Web Communities: The World Online

Raghu RamakrishnanChief Scientist for Audience and Cloud Computing

Research Fellow

Yahoo! (On leave, Univ. of Wisconsin-Madison)

Evolution of Online Communities

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Rate of content creation

• Estimated growth of content– Published content from traditional sources: 3-4

Gb/day

– Professional web content: ~2 Gb/day

– User-generated content: 8-10 Gb/day

– Private text content: ~3 Tb/day (200x more)

– Upper bound on typed content: ~700 Tb/day

(Towards a PeopleWeb, Ramakrishnan & Tomkins, IEEE Computer, August 2007)

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Metadata

• Estimated growth of metadata

– Anchortext: 100Mb/day

– Tags: 40Mb/day

– Pageviews: 100-200Gb/day

– Reviews: Around 10Mb/day

– Ratings: <small>

Drove most advances in search from 1996-present

Increasingly rich and available, but not yet useful in search

This is in spite of the fact that interactions on the web arecurrently limited by the fact that each site is essentially a silo

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PeopleWeb: Site-Centric People-Centric

• Common web-wide id for objects (incl. users)– Even common attributes? (e.g., pixels for camera objects)

• As users move across sites, their personas and social networks will be carried along

• Increased semantics on the web through community activity (another path to the goals of the Semantic Web)

Global Object

Model

Portable Social Environment

Community

Search

(Towards a PeopleWeb, Ramakrishnan & Tomkins, IEEE Computer, August 2007)

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Content Access and Ownership

(Slide courtesy Andrew Tomkins)

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Facebook Apps, Open Social

• Web site provides canvas

– Third party apps can paint on this canvas

– “Paint” comes from data on and off-network• Via APIs that each site chooses to expose What is the core asset

of a web portal?

• What are the computational implications?

– App hosting and caching

– Dynamic, personalized content

– Searching over “spaghetti” information threads

Trends in Search

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Search and Content Supply

• Premise:

– People don’t want to search

– People want to get tasks done

I want to book a vacation in Tuscany.Start Finish

Broder 2002, A Taxonomy of web search

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“seafood san francisco”

Category: restaurantLocation: San Francisco

Reserve a table for two tonight at SF’s best Sushi Bar and get a free sake, compliments of OpenTable!

Category: restaurant Location: San Francisco

Alamo Square Seafood Grill - (415) 440-2828 803 Fillmore St, San Francisco, CA - 0.93mi - map

Category: restaurant Location: San Francisco

Structure Intent

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Y! Shortcuts

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Google Base

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Search as Killer App for Web Data Semantics

• Publishers and search engine collaborate

– Example: Abstracts surfacing structured content

• Users see richer search experience

– Accomplish their tasks faster and more effectively

Social Search

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Social Search

• Explicitly open up search

– Enable communities, sites and consumers to explicitly re-define search results (e.g., SearchMonkey, Boss)

• What is the right unit for a “search result”? Can we intelligently “stitch together” more informative abstracts, possibly from multiple sources?

• Facilitate creation of specialized ranking engines based on different kinds of tasks, or aimed at different communities of users

• Implicitly leverage socially engaged users and their interactions

– Learning from shared community interactions, and leveraging community interactions to create and refine content

• Expanding search results to include sources of information

– E.g., Experts, sub-communities of shared interest, particular search engines (in a world with many, this is valuable!)

Reputation, Quality, Trust, Privacy

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Opening Up Yahoo! Search

Phase 1 Phase 2

Giving site owners and developers control over the appearance of Yahoo!

Search results.

BOSS takes Yahoo!’s open strategy to the next level by providing Yahoo!

Search infrastructure and technology to developers and companies to help them

build their own search experiences.

(Slide courtesy Prabhakar Raghavan)

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What Is It?

Before After

An open platform for using structured data to build more useful and relevant search results

(Slide courtesy Amit Kumar)

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What’s New?

task linksbuy thisuser reviewsbest trips

task linksbuy thisuser reviewsbest trips

structured datareview ratingsproduct priceshours of operation

structured datareview ratingsproduct priceshours of operation

faviconfavicon send resultshare this richresult with others

send resultshare this richresult with others

mediaproduct imagesbusiness photosprofile pictures

mediaproduct imagesbusiness photosprofile pictures

user choiceremovereport spam

user choiceremovereport spam

(Slide courtesy Amit Kumar)

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How Does It Work?

