Web 2.0 的技術與應用

Web 2.0 的技術與應用曾憲雄教授交通大學資訊工程學系2009/4/14

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Human Intelligence vs. Machine Intelligence

在 1997 年許峰雄博士所設計的 IBM Deep Blue 打敗世界西洋棋王 Kasparov 。電腦是否已經比人腦聰明？

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Web 2.0• Web 2.0 的世界 :

– 網路成為新平臺– 內容因使用者的參與（ Participation ）而產生

• 產生個人化（ Personalization ）內容• 藉由人與人（ P2P ）的分享（ Share ）。

• Web 2.0 概念 :– Tim O‘Reilly 與 MediaLive 國際研討會議題開始– 一個架構在知識上的環境，– 人與人之間互動而產生出的內容，– 經由在服務導向的架構中的程式，在這個環境被發佈，管理和使用

http://www.oreillynet.com/pub/a/oreilly/tim/news/2005/09/30/what-is-web-20.html?page=3

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Web 1.0 vs. Web 2.0• DoubleClick• Ofoto• Akamai• mp3.com• 大英百科 (Britannica Online)• 個人網站 (personal websites)• evite• 網域名稱預測(domain name speculation• 頁面瀏覽數 (page views)• 螢幕擷取 (screen scraping)• 發佈 (publishing)• 內容管理系統(content management systems)• 目錄 ( 分類 )(directories (taxonomy))• 黏性 (stickiness)

•Google AdSense •Flickr( 相片分享社群 )•BitTorrent(P2P-BT 下載 )•Napster(P2P 音樂下載 )•維基百科 (Wikipedia)•部落格 (blogging)•upcoming.org and EVDB•搜尋引擎優化 (search engine optimization)•每次點擊成本 (cost per click)•網路服務 (web services)•參與 (participation)•維基式管理(wikis)•標籤 ( 分眾分類 )(tagging ("folksonomy"))•聚合 (syndication)

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Turing TestTuring Test 是 Turing 提出的一個關於機器人的著名判斷原則。此原則說：如果一個人使用任意一串問題去詢問兩個他不能看見的對象：一個是正常思維的人；一個是機器，如果經過若干詢問以後他不能得出實質的區別，則他就可以認為該機器業已具備了人的「智能」（ Intelligence ）。

( 取材自維基百科 )

阿蘭 · 麥席森 · 圖靈（ Alan Mathison Turing, 1912 - 1954 ），英國數學家、邏輯學家，他被視為電腦之父。

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長尾效應

( 網路如同平台 )

( 自行控制資料 )

( 服務非套裝軟體 )( 參與 )

( 集體智慧 )( 軟體超越單一裝置 )

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Web 2.0 原則 • 「網路應該作為平台來使用」 (The Web As Platform) • 從商業的角度看， Web 2.0 讓泡沫的 dot com 起死回生 ?

– Yahoo, e-Bay, Amazon 都不是新公司。– Google 不是第一個免費的搜索引擎。 (AltaVista & Overtur

e?)– 搜尋結果的排序加入商業考量，正在降低公信力。

• 部落格 (Blog) 的崛起 : 個人網頁每天記載的日記形式 – 依時間前後排列方式組成，利用不同的遞送方式來散佈個人的想法與觀點，並且組成價值鏈

• Web 1.0: – Netscape 跟微軟抗衡時所提出來的口號 :– 1.0 VS 2.0:Netscape 與 . Google

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Web 2.0 原則 • 引領群體智慧 : 超連結是網路的基礎。

– Google 在搜尋領域中突圍而出 (PageRank )– eBay 的產品則是全體用戶集體活動的龐大創造物 :

• 賣主提供商品，買家尋找商品 – 維基百科（ Wikipedia ） :

• 眾多網路用戶所提供的知識為基礎，任何人皆可編輯修改而成的線上百科全書。 – del.icio.us 及 Flickr (bookmarks):

• 「大眾分類」（ Folksonomy ）的概念 – 病毒行銷」（ Viral marketing ） :

