p39 ramakrishnan

Community Systems:The World Online: 

Community Systems: The World Online Raghu Ramakrishnan VP and Research Fellow Yahoo! Research

The Evolution of the Web: 

The Evolution of the Web “You” on the Web (and the cover of Time!) Social networking UGC: Blogging, tagging, talking, sharing

The Evolution of the Web: 

The Evolution of the Web “You” on the Web (and the cover of Time!) Social networking UGC: Blogging, tagging, talking, sharing Increasing use of structure by search engines

Y! Shortcuts: 

Y! Shortcuts

Google Base: 

Google Base

DBLife: 

DBLife Integrated information about a (focused) real-world community Collaboratively built and maintained by the community Semantic web, bottom-up

The Web: A Universal Bus: 

The Web: A Universal Bus People to people Social networks People to apps/data Email Apps to Apps/data Web services, mash-ups

A User’s View of the Web: 

A User’s View of the Web The Web: A very distributed, heterogeneous repository of tools, data, and people A user’s perspective, or “Web View”:

Grand Challenge: 

Grand Challenge How to maintain and leverage structured, integrated views of web content Web meets DB … and neither is ready! Interpreting and integrating information Result pages that combine information from many sites Scalable serving of data/relationships Multi-tenancy, QoS, auto-admin, performance Beyond search—web as app-delivery channel Data-driven services, not DBMS software Desktop Web-top

Outline: 

Outline Community Systems research at Yahoo! Social Search Tagging (del.icio.us, Flickr, MyWeb) Knowledge sharing (Y! Answers) Structure Community Information Management (CIM) Web as app-delivery channel Mail and beyond

Community Systems Group@ Yahoo! Research: 

Community Systems Group @ Yahoo! Research Raghu Ramakrishnan Sihem Amer-Yahia Philip Bohannon Brian Cooper Cameron Marlow Dan Meredith Chris Olston Ben Reed Jai Shanmugasundaram Utkarsh Srivastava Andrew Tomkins

What We Do: 

What We Do Science of social search: Use shared interactions to Improve ranking of web-search results Enable focused content creation Go beyond content search to people search Foundations of online communities: Powering community building and operation Understanding community interactions

Social Search: 

Social Search Improve web search by Learning from shared community interactions, and leveraging community interactions to create and refine content Enhance and amplify user interactions Expanding search results to include sources of information (e.g., experts, sub-communities of shared interest) Reputation, Quality, Trust, Privacy

Web Data Platforms: 

Web Data Platforms Powering Web applications A fundamentally new goal: Self-tuning platforms to support stylized database services and applications on a planet-wide scale Challenges: Performance, Federation, Reliability, Maintainability, Application-level customizability, Security, Varied data types & multimedia content, extracting and exploiting structure from web content … Understanding online communities 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

Two Key Subsystems: 

Two Key Subsystems Serving system Takes queries and returns results Content system Gathers input of various kinds (including crawling) Generates the data sets used by serving system Both highly parallel Serving System Content System Data sets Users Logs Web sites Data updates Goal: speedup. Hardware increments speed computations. Goal: scaleup. Hardware increments support larger loads. (Courtesy: Raymie Stata)

Social Search: 

Social Search Is the Turing test always the right question?

Brief History of Web Search: 

Brief History of Web Search Early keyword-based engines WebCrawler, Altavista, Excite, Infoseek, Inktomi, Lycos, ca. 1995-1997 Used document content and anchor text for ranking results 1998+: Google introduces citation-style link-based ranking Where will the next big leap in search come from? (Courtesy: Prabhakar Raghavan)

Social Search: 

Social Search Putting people into the picture: Share with others: What: Labels, links, opinions, content With whom: Selected groups, everyone How: Tagging, forms, APIs, collaboration Every user can be a Publisher/Ranker/Influencer! “Anchor text” from people who read, not write, pages Respond to others People as the result of a search!

