KM gt Kartoo (in terms of effectiveness,
  efficiency, and users' ratings on point
  placement) WC gt RL (in terms of effectiveness,
  efficiency, and user satisfaction)

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Using Web Page Classification Techniques to
Automate Business Stakeholder Analysis
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Current Business Environment

• Networked business environment facilitates
  information sharing and collaboration (Applegate,
  2003)
• Collaborative commerce automating business
  processes by electronic sharing of information
• Knowledge sharing about stakeholder relationships
  through companies Web sites and pages
• Textual content or annotated hyperlinks

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Problems

• Knowledge hidden in interconnected Web resources
• Posing challenges to identifying and classifying
  various business stakeholders
• e.g., A companys manager may not know who are
  using their companys Web resources
• Need better approaches to uncovering such
  knowledge
• Enhance understanding of business stakeholders
  and competitive environments

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Related Work

• Stakeholder theories have evolved over time while
  the view of firm changes
• Production view (19th century) Suppliers and
  Customers
• Managerial view (20th century) Owners,
  Employees
• Stakeholder view (1960-80s) (Freeman, 1984)
  Competitors, Governments, News Media,
  Environmentalists,
• E-commerce view (1990s - now) International
  partners, Online communities, Multinational
  employees,

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• P Partners/suppliers, E Employees/Unions, C
  Customers,
• S Shareholders/investors, U
  Education/research institutions, MMedia/Portals,
• G Public/government, R Recruiters, V
  Reviewers, O Competitors,
• T Trade associations, F Financial
  institutions, I Political groups,
• N SIG/Communities
• Ordered by their relevance to stakeholder types
  appearing on the Web

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Stakeholder Research and BI

• Previous research rarely considers the many
  opportunities offered by the Web for stakeholder
  analysis, e.g.,
• Business intelligence, obtained from the business
  environment, is likely to help in stakeholder
  analysis
• Tools and techniques have been developed to
  exploit business intelligence on the Web
• PageRank (Brin Page 1998), HITS (Kleinberg
  1999), Web IF (Ingwersen 1998)
• External links mirror social communication
  phenomena (e.g., stakeholder relationships)
• Ong et al. 2001 Tan et al. 2002 Reiterer et al.
  2000 Chung et al. 2003 Reid 2003 Byrne 2003
• Lack stakeholder analysis capability

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Existing BI Tools and Techniques

• Exploit structural and textual content
• But commercial BI tools lack analysis capability
  (Fuld et al. 2003)
• Need to automate stakeholder classification, a
  primary step in stakeholder analysis
• Automatic classification of Web pages is a
  promising way to alleviate the problem

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Web Page Classification

• The process of assigning pages to predefined
  categories
• Helps to classify business stakeholders Web
  pages and enables companies to understand the
  competitive environment better
• Major approaches k-nearest neighbor, neural
  network, Support Vector Machines, and Naïve
  Bayesian network (Chen Chau 2004)
• Previous work
• Kwon and Lee 2003 Mladenic 1998 Furnkranz 1999
  Lee et al. 2002 Glover et al. 2002
• NN and SVM achieved good performance

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Feature selection in Web Page Classification

• Features considered
• Page textual content full text, page title,
  headings
• Link related textual content anchor text,
  extended anchor text, URL strings
• Page structural information words, page
  out-links, inbound outlinks (i.e., links that
  point to its own company), outbound outlinks
  (i.e., links that point to external Web sites)
• Methods for selection
• Human judgment / Use of domain lexicon
• Feature ratios and thresholding
• Frequency counting / MI

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Research Gaps

• Stakeholder research provides rich theoretical
  background but rarely considers the tremendous
  opportunities offered by the Web for stakeholder
  analysis
• Conclusions drawn from old data may not reflect
  rapid development in e-commerce
• Existing BI tools lack stakeholder analysis
  capability
• Automatic Web page classification techniques are
  well developed but have not yet been applied to
  business stakeholder classification

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Research Questions

• How can we apply our automatic text mining
  approach to business stakeholder analysis on the
  Web?
• How can Web page textual content and structural
  information be used in such an approach?
• What are the effectiveness (measured by accuracy)
  and efficiency (measured by time requirement) of
  such an approach for business stakeholder
  classification on the Web?

