An Automatic Text Mining Framework for Knowledge Discovery on the Web

1. An Automatic Text Mining Framework for Knowledge Discovery on the Web Wingyan Chung The University of Arizona March 30, 2004

2. 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

3. 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

4. Introduction

5. 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 …

6. Problems of the Internet To effectively and efficientlydiscover knowledge (business intelligence) from vast amount of textual information on the Web Challenges Information Overload Convenient storage hasmade information exploration difficult ??? Information is unreliable Heterogeneity and unmonitored qualityof information on the Web Hard to know all stakeholders Interconnected nature of the Web complicates understanding of relationships

7. 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)?

8. Literature Review Knowledge and Knowledge Management Human-Computer Interaction Text Mining for Web Analysis

9. Knowledge Views Classifications • Hierarchical view (Nunamaker et al., 2001) • Reversed hierarchy (Tuomi, 1999) • As a state of mind, an object, a process, access to information, and a capability (Alavi and Leidner, 2001) • Resource-based theory (Barney, 1991; Penrose, 1959; Wernerfelt, 1984; Drucker, 1995) • Tacit and explicit dimensions (Polanyi, 1965) • Individual vs. collective knowledge • Declarative vs. procedural knowledge • Causal, conditional, relational and pragmatic 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

11. 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

12. 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

14. 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

15. 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

16. Research Formulation and Approach

19. 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)

20. 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

21. Empirical Studies on Business Intelligence Applications

22. 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

23. 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 > Kartoo (in terms of effectiveness, efficiency, and users' ratings on point placement); WC > RL (in terms of effectiveness, efficiency, and user satisfaction)

24. Using Web Page Classification Techniques to Automate Business Stakeholder Analysis

25. 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

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

27. 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, …

28. P = Partners/suppliers, E = Employees/Unions, C = Customers, S = Shareholders/investors, U = Education/research institutions, M=Media/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 * †

29. 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

30. 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

31. 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

32. 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

33. 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

34. 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?

35. 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

36. 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., “link:www.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)

37. 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.,

38. Automatic Stakeholder Classification • Three steps: Manual Tagging Feature selection Automatic classification

39. Manual tagging Feature selection Automatic classification Manual Tagging • 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)

40. Manual tagging Feature selection Automatic classification Feature Selection • 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

41. Manual tagging Feature selection Automatic classification Feature Selection (Textual Content)

42. An Example(A media stakeholder type) Link to the host company (ClearForest) <html> <head> <meta http-equiv="Content-Type" content="text/html; charset=iso-8859-1" /> <title>David Schatsky: Search and Discovery in the Post-Cold War Era</title> ... <p>I just saw a demo by <a href = "http://www.clearforest.com"> ClearForest, </a> 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. ... </p> ... </body> </html> HTML hyperlink and extended anchor text

43. Manual tagging Feature selection Automatic classification Automatic Classification • 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

44. 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

45. Performance Measures Effectiveness: Efficiency: time used (in minutes) User subjective ratings and comments

46. 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

47. Business stakeholders of Siebel Definitions of business stakeholders

48. 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

49. 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 page’s textual and structural content in classifying stakeholders • Humans might spend more time on it

50. Hypotheses (3) • H4 & H5 examined the use of different types of features in automatic stakeholder classification • H4: structural = textual • H5: combined > structural or textual alone