Acme.com’sdatabase

Index

RDF/Microformat Markup

Site owners/publishers share structured data with Yahoo!. 1

Consumers customize their search experience with Enhanced Results or Infobars

3

Site owners & third-party developers build SearchMonkey apps.2

DataRSS feed

Web Services

Page Extraction

Acme.com’s Web Pages

(Slide courtesy Amit Kumar)

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Publishing Structured Data: Support for Emerging Semantic Web Standards ++

• Microformats

– hCard, hEvent, hReview, hAtom, XFN

– More as they get adopted

• RDFa and eRDF markup

• OpenSearch

– +extensions to return structured data

• Atom/RSS Feeds

– +extensions to embed structured data

markup

(crawl)

apis

(pull)

push

(Slide courtesy Andrew Tomkins)

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Infobars: Integrating 3rd Party Data

Pull in data from any web service

(Slide courtesy Amit Kumar)

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babycenter

epicurious

Search Results of the Future

yelp.com

answers.com

LinkedIn

webmd

Gawker

New York Times

(Slide courtesy Andrew Tomkins)

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BOSS Offerings

API

A self-service, web services model for developers and start-ups to quickly build and deploy new search experiences.

BOSS offers two options for companies and developers and has partnered with top technology universities to drive search experimentation, innovation and research into next generation search.

• University of Illinois Urbana Champaign• Carnegie Mellon University

• Stanford University

• Purdue University

• MIT

• Indian Institute of

Technology Bombay

• University of

Massachusetts

CUSTOM

Working with 3rd parties to build a more relevant, brand/site specific web search experience.

This option is jointly built by Yahoo! and select partners.

ACADEMIC

Working with the following universities to allow for wide-scale research in the search field:

(Slide courtesy Prabhakar Raghavan)

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BOSS Could Enable Custom Search Experiences

Social Search

Vertical Search

Visual Search

(Slide courtesy Prabhakar Raghavan)

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Partner Examples

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Web Search Results for “Lisa”

Latest news results for “Lisa”. Mostly about people because Lisa is a popular name

Web search results are very diversified, covering pages about organizations, projects, people, events, etc.

41 results from My Web!

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Save / Tag Pages You Like

You can save / tag pages you like into My Web from toolbar / bookmarklet / save buttons

You can pick tags from the suggested tags based on collaborative tagging technology

Type-ahead based on the tags you have used

Enter your note for personal recall and sharing purpose

You can specify a sharing mode

You can save a cache copy of the page content

(Courtesy: Raymie Stata)

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My Web 2.0 Search Results for “Lisa”

Excellent set of search results from my community because a couple of people in my community are interested in Usenix Lisa-related topics

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Google Co-Op

This query matches a pattern

provided by Contributor…

…so SERP displays (query-specific) links

programmed by Contributor.

Subscribed Link

edit | remove

Query-based direct-display, programmed by Contributor

Users “opts-in” by “subscribing” to

them

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Tech Support at COMPAQ

“In newsgroups, conversations disappear and you have to ask the same question over and over again. The thing that makes the real difference is the ability for customers to collaborate and have information be persistent. That’s how we found QUIQ. It’s exactly the philosophy we’re looking for.”

“Tech support people can’t keep up with generating content and are not experts on how to effectively utilize the product … Mass Collaboration is the next step in Customer Service.”

– Steve Young, VP of Customer Care, Compaq

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KNOWLEDGEBASE

QUESTION

Answer added to power self service

SELF SERVICE

ANSWER

KNOWLEDGEBASE

QUESTION

SELF SERVICE

--Partner Experts-Customer Champions -Employees

Customer

How It Works

Support Agent

Answer added to power self service

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65% (3,247)

77% (3,862)

86% (4,328)

6,845

74% answered

Answersprovidedin 12h

Answersprovidedin 24h

40% (2,057)

Answersprovided

in 3h

Answersprovidedin 48h

Questions

• No effort to answer each question

• No added experts

• No monetary incentives for enthusiasts

Timely Answers

77% of answers provided within 24h

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Power of Knowledge Creation

~80%

Support Incidents Agent Cases

5-10 %

Self-Service *)

CustomerMass Collaboration *)

KnowledgeCreation

SHIELD 1

SHIELD 2

*) Averages from QUIQ implementations

SUPPORT

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Mass Contribution

Users who on average provide only 2 answers provide 50% of all answers

7 % (120) 93 % (1,503)

50 % (3,329)

100 %(6,718)

Answers

ContributingUsers

Top users

Contributed by mass of users

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Interesting Problems

• Question categorization

• Detecting undesirable questions & answers

• Identifying “trolls”

• Ranking results in Answers search

• Finding related questions

• Estimating question & answer quality

(Byron Dom: SIGIR talk)

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Supplying Structured Search Content

• Semantic Web?