• 採用直接從一個用戶到另一個用戶的方式來傳播訊息

9http://www.slideshare.net/heyjudeonline/creative-web-20-learning

RSS (Really Simple Syndication) 技術 : 藉由定期的主動訊息接受，可以得知訂閱的網頁內容有所更動

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Data is the Next Intel Inside

•重要的網路應用系統，都有一個專屬資料庫 :– Google & Yahoo : 網路搜尋資料庫（ web crawl ）– Amazon: 產品資料庫– eBay: 產品與賣家資料庫– Google Map: 地圖資料庫– Napster: 分散式歌曲資料庫裏– Myspace 、 Facebook: 社群資料庫– Youtube 、無名 : 影音相片資料庫， YouTube 的主要內容貢獻者要求分廣告利益。

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NEWS:微軟擴大廣告聯盟，砸下 2.4億美元入股 Facebook • Facebook: http://www.facebook.com/

– 社交網站，– 擁有 150 億美元的身價，– (宣稱 ) 使用者直逼 5,000 萬人 – 超越MySpace: www.myspace.com

WHY??? Source: http://taiwan.cnet.com/news/

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廣告主的天堂 • 絕佳廣告平台 :

– 豐富的個人資料與附加資訊 :• 誰 (Who) 與他們往來，他們在做什麼事 (What)等等。

個人偏好 & Social Network 。• [ 聚焦廣告 ](targeted advertising) :

– 派送鎖定目標的個人化廣告。 – 成功案例 : Google 、 Amazon 。

• 範例 :– 已訂婚 Users:

婚紗業者 &蜜月旅遊方案 &禮餅 & etc. 。– 地區 & 年齡 & 音樂喜愛 & [七月半 ]歌手樂團 :

音樂會宣傳– 愛吃披薩 :

• 顯示出住家附近 [ 打不樂 ] 披薩門市的電話號碼。 Source: http://taiwan.cnet.com/news/

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Web 2.0 原則 : Service

• 網路時代的軟體最重要的特徵是服務 (Service) ，而非產品 (Product):– Gmail, Google Maps, Flickr, del.icio.us, etc.

• 輕巧的程式設計模式 :– 組裝式創新 (Mashup):

• 整合網路上多個資料來源或功能，以創造新服務的網路應用程式 – Google Map 、 YouTube 、 Slideshare

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信義房屋

地圖日記

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Web 2.0 企業的核心競爭力• 提供服務，而不是套裝軟體，能以符合成本效益的方式擴充 :

– Google Mail, Map.• 控制獨特的、難以再製的資料來源，隨著越多人使用而累積越豐富的資料 :

– Wikipedia, Facebook, 無名• 使用者為共同的開發者，善用眾人的集體智慧與自助服務效能 :

– Wikipedia, Myspace, Youtube• 不再侷限於個人電腦的平台之上 :

– iPod / iTunes , Podcasting• 輕巧的使用者介面、開發模式、及商業模式 :

– Google API, Mashup

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1.Proposing a knowledge-based rapid prototyping approach to TM

design for e-Learning grids• Traditional teaching-material design process: ADDIE

model– Analysis, Design, Develop, Implement, and Evaluate

• Disadvantage– Time-consuming, Expensive , Redundant effort

• Alternative : Automated authoring and Reusing existing TMs

• Challenges/Difficulties– Requirement elicitation– Finding useful TMs from existing ones

• Minimize Development time, Development cost

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Problem • Teaching-Material Designing Problem

– For a query given by a teaching-material editor, design a teaching-material, where

• the designer can interactively consult the editor to elicit the meaning of the query;

• the existing materials in m LORs can be reused.– The objective is to minimize the total developm

ent time.