Four Types of Communities: 

Four Types of Communities Marketplaces Trusted transactions eBay, Craigslist

The Power of Social Media: 

The Power of Social Media Flickr – community phenomenon Millions of users share and tag each others’ photographs (why???) The wisdom of the crowds can be used to search The principle is not new – anchor text used in “standard” search (Courtesy: Prabhakar Raghavan)

Anchor text : 

Anchor text When indexing a document D, include anchor text from links pointing to D. www.ibm.com Armonk, NY-based computer giant IBM announced today Joe’s computer hardware links Compaq HP IBM Big Blue today announced record profits for the quarter (Courtesy: Prabhakar Raghavan)

Save / Tag Pages You Like : 

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)

Web Search Results for “Lisa” : 

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!

My Web 2.0 Search Results for “Lisa” : 

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

Searching Yahoo! Groups: 

Searching Yahoo! Groups Over 7M groups!

What is a Relevant Group?: 

What is a Relevant Group? A group whose content is relevant to the query keywords. A group to which many of my buddies belong. A group where many of my buddies post messages. A group with some of my preferred characteristics: traffic, membership. (Courtesy: Sihem Amer-Yahia)

Search Within a Group: 

Search Within a Group Messages in a group stored in one mbox file distributed across 20 machines. Each mbox is at most 2MB. Large groups have 1000 messages and large messages are 2KB. Search on: Message: author (name, email address, Y! alias, YID), body, subject, is-spam, is-special-notice, is-topic Thread: returned if its first message is on the input topic Messages returned sorted by date. (Courtesy: Sihem Amer-Yahia)

Some Challenges in Social Search: 

Some Challenges in Social Search How do we use annotations for better search? How do we cope with spam? Ratings? Reputation? Trust? What are the incentive mechanisms? Luis von Ahn (CMU): The ESP Game

DB-Style Access Control: 

DB-Style Access Control My Web 2.0 sharing modes (set by users, per-object) Private: only to myself Shared: with my friends Public: everyone Access control Users only can view documents they have permission to Visibility control Users may want to scope a search, e.g., friends-of-friends Filtering search results Only show objects in the result set that the user has permissions to access in the search scope (Courtesy: Raymie Stata)

Question-Answering Communities A New Kind of Search Result: People, and What They Know : 

Question-Answering Communities A New Kind of Search Result: People, and What They Know

Slide38: 

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

Slide39: 

KNOWLEDGE BASE QUESTION Answer added to power self service SELF SERVICE ANSWER KNOWLEDGE BASE QUESTION SELF SERVICE Customer HOW IT WORKS Support Agent Answer added to power self service

Slide40: 

SELF-SERVICE

Slide41: 

PARTICIPATION

Slide42: 

REPUTATION

Slide43: 

RATINGS, QUALITY

Slide44: 

65% (3,247) 77% (3,862) 86% (4,328) 6,845 74% answered Answers provided in 12h Answers provided in 24h 40% (2,057) Answers provided in 3h Answers provided in 48h Questions No effort to answer each question No added experts No monetary incentives for enthusiasts TIMELY ANSWERS 77% of answers provided within 24h

Slide45: 

POWER OF KNOWLEDGE CREATION ~80% Support Incidents Agent Cases 5-10 % Self-Service *) Customer Mass Collaboration *) Knowledge Creation SHIELD 1 SHIELD 2 *) Averages from QUIQ implementations SUPPORT

Slide46: 

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 Contributing Users Top users Contributed by mass of users

Slide47: 

COMMUNITY STRUCTURE ? COMMUNITY EXPERTS ENTHUSIASTS AGENTS SUPERVISORS EDITORS ESCALATION COMPAQ APPLE MICROSOFT ROLES vs. GROUPS

Structure on the Web: 

Structure on the Web

Slide49: 

Make Me a Match! USER – AD CONTENT - AD USER - CONTENT

Tradition: 

Keyword search: seafood san francisco Tradition

Structure: 

“seafood san francisco” Category: restaurant Location: San Francisco Structure

Finding Structure: 