• Unleash community computing—PeopleWeb!

• Three ways to create semantically rich summaries that address the user’s information needs:

– Editorial, Extraction, UGC

Challenge: Design social interactions that lead to creation and maintenance of high-quality structured content

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Better Search via Information Extraction

• Extract, then exploit, structured data from raw text:

For years, Microsoft Corporation CEO Bill Gates was against open source. But today he appears to have changed his mind. "We can be open source. We love the concept of shared source," said Bill Veghte, a Microsoft VP. "That's a super-important shift for us in terms of code access.“Richard Stallman, founder of the Free Software Foundation, countered saying…

Name Title OrganizationBill Gates CEO MicrosoftBill Veghte VP MicrosoftRichard Stallman Founder Free Soft..

PEOPLE

Select Name From PEOPLE Where Organization = ‘Microsoft’

Bill Gates

Bill Veghte(from Cohen’s IE tutorial, 2003)

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Community Information Management (CIM)

• Many real-life communities have a Web presence

– Database researchers, movie fans, stock traders

• Each community = many data sources + people

• Members want to query and track at a semantic level:

– Any interesting connection between researchers X and Y?

– List all courses that cite this paper

– Find all citations of this paper in the past one week on the Web

– What is new in the past 24 hours in the database community?

– Which faculty candidates are interviewing this year, where?

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DBLife

Integrated information about a (focused) real-world community

Collaboratively built and maintained by the community

Semantic web via extraction & community

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DBLife

• Faculty: AnHai Doan & Raghu Ramakrishnan

• Students: P. DeRose, W. Shen, F. Chen, R. McCann, Y. Lee, M. Sayyadian

• Prototype system up and running since early 2005

• Plan to release a public version of the system in Spring 2007

• 1164 sources, crawled daily, 11000+ pages / day

• 160+ MB, 121400+ people mentions, 5600+ persons

• See DE overview article, CIDR 2007 demo

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DBLife Papers

• Efficient Information Extraction over Evolving Text Data, F. Chen, A. Doan, J. Yang, R. Ramakrishnan. ICDE-08.

• Building Structured Web Community Portals: A Top-Down, Compositional, and Incremental Approach, P. DeRose, W. Shen, F. Chen, A. Doan, R. Ramakrishnan. VLDB-07.

• Declarative Information Extraction Using Datalog with Embedded Extraction Predicates, W. Shen, A. Doan, J. Naughton, R. Ramakrishnan. VLDB-07.

• Source-aware Entity Matching: A Compositional Approach, W. Shen, A. Doan, J.F. Naughton, R. Ramakrishnan: ICDE 2007.

• OLAP over Imprecise Data with Domain Constraints, D. Burdick, A. Doan, R. Ramakrishnan, S. Vaithyanathan. VLDB-07.

• Community Information Management, A. Doan, R. Ramakrishnan, F. Chen, P. DeRose, Y. Lee, R. McCann, M. Sayyadian, and W. Shen. IEEE Data Engineering Bulletin, Special Issue on Probabilistic Databases, 29(1), 2006.

• Managing Information Extraction, A. Doan, R. Ramakrishnan, S. Vaithyanathan. SIGMOD-06 Tutorial.

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DBLife

• Integrate data of the DB research community

• 1164 data sources

Crawled daily, 11000+ pages = 160+ MB / day

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Entity Extraction and Resolution

Raghu Ramakrishnan

co-authors = A. Doan, Divesh Srivastava, ...

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Resulting ER Graph

“Proactive Re-optimization

Jennifer Widom

Shivnath Babu

SIGMOD 2005

David DeWitt

Pedro Bizarrocoauthor

coauthor

coauthor

advise advise

write

write

write

PC-Chair

PC-member

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Challenges

• Extraction– Domain-level vs. site-level extraction “templates”

• Compositional, customizable approach to extraction planning

– Blending extraction with other sources (feeds, wiki-style user edits)

• Maintenance of extracted information– Managing information Extraction

– Incremental maintenance of “extracted views” at large scales

– Mass Collaboration—community-based maintenance

• Exploitation– Search/query over extracted structures in a community

– Search across communities—Semantic Web through the back door!