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Idea

• Idea: a rapid prototyping approach to designing TMs– Reuse

• Using expertise to search useful TMs– Automated

• KA tools are available to speed the process.• Automatically merge algorithm

– Collaborative authoring• Using a Wiki-based authoring environment

• Design and implementation of a searching expert system to find reusable TMs

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Approach System overview: WARP (Wiki-based Authoring by Rapid

Prototyping)

• Phase 1: to verify user’s query• Phase 2: to expand the query• Phase 3: to search existing relevant TMs• Phase 4: to generate the 1st version• Phase 5: to revise by Wiki-based authoring

IntelligentQuery

Expansion

AutomaticDraft-

Generation

Wiki-basedRevision

Editor Users

FinalVersion

First DraftInteractive

RequirementVerification

QueryVerifiedQuery

ExistingTM

SearchingExisting

TMs

ExpandedQuery

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Implementation (1)A Wiki-based authoring environment on grids

Web -based Interface

Wiki-HTML Conversion

LOR 1

Wiki-enabled Authoring Server

Edit

SCORM-HTML Conversion

LOR nLOR 2

Grid Middleware

Edit Edit

Wiki-Text

HTML

SCORM

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Implementation (2)Grid Configuration

• The grid test-bed is composed of 4 domains.– Implemented by Globus Toolkit 4.0

End User

Internet

Li-Zen highschool

Tung-Hai University

Hsiuping Institute of Technology

1Gbps

30Mbps

1Gbps

10/100Mbps LAN

10/100Mbps LAN

Dali highschool

20Mbps10/100Mbps LAN

Alpha

Portal Server

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http://www.slideshare.net/heyjudeonline/creative-web-20-learning

Web 2.0:

UGC+SNS

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Social Networking Service (SNS)

http://staffdev.henrico.k12.va.us/parents/socnetwork.htm

•為一群擁有相同興趣與活動的網友，建立並鞏固網絡上的社交網路 •提供使用者進行互動 :

•聊天、寄信、影音、分享檔案、寫部落格、參加討論群組等等 :•EX: Facebook

http://zh.wikipedia.org

http://staffdev.henrico.k12.va.us/parents/socnetwork.htm

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User-Generated Content (UGC)

http://www.linuxelectrons.com/news/general/user-generated-web-content-will-grow-rapidly-through-2010



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長尾（ The Long Tail ） :• Chris Anderson (2004) 發表在連線雜誌 :

– 亞馬遜和 Netflix Real.com Rhapsody 的商業和經濟模式 :• 一半左右的銷售來自於比較熱門的商品，• 另一半卻來自相對不那麼熱門的商品人口（

popularity ）

產品（ products ） Long Tail

20%

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長尾（ The Long Tail ） :• 20-80法則 :

– 企業界 80% 的業績來自 20% 的產品 • 長尾 (The Long Tail):

– Web2.0興起後，改變 20-80法則的新理論 – Internet讓 99% 的產品都有機會銷售 :

• 長尾特性商品將具有增長企業營利空間的價值。• 長尾商品總值甚至可與暢銷商品抗衡。

– 「長尾」的總合也未必超越幾個暢銷品，更何況不只一家 Web 2.0 在分一條長尾。

•

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2. Folksonomy-based Indexing for Location-aware Retrieval of Learning Conte

nts• An example scenario of location-aware u-learning

– The “identification of plants” unit of the Nature Science course in an elementary school

– Place: campusSystem: Can you identify the type of the plant in front of you?Student: No.System: What is the color of the flower?Student: red.…

– The system is aware of the location of the student, and the nearby plants, by sensor technologies and maps.

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Introduction (1)

• Content Retrieval (CR) is an important task in learning activities.

• Classification of CR– Personalization

• adaptive to subjective factors• e.g.: user profile, preference, etc.• The same query, different persons ->

different results– Context-awareness

• adaptive to objective factors• e.g.: time, place, device, activity,

peers, etc.• The same query, different contexts ->

different results

Content Retrieval

StaticContent Retrieval

Dynamic (Adaptive)Content Retrieval

PersonalizedContent Retrieval

Context-awareContent Retrieval

A kind of A kind of

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Introduction (2)

• Location-aware content retrieval– Advantages:

• Adaptive• Fast

– Difficulties: Index creation• Maintenance: lack of flexibility in manually constructed ontology• Usability: corresponding to the content collection

– WordNet• Characteristics of learning content are not considered.

