implications of web 2.0 on information research

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Implications of Web 2.0 on Information Research

Wen-Lian HsuAcademia Sinica, Taiwan

中央研究院資訊所許聞廉[email protected]

2/62Workshop on Web 2.0 Technology and Applications, Taipei, 2007 Dec. 19th

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Outline

What is Web 2.0? Web 2.0 and Research

Human-based Computation Folksonomy (Social Tagging) Academic Data Analysis GIO-Info

Conclusion


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What is Web 2.0?

Web 2.0 Conference (October 2004) Tim O'Reilly

The Web As a Platform Harnessing Collective Intelligence Data is the Next Intel Inside End of the Software Release Cycle Lightweight Programming Models Software Above the Level of a Single Device Rich User Experiences


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What is Web 2.0?


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What is Web 2.0? Web 2.0 is the combination of “tools and technologie

s”, “business strategies” and social/cultural trends, which drive the individual creation and sharing of content on the Internet.

ED YOURDON Web 2.0 opens up the Long Tail, making it increasingly

cost-effective to service the interests of large numbers of relatively small groups of individuals, and to enable them to benefit from key pieces of the platform while fulfilling their own needs.

PAUL MILLER


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What is Web 2.0? "Web 2.0" seems to be like Pink Floyd lyrics: It can mean

different things to different people, depending on your state of mind.

KEVIN MANEY “Web 2.0 definitely is a buzzword, and it’s overused. But the

movement is only starting. That movement is about leveraging the power of people”

CHAD HURLEY “What we’re seeing is a return to the roots of the web.”

CATARINA FAKE Web 2.0 “is enabling a fundamental shift in power that

really is giving power to the consumer”MARK PARKER

“It’s a way to collaborate with your customers, to allow them to co-create with you”.


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Web 2.0 Sites

Source: http://www.go2web20.net/


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Key Web 2.0 services/applications

Blogs Wikis Tagging and social bookmarking Multimedia sharing RSS and syndication Podcasting P2P


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Sharing and Collecting Resources

Content: Blogger, Wikipeida, Flickr, Youtube

Opinion: Digg, Hemidemi, 推推王 Bandwidth: Emule, BT, Skype, Joost, PPStream Computing: SETI, Grid Innovation: Second Life Money: Din.Ben.Don 訂便當團購網 , 共

乘網


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Social Bookmarking

Source: http://funp.com/push/


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Soruce: http://www.hemidemi.com/

Source: http://digg.com/


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Blog

ContentContent

comments

comments

adsenseadsenseSocial bookmark

Social bookmark

Source: http://carol.bluecircus.net/


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Skype

Source: S.A Baset, H. Schulzrinne (September 14, 2004). An Analysis of the Skype Peer-to-Peer Internet Telephony Protocol. Technical Report. Columbia University.


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Wikipedia


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Second Life


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Symbiosis ( 共生機制 ) is the Key

Blog Social bookmark


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The Web Changes in Several Dimensions

Dynamics Heterogeneity Collaboration Composition Socialization


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Current Research Activities Information Retrieval on Blogs

NTCIR-7 CLIRB (Cross-Lingual Information Retrieval for Blog) Question Answering on Blogs

TREC 2007 QA Track Question Answering on Wikipedia

QA@CLEF 2007 CLEF 2006 WiQA

given a Wikipedia page, locate information snippets in Wikipedia PASCAL Ontology Learning Challenge

Ontology construction Ontology extension Ontology population Concept naming

LinkKDD2006, Textlink2007, MRDM2007


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Web 2.0 and Research

Human-based Computation Folksonomy (Social Tagging) Academic Data Analysis GIO-Info

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Human-based Computation


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Human-based Computation

Social Search wayfinding tools informed by human judgment

CAPTCHA reversed Turing test (Turing test 是由人來詢問系統，這裡

則是由系統來詢問使用者） Interactive Genetic Algorithm (IGA)

a genetic algorithm informed by human judgment. 由人工提供 fitness function 結果

例子：描繪罪犯畫像，系統以 GA 方式產生嫌犯畫像，目擊者負責評分看那個比較像，不斷重複過程直到接近罪犯樣子為止


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CAPTCHA Completely Automated Public Turing test to tell Computers and Humans Apart

A CAPTCHA is a type of challenge-response test used in computing to determine whether the user is human. wikipedia

SOURCE: http://recaptcha.net/


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CAPTCHA

blog

CAPTCHA

blog

CAPTCHA

blog

CAPTCHA

Unrecognizedtext

Recognizedtext


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The ESP Game a two-player game The goal is to guess what y

our partner is typing on each image.