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Application of the Approach

• Purpose To automatically identify and classify
  the stakeholders of businesses on the Web in
  order to facilitate stakeholder analysis
• Rationale
• Business stakeholders Web pages should contain
  identifiable clues that can be used to
  distinguish their types
• Web textual and structural content information is
  important for understanding the clues for
  stakeholder classification
• Two generic steps
• Creation of a domain lexicon that contains key
  textual attributes for identifying stakeholders
• Automatic classification of Web pages
  (stakeholders) linking to selected companies
  based on textual and structural content of Web
  pages

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Building a Research Testbed

• Business stakeholders of the KM World top 100 KM
  companies (McKellar 2003)
• Used backlink search function of the Google
  search engine to search for Web pages having
  hyperlinks pointing to the companies Web sites
  (e.g., linkwww.siebel.com)
• For each host company, we considered only the
  first 100 results returned
• Removed self links and extra links from same
  sites
• After filtering, we obtained 3,713 results in
  total
• Randomly selected the results of 9 companies as
  training examples (414 ? 283 pages stored in DB)

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Creation of a Domain Lexicon

• Manually read through all the Web pages of the
  nine companies business stakeholders to identify
  one-, two-, and three-word terms that were
  indicative of business stakeholder types (Thanks
  to Edna Reid)
• Extracted a total of 329 terms (67 one-word
  terms, 84 two-word terms, and 178 three-word
  terms), e.g.,

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Automatic Stakeholder Classification

• Three steps

Manual Tagging
Feature selection
Automatic classification
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Manual Tagging
Manual tagging
Feature selection
Automatic classification

• Manually classified each of the stakeholder pages
  of the nine selected companies into one of the 11
  stakeholder types (based on our literature
  review) (thanks Edna again)

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Feature Selection
Manual tagging
Feature selection
Automatic classification

• Structural content features binary variables
  indicating whether certain lexicon terms are
  present in the structural content
• A term could be a one-, two-, or three-word long
• Considered occurrences in title, extended anchor
  text, and full text (Lee et al. 2002)
• Textual content features frequencies of
  occurrences of the extracted features (see next
  slide)
• The first set of features was selected based on
  human knowledge, while the second was selected
  based on statistical aggregation (Glover et al.
  2002), thereby combining both kinds of knowledge

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Feature Selection (Textual Content)
Manual tagging
Feature selection
Automatic classification
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An Example(A media stakeholder type)
Link to the host company (ClearForest)
lthtmlgt ltheadgt ltmeta http-equiv"Content-Type"
content"text/html charsetiso-8859-1"
/gt lttitlegtDavid Schatsky Search and Discovery in
the Post-Cold War Eralt/titlegt ... ltpgtI just saw a
demo by lta href "http//www.clearforest.com"gt
ClearForest, lt/agt a company that provides tools
for analyzing unstructured textual information.
It's truly amazing, and truly the search tool for
the post-Cold War era. ... lt/pgt
... lt/bodygt lt/htmlgt
HTML hyperlink and extended anchor text
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Automatic Classification
Manual tagging
Automatic classification
Feature selection

• A feedforward/backpropagation neural network
  (Lippman 1987) and SVM (Joachims, 1998) were used
  due to their robustness in automatic
  classification
• Train the algorithms using the stakeholder pages
  of the 9 training companies and obtain a model or
  sets of weights for classification
• Test the algorithms on sets of stakeholder pages
  of 10 companies different from training examples

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Evaluation Methodology

• Motivation to know effectiveness and efficiency
  of the approach
• Consisted of algorithm comparison, feature
  comparison, and a user evaluation study
• Compared the performance of neural network (NN),
  SVM, baseline method (random classification),
  human judgment
• Compared structural content features, textual
  content features, and a combination of the two
  sets of features
• 36 Univ. of Arizona business school students
  performed manual stakeholder classification and
  provided comments on the approach

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Performance Measures

• Effectiveness
• Efficiency time used (in minutes)
• User subjective ratings and comments

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User Study

• Each subject was introduced to stakeholder
  analysis and was asked to use our system named
  Business Stakeholder Analyzer (BSA) to browse
  companies stakeholder lists
• We randomly selected three companies
  (Intelliseek, Siebel, and WebMethods) from
  testing companies to be the targets of analysis

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Definitions of business stakeholders
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Hypotheses (1)