– Structural information– metadata

QuerySearchEngine

User

Location-based Index

Retriever

Repository

User

Interface

Results

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Preliminaries (1)• Folksonomy: One of Web 2.0 features

– Collaborative categorization using freely chosen keywords– To allow users to describe a set of shared items

• Bookmarks: del.icio.us

• Photos: Flickr• Scientific publications: CiteULike

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Preliminaries (2)• Example: http://del.icio.us

– To store your bookmarks online– To use tags to organize and remember your bookm

arks– To see the interesting links that your friends and oth

er people bookmark, and share links with them in return.

– You can search del.icio.us to discover cool and useful bookmarks that everyone else has saved

• Limitations– Informal:

• the set of keywords is not fixed• Semantic ambiguity

– Single-layer structure – Limited sharing scope: not crossing the boundaries

of a single website

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Folksonomy

• Definition– Tag: T

• A set of tags• A tag is a user-defined keyword

– Item: I• A set of SCORM-compliant teaching materials

– Relation: R• A relation on T× I

– Folksonomy• F = (T, I, R)

• Example: The folksonomy of User A– T = {t1, t2}– I = {i1, i2, i3, i4}– R = {(t1, i1), (t1, t2), (t1, i3), (t2, i1), (t2, i4)}

Tags

Items

UsersUser A User B

t1 t2 t3 t4 t5

i1 i2 i3 i4 i5 i6 i7 i8 i9

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Problem

• Definition: Index (a rooted tree )– The nodes represent concepts in the domain, and the ed

ges represent relations between nodes. – A node is a specialization of its parent node. – Each node is associated with a feature vector, which char

acterizes the semantic meaning of this concept. • Folksonomy-based Index Creation Problem

– propose a folksonomy-based method to automatically construct location-based indices. The built ontology can be applied to organization and retrieval of learning contents.

Given a collection of learning contents and corresponding folksonomies, construct an Index for the collection.

– Precision and recall of retrieval using the built index

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網路世代 (N-gen)

• 吸收資訊快，資訊多元 .

• 反應速度快 .

• 需求為主 (on-demand)地使用媒體 :– 喜歡在持續與朋友通訊 ( MSN 、 QQ 、 Skype)

• 喜歡自行創作與分享

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e-learning 2.0 精神 :自主學習、參與、分享、討論Teacher-centered Lear

ning學生選擇少學生被動教師掌控權力

http://www.aishe.org/readings/2005-1/oneill-mcmahon-Tues_19th_Oct_SCL.html#x1-30011

Student-centered Learning

學生選擇多學生主動

學生掌控權力

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eLearning 2.0 模式• 基於 Web 2.0 與 elearning 新趨勢的新模式 :

– 學生製作內容 (students create content) – 協同合作 (collaboration)

• 利用 blogs, Wikis, discussions, RSS, etc.– 組成學習網路 (Learning Network).

– 利用多元組成的內容來得到學習經驗 (learning experiences).

– 利用多元工具 :• online references, courseware, • knowledge management, collaboration 與 search.

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http://www.slideshare.net/akarrer/elearning-20-karrer-astd-oc-2007

Small-Medium Enterprise

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http://www.slideshare.net/akarrer/elearning-20-karrer-astd-oc-2007

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Learning Community

• 知識的建構 (Knowledge construction):– 最好經由協同合作 (Collaboration) 來完成 .