“seafood san francisco” Category: restaurant Location: San Francisco CLASSIFIERS (e.g., SVM) Finding Structure Can apply ML to extract structure from user context (query, session, …), content (web pages), and ads Alternative: We can elicit structure from users in a variety of ways

Better Search via IE (Information Extraction): 

Better Search via IE (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 Organization Bill Gates CEO Microsoft Bill Veghte VP Microsoft Richard Stallman Founder Free Soft.. PEOPLE Select Name From PEOPLE Where Organization = ‘Microsoft’ Bill Gates Bill Veghte (from Cohen’s IE tutorial, 2003)

Community Information Management: 

Community Information Management

Community Information Management (CIM): 

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?

The DBLife Portal: 

The DBLife Portal 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

DBLife: 

DBLife Integrated information about a (focused) real-world community Collaboratively built and maintained by the community Semantic web, bottom-up

1. Focused Data Retrieval: 

1. Focused Data Retrieval Identify relevant data sources Websites in each category identified by portal-builder Allow users to add sources Learn to identify/suggest sources Crawl to dowload and archive data once a day

Prototype System: DBLife: 

Prototype System: DBLife Integrate data of the DB research community 1164 data sources Crawled daily, 11000+ pages = 160+ MB / day

2. Semantic Data Enrichment: 

2. Semantic Data Enrichment Given a page, find mentions of entities: researchers, conferences, papers, talks, etc. A mention is a span of text referring to an entity Many sophisticated techniques are known Must exploit domain knowledge to do a better job We find about 114,400 mentions per day

Data Extraction: 

Data Extraction

3. Entity and Relationship Discovery: 

3. Entity and Relationship Discovery Given a set of mentions, infer the real-world entities Fundamental challenge: Determine if two mentions refer to same entity “John Smith” = “J. Smith”? “Dave Jones” = “David Jones”? Infer meta-data about entities and their relationships Researchers: Contact information, institution, research interests, year of graduation, publication list Publications: Topic, year, journal/conference, other publications citing it, authors Conferences: Location, date, acceptance rate, number of tracks, organizers, PC

Data Integration: 

Data Integration Raghu Ramakrishnan co-authors = A. Doan, Divesh Srivastava, ...

Entity Resolution (Mention Disambiguation / Matching): 

Entity Resolution (Mention Disambiguation / Matching) Text is inherently ambiguous; must disambiguate and merge extracted data … contact Ashish Gupta at UW-Madison … … A. K. Gupta, agupta@cs.wisc.edu ... (Ashish Gupta, UW-Madison) (A. K. Gupta, agupta@cs.wisc.edu) Same Gupta? (Ashish K. Gupta, UW-Madison, agupta@cs.wisc.edu)

Resulting ER Graph: 

Resulting ER Graph

Structure-Related Challenges: 

Structure-Related Challenges Extraction Domain-level vs. site-level Compositional, customizable approach to extraction planning Cannot afford to implement extraction afresh in each application! Maintenance of extracted information Managing information Extraction Mass Collaboration—community-based maintenance Exploitation Search/query over extracted structures Detect interesting events and changes

Complications in Extraction and Disambiguation : 

Complications in Extraction and Disambiguation

Overview: 

Overview Multi-step, user-guided workflows In practice, developed iteratively Each step must deal with uncertainty / errors of previous steps Integrating multiple data sources Extractors and workflows tuned for one source may not work well for another source Cannot tune extraction manually for a large number of data sources Incorporating background knowledge E.g., dictionaries, properties of data sources, such as reliability/structure/patterns of change Challenges in continuous extraction, i.e., monitoring Reconciling prior results, avoiding repeated work, tracking real-world changes by analyzing changes in extracted data

Workflows in Extraction Phase: 