• H1 NN and SVM would achieve similar
  effectiveness when the same set of features was
  used
• Both techniques were robust
• Procedure created 30 sets of stakeholder pages
  by randomly selecting groups of 5 stakeholder
  pages of each of the 10 testing companies

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Hypotheses (2)

• H2 NN and SVM would perform better than the
  baseline method
• Incorporated human knowledge and machine learning
  capability into the classification
• H3 Human judgment in stakeholder classification
  would achieve effectiveness similar to that of
  machine learning, but that the former is less
  efficient
• They could make use of the Web pages textual and
  structural content in classifying stakeholders
• Humans might spend more time on it

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Hypotheses (3)

• H4 H5 examined the use of different types of
  features in automatic stakeholder classification
• H4 structural textual
• H5 combined gt structural or textual alone

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Experimental Results

• Algorithm Comparison
• H1 not confirmed
• NN performed significantly differently than SVM
  when the same set of features was used
• NN performed significantly better than SVM when
  structural content features were used
• SVM performed significantly better than NN when
  textual content features or a combination of both
  feature sets were used
• More studies would be needed to identify optimal
  feature sets for each algorithm

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Effectiveness of the Approach

• H2 confirmed
• The use of any combination of features and
  techniques in automatic stakeholder
  classification outperformed the baseline method
  significantly
• Our approach has integrated human knowledge with
  machine-learned information related to
  stakeholder types
• and was significantly better than a random
  conjecture

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Comparing with Human Judgment

• H3b and H3d (efficiency) confirmed
• Human 22 minutes (average), varied
• Algorithms 1 30 seconds (average)
• Showing high efficiency of using the automatic
  approach to facilitate stakeholder analysis
• H3a and H3c (effectiveness) not confirmed
• Humans were significantly more effective than NN
  or SVM
• Could rely on more clues in performing
  classification
• Experience in Internet browsing and searching
  helped narrow down choices

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However, the algorithms achieved better
within-class accuracies than humans in frequently
occurring types
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Use of Features

• To our surprise, hypotheses H4a-b, H5a-b, and H5d
  were not confirmed
• Different feature sets yielded different
  performances of the algorithms
• Structural features enabled NN to achieve better
  effectiveness than textual ones
• Textual and combined features enabled SVM to
  achieve better effectiveness than structural ones
• Do not know exactly why
• Future research studying the effect of features
  and the nature of algorithms
• H5c was confirmed structural content feature did
  not add value to the performance of SVM

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Subjects Comments

• Overwhelmingly positive
• It would be very helpful!
• Thats cool!
• I want to use it.

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Conclusions, Limitations and Future Directions
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Conclusions

• General conclusion our approach helped alleviate
  information overload and enhance human analysis
  on the Web
• Conclusions related to this presentation
• Showed how our approach could be applied to
  business stakeholder analysis on the Web
• Integrated Human expert knowledge
  machine-learned knowledge
• Promising in terms of effectiveness and
  efficiency
• Could potentially facilitate business analysts
  interaction with automated stakeholder analysis
  systems in todays networked enterprises

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Contributions

• Developing and validating a useful and
  comprehensive approach to knowledge discovery on
  the Web
• New integration and application of techniques
  together with appropriate human intervention
• Contributions related to this presentation
• Helps BI analysts to understand business
  stakeholders more efficiently
• The feature selection approach can be used as a
  way of knowledge acquisition
• Extends current stakeholder research by providing
  a new perspective for automated analysis

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Limitations

• Technical limitations (e.g., efficiency)
• Lab experiment limits external validity
• Limitations in the presented study
• Limited data provided by Google
• The use of business school students in our study
  ? reduces external validity
• Limitation in identifying stakeholder
  relationships (only rely on hyperlinks)
• Limited domain knowledge

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Using Web Page Classification for Business
Stakeholder Analysis
Building a BI Search Portal
Applying Web Page Visualization to Exploring BI
Contributions Generic applicability Enhance
knowledge discovery on the Web Better
understanding in HCI
Problems Information overload Unreliable
information Complicated relationships
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Future Directions

• Related to the presented study
• Automate next steps of business stakeholder
  analysis
• Type-specific stakeholder analysis
• Strategic management
• Cross-regional issues
• Other domains (e.g., terrorism)
• New text mining and visualization techniques, and
  related HCI issues
• Collaborative commerce topics
• Integration of the approach with business process
  logics, collaborative technologies