• 學生於同學間經由供給與獲得 (give-and-take) 來進行學習 : – 當學生寫下貢獻 (contributions) 於討論時 :

• 學生將學習到他們想說之事• 他們所得之回應將可促進學習

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學習與遊戲模型對應

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網路遊戲沉迷原因• 美國羅切斯特大學研究成果 :

– 因為網路遊戲滿足了人們的心理需要 – 心理上產生成就感和自我支配感 :

• 只有讓遊戲者與其他參與者互動的遊戲才能使遊戲者更加投入，更加樂此不疲，也更容易上癮 • 讓遊戲者自行做主，展示自己的能力，還可讓遊戲者支配他人或得到他人的呼應和支援。• 如果網路遊戲只是單純地向人們提供“樂趣”的話，就不可能讓人長久著迷。

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Game based learning community• Web 1.0:

– 存取民主化 (Democratization of Access)• Web 2.0 :

– 參與與協同合作民主化 (Democratization of Participation and Collaboration)

• Web 3D :– 虛擬學習與共創– (Enablement of true generative learning and co-cre

ation distributed virtually across the world).

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非 Learning 社群

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非 Learning 社群

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Second Life • 誕生於 2003 年 :

– 為 RealNetwork 前任技術長 Philip Rosedale 於舊金山成立林頓軟體公司（ Linden Lab ）• 社交導向的線上遊戲 :

– 大規模多人線上角色扮演遊戲（ MMORG ）。– 遊戲無技能點數、經驗值、等級、打怪或轉職，沒任務、解謎或組隊。

• 實際經濟 :– 每 1,000 個林頓元約可兌換 3.3美元。– 月費為 9.95美元，會員約 90萬名，每月會費收入高達 895.5萬美元，約當於新台幣 2.95億元。

• 學習應用 :– 某些大學教授會到 Second Life裡開堂授課– 學生在裡面做實驗，也有醫生在裡面開起了診所，提供醫療諮詢服務– 公益人士成立支持血癌患者的團體– Toyota及 Sun Microsystems進駐 : 增加其產品的知名度。

www.secondlife.com/

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CyberOne Classroom in Second Life

Source: http://blogs.law.harvard.edu/vvvv/files/2006/09/CyberOne_2006-09-21.png

http://blogs.law.harvard.edu/vvvv/files/2006/09/CyberOne_2006-09-21.png

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Virtual Presence:Second Life Library 2.0

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Virtual Learning:EduNation-Second Life

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Social Network Service

• Social Network Service (SNS)– Myspace, Facebook, Friendster, etc.

• SNS: provide low cost social communication medium with other people

• Recommend possible new friends– From your friend’s friend– Interest in the same topics

• Idea: human resource hunting– e.g. Expert finding for problem solving

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3. Trustworthy experts finding service to improve the social network for problem solving

• Recommend experts to students’ for programming inquiry learning

• Technical Issues– Trustworthiness– Availability– Domain expertise

• Obtain the preference of experts from the behaviors on the discussion forum

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Model the Topic interest

• Ontology: hierarchical structure to represent the topic

Programming Capability Ontology

C/C++VisualC++

Part of Part of

VisualC#

Part of

What's wrong

Part of

What's different

Part ofPart of Part of

What's the mearning

What's wrong

Part of

Doc1

Instance of

Docm

Instance of

Document Layer

…How to do

Object Class

A kind of A kind of

Doc2

Instance of

libC++API

A kind of A kind of

……Doc5

Instance of

string GCC

A kind of A kind of

Doc4

Instance of

Docm-1

Instance of

Issue Layer

Doc3

Instance of

Doc6

Instance of

A Kind OfA kind of

Part ofPart of

Instance ofInstance of

PHPJAVA ...

Part of Part ofPart of

Category Layer

Topic Layer

57

Trustworthy Expert Finding Service

Question identification

Questioner

Expert C

Expert A

Expert B

Expert finding service

Notice the experts & organize the social

network

Question

Expert A

Expert B

Expert C

Questioner

Ontology

Search profile

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Criteria• Domain expertise

– Experts with similar topic interest are obtained from experts’ posting documents on the forum.

• Trustworthiness– The trustworthiness values are computed by the expe

rts’ average reputation degrees given by other community members.

• Availability – The availability is heuristically obtained by the weighte

d average of experts’ presence frequency online.

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Contribution

• Inquiry-based learning is applied for students’ programming problem solving on Web forum.

