Title: An Automatic Text Mining Framework for Knowledge Discovery on the Web
1An Automatic Text Mining Framework for Knowledge
Discovery on the Web
- Wingyan Chung
- The University of Arizona
- March 30, 2004
2Acknowledgments
- 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
3Outline
- 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
4Introduction
5The 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
6Problems 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
7Research 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)?
8Literature Review
- Knowledge and Knowledge Management
- Human-Computer Interaction
- Text Mining for Web Analysis
9Knowledge
- 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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11Human 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
12Need 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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14Text 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
15Summary
- 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
16Research Formulation and Approach
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19Methodology
- 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)
20Domain 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
21Empirical Studies on Business Intelligence
Applications
22Previous 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
23Previous 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)
24Using Web Page Classification Techniques to
Automate Business Stakeholder Analysis
25Current 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
26Problems
- 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
27Related 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,
28- 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
29Stakeholder 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
30Existing 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
31Web 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
32Feature 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
33Research 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
34Research 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?
35Application 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
36Building 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)
37Creation 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.,
38Automatic Stakeholder Classification
Manual Tagging
Feature selection
Automatic classification
39Manual 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)
40Feature 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
41Feature Selection (Textual Content)
Manual tagging
Feature selection
Automatic classification
42An 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
43Automatic 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
44Evaluation 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
45Performance Measures
- Effectiveness
- Efficiency time used (in minutes)
- User subjective ratings and comments
46User 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
47Definitions of business stakeholders
48Hypotheses (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
49Hypotheses (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
50Hypotheses (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
51Experimental 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
52Effectiveness 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
53Comparing 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
54However, the algorithms achieved better
within-class accuracies than humans in frequently
occurring types
55Use 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
56Subjects Comments
- Overwhelmingly positive
- It would be very helpful!
- Thats cool!
- I want to use it.
57Conclusions, Limitations and Future Directions
58Conclusions
- 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
59Contributions
- 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
60Limitations
- 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
61Using 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
62Future 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