Workflows in Extraction Phase A possible workflow I will be out Thursday, but back on Friday. Sarah can be reached at 202-466-9160. Thanks for your help. Christi 37007. Sarah’s number is 202-466-9160 Example: extract Person’s contact PhoneNumber person-name annotator phone-number annotator contact relationship annotator I will be out Thursday, but back on Friday. Sarah can be reached at 202-466-9160. Thanks for your help. Christi 37007. Hand-coded: If a person-name is followed by “can be reached at”, then followed by a phone-number  output a mention of the contact relationship

Workflows in Entity Resolution: 

Workflows in Entity Resolution Workflows also arise in the matching phase As an example, we will consider two different matching strategies used to resolve entities extracted from collections of user home pages and from the DBLP citation website The key idea in this example is that a more liberal matcher can be used in a simple setting (user home pages) and the extracted information can then guide a more conservative matcher in a more confusing setting (DBLP pages)

Example: Entity Resolution Workflow: 

Example: Entity Resolution Workflow L. Gravano, K. Ross. Text Databases. SIGMOD 03 L. Gravano, J. Sanz. Packet Routing. SPAA 91 Members L. Gravano K. Ross J. Zhou L. Gravano, J. Zhou. Text Retrieval. VLDB 04 C. Li. Machine Learning. AAAI 04 C. Li, A. Tung. Entity Matching. KDD 03 Luis Gravano, Kenneth Ross. Digital Libraries. SIGMOD 04 Luis Gravano, Jingren Zhou. Fuzzy Matching. VLDB 01 Luis Gravano, Jorge Sanz. Packet Routing. SPAA 91 Chen Li, Anthony Tung. Entity Matching. KDD 03 Chen Li, Chris Brown. Interfaces. HCI 99 d4: Chen Li’s Homepage d1: Gravano’s Homepage d2: Columbia DB Group Page d3: DBLP union s0 s1 union d3 d4 s0 s0 matcher: Two mentions match if they share the same name. s1 matcher: Two mentions match if they share the same name and at least one co-author name.

Intuition Behind This Workflow: 

Intuition Behind This Workflow union s0 s1 union d3 d4 s0 So when we finally match with tuples in DBLP, which is more ambiguous, we already have more evidence in the form of co-authors, and can use the more conservative matcher s1. Since homepages are often unambiguous, we first match home pages using the simple matcher s0. This allows us to collect co-authors for Luis Gravano and Chen Li.

Entity Resolution With Background Knowledge: 

Entity Resolution With Background Knowledge Database of previously resolved entities/links Some other kinds of background knowledge: “Trusted” sources (e.g., DBLP, DBworld) with known characteristics (e.g., format, update frequency) … contact Ashish Gupta at UW-Madison … A. K. Gupta agupta@cs.wisc.edu D. Koch koch@cs.uiuc.edu (Ashish Gupta, UW-Madison) (A. K. Gupta, agupta@cs.wisc.edu) Same Gupta? Entity/Link DB cs.wisc.edu UW-Madison cs.uiuc.edu U. of Illinois

Continuous Entity Resolution: 

Continuous Entity Resolution What if Entity/Link database is continuously updated to reflect changes in the real world? (E.g., Web crawls of user home pages) Can use the fact that few pages are new (or have changed) between updates. Challenges: How much belief in existing entities and links? Efficient organization and indexing Where there is no meaningful change, recognize this and minimize repeated work

Continuous ER and Event Detection: 

Continuous ER and Event Detection The real world might have changed! And we need to detect this by analyzing changes in extracted information Raghu Ramakrishnan University of Wisconsin SIGMOD-06 Affiliated-with Gives-tutorial

Complications in Understanding and Using Extracted Data: 

Complications in Understanding and Using Extracted Data

Overview: 

Overview Answering queries over extracted data, adjusting for extraction uncertainty and errors in a principled way Maintaining provenance of extracted data and generating understandable user-level explanations Mass Collaboration: Incorporating user feedback to refine extraction/disambiguation Want to correct specific mistake a user points out, and ensure that this is not “lost” in future passes of continuous monitoring scenarios Want to generalize source of mistake and catch other similar errors (e.g., if Amer-Yahia pointed out error in extracted version of last name, and we recognize it is because of incorrect handling of hyphenation, we want to automatically apply the fix to all hyphenated last names)