– Detect interesting events and changes

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Mass Collaboration

• We want to leverage user feedback to improve the quality of extraction over time.– Maintaining an extracted “view” on a collection of documents

over time is very costly; getting feedback from users can help

– In fact, distributing the maintenance task across a large group of users may be the best approach

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Mass Collaboration: A Simplified Example

Not David!

Picture is removed if enough users vote “no”.

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Mass Collaboration Meets Spam

Jeffrey F. Naughton swears that this is David J. DeWitt

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Incorporating Feedback

A. Gupta, D. Smith, Text mining, SIGMOD-06

System extracted “Gupta, D” as a person name

System extracted “Gupta, D” using rules:

(R1) David Gupta is a person name(R2) If “first-name last-name” is a person name, then “last-name, f” is also a person name.

Knowing this, system can potentially improve extraction accuracy.

(1) Discover corrective rules(2) Find and fix other

incorrect applications of R1 and R2

A general framework for incorporating feedback?

User says this is wrong

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Collaborative Editing

• Users should be able to

– Correct/add to the imported data

– E.g., User imports a paper, system provides bib item

• Challenges

– Incentives, reputation

– Handling malicious/spam users

– Ownership model• My home page vs. a citation that appears on it

– Reconciliation• Extracted vs. manual input

• Conflicting input from different users

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The Purple SOX Project

Operator Library

Extraction Management System(e.g Vertex, Societek)

Shopping,Travel,Autos

Academic Portals

(DBLife/MeYahoo)

EnthusiastPlatform

…and many others

Application Layer

(SOcial eXtraction)

Web Data Management:Massively Distributed Hosted

Systems

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An Example Web App

uploads tags as“flower”

» Friend activity » Your Photos

Sonja uploaded Brandon tagged a photo

» Photos tagged as “flower”

Updates

Queries

Heavy use of simple database operations

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The Problem

What does it take to build the next big app?

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Why Hosted?

simpleAPI

No maintenance worries for application Single ops team Resource sharing leads to savings

No maintenance worries for application Single ops team Resource sharing leads to savings

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Data Analysis Platforms

User

Tags

• Understanding online communities, and provisioning their data needs

– Exploratory analysis over massive data sets

• Challenges: Analyze shared, evolving social networks of users, content, and interactions to learn models of individual preferences and characteristics; community structure and dynamics; and to develop robust frameworks for evolution of authority and trust; extracting and exploiting structure from web content …

• Examples:

– Bigtable, Map-Reduce, Hadoop, PIG

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The Bigger Picture

• Software-as-a-service

– E.g., Salesforce.com

• Hosted data systems

– E.g., Amazon’s S3/Dynamo and EC2

• Web application development

– Ning, Ruby-on-rails

• Change tracking

– Stream management

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Implications

• Data management as a service– Scientists and others who’ve resisted (installing, maintaining, and) using DBMSs

will find it much easier to reap the benefits– “Data centers” and “Computing Centers” will come into vogue again

• Hosted back-ends and RAD tools will make Web application development accessible to all– The Web is becoming open

• E.g., OpenSocial, OpenID • Ideas will be the most valuable currency, not the wherewithal to build complex systems

• Paradigm shifts possible for how we do research in many fields– Build applications that embed your algorithms and test them directly in the field—

Computer Scientists can interact directly with users (ironically, this would still be a breakthrough of sorts after four decades!)

– Many other disciplines (e.g., Sociology, microeconomics) can design and conduct online experiments involving unprecedented numbers of participants

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Summary

• Online communities represent a tremendous resource for organizing information online

– Open APIs and cloud services = mass engagement

– Extraction + mass collaboration = semantics

• Web is becoming

– More people-centric, less site-centric

– Highly intertwined, distributed, dynamic, personalized

– Models of ownership, trust, incentives?

– Next generation of search algorithms and infrastructure?

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Further Reading

• Content, Metadata, and Behavioral Information: Directions for Yahoo! Research, The Yahoo! Research Team, IEEE Data Engineering Bulletin, Dec 2006 (Special Issue on Web-Scale Data, Systems, and Semantics)

• Systems, Communities, Community Systems on the Web, Community Systems Group at Yahoo! Research, SIGMOD Record, Sept 2007

• Towards a PeopleWeb, R. Ramakrishnan and A. Tomkins, IEEE Computer, August 2007 (Special Issue on Web Search)