Title: Text Information Retrieval and Applications
1Text Information Retrieval and Applications
Advanced Topics
- By J. H. Wang
- May 27, 2009
2Outline
- Advanced Retrieval Technologies
- Cross-Language Information Retrieval
- Multimedia Information Retrieval
- Semantic Retrieval
- Applications to IR
- Advanced Google
- Meta Search
- Search Result Clustering
3Advanced Retrieval Technologies
- Cross-Language Information Retrieval (CLIR)
- Multimedia IR (image, speech, music, video)
- Semantic retrieval (XML, Semantic Web)
4Cross-Language Information Retrieval
- Cross Language Information Retrieval (CLIR) -- A
technology enabling users to query in one
language and retrieve relevant documents written
or indexed in another language
5Cross Language Web Search
- A technology enabling users to query in one
language and retrieve relevant Web pages written
or indexed in another language
6Why Cross-Language?
- Source Global Reach (global-reach.biz/globstats)
7Internet World Users by Language
8Top Ten Languages Used in the Web
Source Internet World Stats (Mar. 31, 2009)
TOP TEN LANGUAGESIN THE INTERNET Internet Usersby Language InternetPenetrationby Language Growthin Internet( 2000 - 2008 ) Internet Users of Total World Populationfor this Language(2008 Estimate)
English 463,790,410 37.2 226.7 29.1 1,247,862,351
Chinese 321,361,613 23.5 894.8 20.1 1,365,138,028
Spanish 130,775,144 32.0 619.3 8.2 408,760,807
Japanese 94,000,000 73.8 99.7 5.9 127,288,419
French 73,609,362 17.8 503.4 4.6 414,043,695
Portuguese 72,555,800 29.7 857.7 4.5 244,080,690
German 65,243,673 67.7 135.5 4.1 96,402,666
Arabic 41,396,600 14.2 1,545.2 2.6 291,073,346
Russian 38,000,000 27.0 1,125.8 2.4 140,702,094
Korean 36,794,800 51.9 93.3 2.3 70,944,739
TOP 10 LANGUAGES 1,337,527,402 30.4 329.2 83.8 4,406,296,835
Rest of the Languages 258,742,706 11.2 424.5 16.2 2,303,732,235
WORLD TOTAL 1,596,270,108 23.8 342.2 100.0 6,710,029,070
Top Ten Languages Used in the Web( Number of Internet Users by Language )
More and more non-English users!
9Web Content
More and more non-English pages
Source Network Wizards Internet Domain Survey
(Jan 99 )
10Chart of Web Content (by Language)
Source Vilaweb.com, as quoted by eMarketer
(Feb. 2001)
- Total Web pages 313 B
- English 68.4
- Japanese 5.9
- German 5.8
- Chinese 3.9
- French 3.0
- Spanish 2.4
- Russian 1.9
- Italian 1.6
- Portuguese 1.4
- Korean 1.3
- Other 4.6
11Language Percent of Public Sites
- English 72
- German 7
- Japanese 6
- Spanish 3
- French 3
- Italian 2
- Dutch 2
- Chinese 2
- Korean 1
- Portuguese 1
- Russian 1
- Polish 1
Source OCLC, 2002
12Web Users and Pages (10 years ago)
Challenge of Scalability !
Total Users 800MChinese Users 110M Including
87M (CN), 4.9M (HK), 11.6M (TW), 2.9M (MY), 2.14M
(SG), 1.5M (US), and others. Source Global
Reach, 2004
13Number of Chinese Web Pages
10,030,000,000 pages
Scalability Problem !
14Number of Web Pages
The worlds largest search engine ?
Billions Of Textual Documents IndexedDecember
1995-September 2003
Search Engine Reported Size Page Depth
Google 8.1 billion 101K
MSN 5.0 billion 150K
Yahoo 4.2 billion (estimate) 500K
Ask Jeeves 2.5 billion 101K
KEY GGGoogle, ATWAllTheWeb, INKInktomi,
TMATeoma, AVAltaVista. Source Search Engine
Watch (Nov. 2004)
15Number of Web Pages
- Estimated size
- Web pages in the world 19.2 billion pages
(indexed by Yahoo as of August 2005) - Websites in the world 70,392,567 websites
(indexed by Netcraft as of August 2005) - Web pages per website 273 (rounding to the
nearest whole number) - Updated estimate
- 231,510,169 distinct websites (as found by the
Netcraft Web Server Survey in April 2009) - 63.2 billion
Source http//news.netcraft.com/archives/web_ser
ver_survey.html
Source http//www.boutell.com/newfaq/misc/sizeof
web.html
16Number of Web Pages
- 1 trillion unique URLs (We knew the web was big,
by Jesse Alpert Nissan Hajaj, Software
Engineers, Web Search Infrastructure Team, 25
July 2008) - 19,200,000,000 pages (Mayer, Tim, 8 August 2005,
Our Blog is Growing Up And So Has Our Index) - 320,000,000 pages (World Wide Web is 320 million
and growing, BBC News Sci/Tech, 3 April 1998.) - 1,000,000,000 pages (Internet. How much
information? 2000. Regents of the University of
California.) - 800,000,000 pages (Maran, Ruth, and Paul
Whitehead. "Web Pages." Internet and World Wide
Web Simplified, 3rd ed. Foster City IDG Books
Worldwide, 1999. ) - 8,034,000,000 pages (Miller, Colleen. web sites
number of pages. NEC Research, IDC.)