Once you both type the same word(s), you get scores.

Source: http://www.espgame.org/

ESPESP


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The Phetch GamePlay as a describer


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The Phetch GamePlay as a seeker

PhetchPhetch


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How about a game for describing idioms?

罄竹難書如沐春風

高抬貴手不動如山壞事做太多罄竹難書 : 壞事做太多虎頭蛇尾 : 做事沒有毅力………

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Folksonomy (Social Tagging)


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Folksonomy (Social Tagging) Also known as social tagging, collaborative

tagging, social classification, social indexing

Folksonomy is the practice and method of collaboratively creating and managing tags to annotate and categorize content.

Wikipedia


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del.icio.usTags: Descriptive words applied by users to links. Tags are searchable

My Tags: Words I’ve used to describe links in a way that makes sense to me


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Tag Cloud


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Semantic Web

Source: Tim Berners-Lee


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Using Folksonomy to Help Semantic Web Top-down Semantic Annotation

Approach Define an ontology first Use the ontology to add semantic markups to web

resources. The semantics is provided by the ontology which

is shared among different web agents and applications.

Problem Negotiation Evolution (hard to maintain) High Barrier (background)

Source: Xian Wu, Lei Zhang, Yong Yu. “Exploring Social Annotations for the Semantic Web”


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Using Folksonomy to Help Semantic Web Bottom-up approach with social tagging

Advantage No common ontology or dictionary are needed Easy to access Sensitive to information drift

Disadvantage Ambiguity Problem: For example, “XP” can refer to

either “Extreme Programming” or “Windows XP”. Group Synonymy Problem: two seemingly different

annotations may bear the same meaning.

Source: Xian Wu, Lei Zhang, Yong Yu. “Exploring Social Annotations for the Semantic Web”


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Or Folksonomy is the Solution? Ontology is Overrated

Classification of the web has failed Classification itself is filled with bias and

error Tagging is the solutionSource: http://www.shirky.com/writings/ontology_overrated.html

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Academic Data Analysis


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Academic Data Analysis

CiteSeer

Google Scholar

e-Lib, Lib 2.0 concept adding

into application, so search platform

provide open API for collecting more

data

Users participate and

interact with data and people

Add My Library, TagEx. Citeulike, BibSonomy

Add Comments, Rating, Recommendation

Ex. Techlens

Domain Focus GroupsEx. Botanicus

Windows Live Academic Search

PudMed

Arxiv

Citation indexPapers , journal/conference, authors


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An Example

Let’s use an example of TechLen to imagine what research on IR /NLP can do.

Authors Readers

Papers


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The Terminology Alfred V AhoEntities

Alfred Aho AV AhoAho, A. V.References

LinksAlfred Aho, John Hopcroft, Jeffrey Ullman

AV Aho, BW Kernighan, PJ Weinberger

Entity Groups G1(Programming Languages)

G2(Databases)

G3(Algorithms)


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Imagine how we can make use of them

Papers

Authors

Readers

Comments

Rating

Reference Extraction

Entity Resolution


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New Research Topics From those changes, key emerging challenge for “Data

Mining” is tackling the problem of dealing with richly structured, finding patterns behind heterogeneous datasets, …, etc.

Several researches focus on those problem like (Social) Network Analysis Link Mining PASCAL Ontology Learning Challenge …


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Society

Nodes: individuals (Authors, Readers)

Links: social relationship (family/work/friendship/belong to,…etc.)

S. Milgram (1967)

Social networks: Many individuals with diverse social interactions between them.