• The trustworthy experts finding service has been proposed to improve the social network for problem solving.

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Collective Intelligence

• Collective Intelligence: group intelligence that emerges from collaboration and competition of many individuals– e.g. Wikipedia

• Collaborative knowledge construction– Knowledge integration– Knowledge fusion– Folksonomy-based approach

61

4. Ontology construction from folksonomies

D1 D2 D3 D4 D5Contents

New Assertions

Existing Ontology

Community members

1

Tags

Upload Contents & Edit Folksonomy

Upload

Collaborative Editing

LO

RS

62

Ontology Inconsistency, redundancy issues

AAAAA AAAADAAAAC

AA

AAB

AAAA

AAA

AKO

AKO

AACAPO

3

AACA

AKO

AKO

2

AKO

AADPR

5

A

APO

AKO

AKO

AKO AKO

AAAAB

AKO

AKO

PR

PR AADA

AKO

Root

1

4

Hierarchical Cycle

Hierarchical Redundant

Exclusive Violation

Unreachable

Prerequisite Cycle

63

Goal: Social Agreement Ontology

• Ontology Crystallization Problem : ”how do we construct the ontology via community to achieve social agreement” .

Communities Communities

Crystallizer (Social Interaction

Consensus Evaluation)

Social Agreement Ontology

Knowledge Contribution

Refine and Converge Knowledge

Social Agreement Ontology

64

Iterative, Collaborative Ontology Construction

• Iterative convergence approach can reduce the updating effort – Wiki-like ontology editor to track the revision log– Delphi-like consensus building approach can resolve th

e inconsistency by predefined questionnaire template

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Questionnaire item templates to assist the inconsistency resolution

Item Type Questionnaire Item TemplatesT1: Likert five-point s

calesDo you agree or disagree with this relationship? Concept (Ci)

Relation (rm) Concept (Cj)(1)Strongly Agree (2)Agree (3)Not Agree and Not Disagree (4)Disa

gree (5)Strongly Disagree

T2: True/False Do you agree or disagree with this relationship? Concept (Ci) Relation (rm) Concept (Cj)

(1)Agree (2)Disagree

T3: Multiple concepts selection

What is your opinion about which following Concept (CX) is the most suitable for Concept (CX/Ci) Relation (rm) Concept (Cj/Cx) ?

(1)Concept1 (2)Concept2 (3)…(n) Conceptn (n+1) Not Above All (, where n 5)≦

T4: Multiple relation selection

What is your opinion about which following Relation (rm) that is the most suitable to describe the relationship between Concept (Ci) and Concept (Cj) ?

(1)Relation1 (2)Relation2 (3)…(n) Relation n (n+1) Not Above All (, where n 5)≦

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System implementation

Wiki-like Ontology Editor

Questionnaire

67

Usability of the ICOC system

Questionnaire Item Mean SDQ1. The ontology construction using ICOC system has higher efficiency.

4.00 0.79

Q2. The ontology construction using ICOC system has higher flexibility.

2.65 0.88

Q3. The conflict resolution of ICOC system is helpful during the ontology construction.

4.30 0.66

Q4. The ontology construction using ICOC system has higher reliability.

3.60 0.68

Q5. The ontology construction using the ICOC system has better ontology quality.

4.10 1.02

Likert’s five point scale: from 5 (strongly agree) to 1 (strongly disagree)

68

Contribution

• The inconsistency and redundancy of folksonomy-based approach was modeled as Ontology Crystallization Problem

• The iterative, collaborative convergence process was proposed

• The experimental result shows the ICOC system is feasible and effective.

69

結論• 「長尾理論」也無法否認 20% 的網站正在吸引 80% 目光的事實。• Web 2.0 : 網路上資訊分享的現象與環境

– 把 Web 2.0 做為商業手段時，正在使 dot com 夢想成真，但並不保證是一個獲利模式。

– UGC+SNS 這個現象，正在改變許多科學研究的方法。

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Web 2.0 的技術與應用