WebWeb MiningMining ResearchResearch: AA SurveySurvey

Raymond Kosala and Hendrik Blockeel

ACM SIGKDD , July 2000

Presented by Shan Huang,

4/24/2007

Outline

Introduction

Web Mining

Web Content Mining

Web Structure Mining

Web Usage Mining

Conclusion & Exam Questions

Four Problems Finding relevant information

Low precision Unindexed information

Creating new knowledge out of available information on the web

Personalizing the information Catering to personal preference in content and

presentation Learning about the consumers

What does the customer want to do? Using web data to effectively market products and/or

services

Other Approaches

Web mining is not the only approach Database approach (DB) Information retrieval (IR) Natural language processing (NLP)

In-depth syntactic and semantic analysis Web document community

Standards, manually appended meta-information, maintained directories, etc

Direct vs Indirect Web Mining

Web mining techniques can be used to solve the information overload problems: Directly

Attack the problem with web mining techniques E.g. newsgroup agent classifies news as relevant

Indirectly Used as part of a bigger application that addresses

problems E.g. used to create index terms for a web search

service

The Research

Converging research from: Database, information retrieval, and artificial intelligence (specifically NLP and machine learning)

Paper focuses on research from the machine learning point of view

Outline

Introduction

Web MiningWeb Mining

Web Content Mining

Web Structure Mining

Web Usage Mining

Conclusion & Exam Questions

Web Mining: Definition

“Web mining refers to the overall process of discovering potentially useful and previously unknown information or knowledge from the Web data.” Can be viewed as four subtasks Not the same as Information Retrieval Not the same as Information Extraction

Web Mining: Subtasks

Resource finding Retrieving intended documents

Information selection/pre-processing Select and pre-process specific information from selected

documents

Generalization Discover general patterns within and across web sites

Analysis Validation and/or interpretation of mined patterns

Web Mining: Not IR or IE

Information retrieval (IR) is the automatic retrieval of all relevant documents while at the same time retrieving as few of the non-relevant documents as possible

Web document classification, which is a Web Mining task, could be part of an IR system (e.g. indexing for a search engine)

Web Mining: Not IR or IE

Information extraction (IE) aims to extract the relevant facts from given documents while IR aims to select the relevant documents IE systems for the general Web are not feasible Most focus on specific Web sites or content

Web Mining and Machine Learning

As a broad subfield of artificial intelligence, machine learning is concerned with the development of algorithms and techniques that allow computers to "learn". Web mining not the same as learning from the Web.Some applications of machine learning on the web are not Web MiningSome methods used for Web Mining besides machine learningHowever, there is a close relationship between web mining and machine learning.

Outline

Introduction

Web Mining

Web Content Mining

Web Structure Mining

Web Usage Mining

Conclusion & Exam Questions

Web Mining CategoriesWeb Content Mining Discovering useful information from web

contents/data/documents. IR view for finding DB view for modeling

Web Structure Mining Discovering the model underlying link structures (topology) on the

Web E.g. discovering authorities and hubs

Web Usage Mining Make sense of data generated by surfers Usage data from logs, user profiles, user sessions, cookies, user

queries, bookmarks, mouse clicks and scrolls, etc.

Web Content Data Structure

Unstructured – free text

Semi-structured – HTML

More structured – Table or Database generated HTML pages

Multimedia data – receive less attention than text or hypertext

Web Mining: The Agent Paradigm

User Interface Agents information retrieval agents, information filtering

agents, & personal assistant agents.

Distributed Agents distributed agents for knowledge discovery or data

mining. Problem solving by a group of agents

Mobile Agents

Web Mining: The Agent Paradigm

Content-based approach The system searches for items that match based

on an analysis of the content using the user preferences.

Collaborative approach The system tries to find users with similar

interests Recommendations given based on what similar

users did

Outline

Introduction

Web Mining

Web Content Mining

Web Structure Mining

Web Usage Mining

Conclusion & Exam Questions

Web Content Mining: IR View

Unstructured Documents Bag of words, or phrase-based feature

representation Features can be boolean or frequency based Features can be reduced using different feature

selection techniques Word stemming, combining morphological

variations into one feature

Web Content Mining: IR View

Semi-Structured Documents Uses richer representations for features, based on

information from the document structure (typically HTML and hyperlinks)

Uses common data mining methods (whereas unstructured might use more text mining methods)

Web Content Mining: DB View

Tries to infer the structure of a Web site or transform a Web site to become a database Better information management Better querying on the Web

Can be achieved by: Finding the schema of Web documents Building a Web warehouse Building a Web knowledge base Building a virtual database

Web Content Mining: DB View

Mainly uses the Object Exchange Model (OEM) Represents semi-structured data (some structure,

no rigid schema) by a labeled graph

Process typically starts with manual selection of Web sites for content miningMain application: building a structural summary of semi-structured data (schema extraction or discovery)

Outline

Introduction

Web Mining

Web Content Mining

Web Structure Mining

Web Usage Mining

Conclusion & Exam Questions

Web Structure Mining

Interested in the structure between Web documents (not within a document)Inspired by the study of social networks and citation analysisExample: PageRank – GoogleApplication: Discovering micro-communities in the WebMeasuring the “completeness” of a Web site

Outline

Introduction

Web Mining

Web Content Mining

Web Structure Mining

Web Usage Mining

Conclusion & Exam Questions

Web Usage Mining

Tries to predict user behavior from interaction with the WebWide range of data (logs)

Web client data Proxy server data Web server dataTwo common approaches

1. Map usage data into relational tables before using adapted data mining techniques

2. Use log data directly by utilizing special pre-processing techniques

Web Usage Mining

Typical problems: Distinguishing among unique users, server sessions, episodes, etc in the presence of caching and proxy servers

Often Usage Mining uses some background or domain knowledge E.g. site topology, Web content, etc

Web Usage Mining

Two main categories:1. Learning a user profile (personalized)

Web users would be interested in techniques that learn their needs and preferences automatically

2. Learning user navigation patterns (impersonalized)

Information providers would be interested in techniques that improve the effectiveness of their Web site or biasing the users towards the goals of the site

Outline

Introduction

Web Mining

Web Content Mining

Web Structure Mining

Web Usage Mining

Conclusion & Exam Questions

Conclusions

Tried to resolve confusion with regards to the term Web Mining Differentiated from IR and IE

Suggest three Web mining categories: Content, Structure, and Usage Mining

Briefly described approaches for the three categories

Explored connection with agent paradigm

Exam Question #1

Question: Outline the main characteristics of Web information.

Answer: Web information is huge, diverse, and dynamic.

Exam Question #2

Question: How data mining techniques can be used in Web information analysis? Give at least two examples. Classification: classification on server logs using decision

tree, Naïve-Bayes classifier to discover the profiles of users belonging to a particular class

Clustering: Clustering can be used to group users exhibiting similar browsing patterns.

Association Analysis: association analysis can be used to relate pages that are most often referenced together in a single server session.

Exam Question #3

Question: What are the three main areas of interest for Web mining?

Answer: (1) Web Content

(2) Web Structure

(3) Web Usage

Thank you!