John Guare

Six Degrees of Separation,

Science

source: www.cs.uiuc.edu/~hanj


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Communication networks

The Earth is developing an electronic nervous system, a network with diverse nodes and links are

-computers

-routers

-satellites

-Papers

-User IP

-Comments

-Response

-…

-phone lines

-TV cables

-EM waves

- Relations between artifacts

Communication networks: Many non-identical components with diverse connections between them.

source: www.cs.uiuc.edu/~hanj

Artifacts in Techlens


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Link-based Object Ranking Perhaps the most well known link mining task is that of link-

based object ranking (LBR), which is a primary focus of the link analysis community. The objective of LBR is to exploit the link structure of a graph to order or prioritize the set of objects within the graph.

Example PageRank What paper is most important in this area? What journal/conference is most important in this area? What topic is important in this area?


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Link-based Object Classification/ Link-based Classification (LBC)

Predicting the category of an object based on its attributes and its links and attributes of linked objects

Web: Predict the category of a web page, based on words that occur on the page, links between pages, anchor text, html tags, etc.

Citation: Predict the topic of a paper, based on word occurrence, citations, co-citations

Epidemic : Predict disease type based on characteristics of the people; predict person’s age based on ages of people they have been in contact with and disease type


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Group Detection Cluster the nodes in the graph into

groups that share common characteristics. That is, Predicting when a set of entities belong to the same group based on clustering both object attribute values and link structure.

Web: identifying communities Citation: identifying research communities


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Entity Resolution

Predicting when two objects are the same, based on their attributes and their links Web: predict when two sites are mirrors of

each other Citation: predicting when two citations are

referring to the same paper Epidemics: predicting when two disease

strains are the same Biology: learning when two names refer to

the same protein


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Link Prediction Predict whether a link exists between

two entities, based on attributes and other observed links Web: predict if there will be a link between

two pages Citation: predicting if a paper will cite

another paper, or predict the venue type of a publication (conference, journal, workshop) based on properties of the paper

Epidemics: predicting who a patient’s contacts are ( 在流行病學上需要去找出病源( 灶 )/ 傳染源 )


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Other Possible Research Directions

Expert Finding like giving a suggestion of Paper Reviewer,

Conference committee member Ecological Evolution of Some Research

Like one topic with different solution in a time period

A domain’s topic distribution

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GEO-Info 地理資訊


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GEO-Info

Google Earth/Map

GISLimited user, limited usage

Open for every one

Google Earth Community

Google Earth Blog

Ogle Earth ….

User Participate

GML

Photo-sharing User Annotation


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Some Research Topics Until now, a lot of information can be combined int

o google earth/map by KML. Hence such information can be integrated by geoc

oding, some models become very interesting, such as

Photo Annotation, Sharing, and Search Live information Planning 3D, Flights Animation Travel experience, comments Transportation information, survival information Climate Change


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Some Information bundled with Google Earth/Map ( 中山公園 )

Integrated with Youtube (video & tags)

Photo sharing, (photo & Tags)


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Some Application Integrate more Information on Map

Personal Life Information Integration

GeoDDupe: A Novel Interface for Interactive Entity Resolution in Geospatial Data


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Photo link with Map

Source: http://www.panoramio.com


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Image-based Rendering (IBR)

IBR relies on a set of two-dimensional images of a scene to generate a three-dimensional model and then render some novel views of this scene.

Web 2.0 enables sharing of photographs on a truly massive scale


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Microsoft PhotoSynth From SIFT to PhotoSynth


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Conclusion Research results can be easily integrated on the W

eb 2.0 platform make restricted-domain research more useful for t

he public (such as image-based rendering) Software agent

Benefit human-based computation Certain research topics will be easier to tackle, suc

h as personalization in virtual world (more data available)

Data becomes more task oriented (e.g. Wikipedia) More versatile data networks available


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Acknowledgement

謝謝盧文祥教授及鄭卜壬教授的邀請

I would also like to thank two Ph. D. students of mine who help organize the slides: 李政緯，呂俊宏

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Thank You

implications of web 2.0 on information research

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