Real-life IE: What Makes Extracted Information Hard to Use/Understand: 

Real-life IE: What Makes Extracted Information Hard to Use/Understand The extraction process is riddled with errors How should these errors be represented? Individual annotators are black-boxes with an internal probability model and typically output only the probabilities. While composing annotators how should their combined uncertainty be modeled? Lots of work Fuhr-Rollecke; Imielinski-Lipski; ProbView; Halpern; … Recent: See March 2006 Data Engineering bulletin for special issue on probabilistic data management (includes Green-Tannen survey) Tutorials: Dalvi-Suciu Sigmod 05, Halpern PODS 06

Real-life IE: What Makes Extracted Information Hard to Use/Understand: 

Real-life IE: What Makes Extracted Information Hard to Use/Understand Users want to “drill down” on extracted data We need to be able to explain the basis for an extracted piece of information when users “drill down”. Many proof-tree based explanation systems built in deductive DB / LP /AI communities (Coral, LDL, EKS-V1, XSB, McGuinness, …) Studied in context of provenance of integrated data (Buneman et al.; Stanford warehouse lineage, and more recently Trio) Concisely explaining complex extractions (e.g., using statistical models, workflows, and reflecting uncertainty) is hard And especially useful because users are likely to drill down when they are surprised or confused by extracted data (e.g., due to errors, uncertainty).

Provenance, Explanations: 

Provenance, Explanations A. Gupta, D. Smith, Text mining, SIGMOD-06 System extracted “Gupta, D” as a person name System extracted “Gupta, D” using these 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 builder can potentially improve extraction accuracy. One way to do that: (S1) Detect a list of items (S2) If A straddles two items in a list  A is not a person name Incorrect. But why?

Provenance and Collaboration: 

Provenance and Collaboration Provenance/lineage/explanation becomes even more important if 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

Mass Collaboration: 

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

Mass Collaboration: A Simplified Example: 

Mass Collaboration: A Simplified Example Not David! Picture is removed if enough users vote “no”.

Mass Collaboration Meets Spam: 

Mass Collaboration Meets Spam Jeffrey F. Naughton swears that this is David J. DeWitt

Incorporating Feedback: 

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. Discover corrective rules such as S1—S2 Find and fix other incorrect applications of R1 and R2 A general framework for incorporating feedback? User says this is wrong

Collaborative Editing: 

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

Web as Delivery ChannelEmail … and More: 

Web as Delivery Channel Email … and More

A Yahoo! Mail Example: 

A Yahoo! Mail Example No. 1 web mail service in the world Based on ComScore & Media Metrix More than 227 million global users Billions of inbound messages per day Petabytes of data Search is a key for future growth Basic search across header/body/attachments Global support (21 languages) (Courtesy: Raymie Stata)

Search Views: 

Search Views For Presentation Only – Final UI TBD Shows all Photos and Attachments in Mailbox User can change “View” of current results set when searching 1 2 (Courtesy: Raymie Stata)

Search Views: Photo View: 

Search Views: Photo View For Presentation Only – Final UI TBD Photo View turns the user’s mailbox into a Photo album Clicking photo thumbnails takes user to high resolution photo Hovering over subject provides additional information: filename, sender, date, etc.) Ability to quickly save one or multiple photos to the desktop Refinement Options still apply to Photo View 1 2 3 4 5 (Courtesy: Raymie Stata)

The Net: 

The Net The Web is scientifically young It is intellectually diverse The social element The technology The science must capture economic, legal and sociological reality And the Web is going well beyond search … Delivery channel for a broad class of apps We’re on the cusp of a new generation of Web/DB technology … exciting times!

Thank you.: 

Thank you. Questions? ramakris@yahoo-inc.com http://research.yahoo.com