Source http//hypertextbook.com/facts/2007/Loran
tLee.shtml
17Challenge of Cross-Language Web Search
- Existing CLIR systems mostly rely on bilingual
dictionaries and dictionary lookup - 81 of the search terms could not be obtained
from common English-Chinese translation
dictionaries
????? (CPU), ???? (E-commerce), ??????(PDA), ??
(Yahoo), ???? (NASA), ???? (Star War), ?????
(SARS),
18Challenge
- Existing CLIR systems mostly rely on bilingual
dictionaries and dictionary lookup - 81 of the search requests could not be obtained
from common English-Chinese translation
dictionaries - How to find effective translations automatically
for query terms not included in a dictionary ?
19Query Translation CLIR in DL
Chinese Query
Mono-Lingual Document Search
Chinese Digital Libraries
??
Possible global use
20Query Translation CLIR in DL
Chinese Query
Mono-Lingual Document Search
Chinese Digital Libraries
??
Need for CLIR services
21Query Translation CLIR in DL
Chinese Query
Mono-Lingual Document Search
Chinese Digital Libraries
??
??/?/??
Query Translation
22Query Translation CLIR in DL
Chinese Query
Mono-Lingual Document Search
Chinese Digital Libraries
??
??/?/??
Cost-ineffective to construct translation
dictionaries
Query Translation
23Query Translation CLIR in DL
Chinese Query
Mono-Lingual Document Search
Chinese Digital Libraries
??
??/?/??
Query Translation
Taking the Web as online corpus to deal with
translation of unknown terms
?
Web
24Query Translation CLIR in DL
Chinese Query
Mono-Lingual Document Search
Chinese Digital Libraries
??
??/?????
English Query
Query Translation
National Palace Museum
?
Online Term Translation Suggestions
?
Web
25Query Translation CLIR in DL
Chinese Query
Mono-Lingual Document Search
Chinese Digital Libraries
??
??/?/??
English/Japanese/Korean Queries
?
Query Translation
?
Auto- generated Translation Lexicons
?
Web
26CLIR
- Conventional approach to query translation
- Parallel documents as the corpus
- Assume long queries
- Problems of CLIR in digital libraries
- No corpus for cross-lingual training
- Short queries
- ? Out-of-dictionary terms
- Ex proper nouns, new terminologies,
English Terminologies Chinese Translation
mechanical strain ????
viscous damping ????
Richard Feynman ??
Hyoplastic Left Heart Syndrome ?????????
NII Japan ????????
SARS ??????????
Extracorporeal Shock Wave Lithotripsy ????
Davinci ???
27Translation Lexicon Construction for CLIR
- To use the Web as the corpus for query
translation - Web mining techniques
- Anchor-text-based ACM TOIS 04, ACM TALIP 02
- Search-result-based JCDL 04
- To extract terms from real document collections
as possible queries - Term extraction method SIGIR 97
28Web Mining Approach to Term Translation Extraction
The Web
Source query
Anchor texts
Academia Sinica
LiveTrans Engine
Search results
Target translations
?????/???
- LiveTrans http//wkd.iis.sinica.edu.tw/LiveTrans/
29National Palace Museum vs. ?????Search-Result
Page
Noises
- Mixed-language characteristic in Chinese pages
- How to extract translation candidates?
- Which candidates to choose?
30Yahoo vs. ?? -- Anchor-Text Set
- Anchor text (link text)
- The descriptive text of a link on a Web page
- Anchor-text set
- A set of anchor texts pointing to the same page
(URL) - Multilingual translations
- Yahoo/??/??
- America/??/????
- Anchor-text-set corpus
- A collection of anchor-text sets
??-USA
Korea
Yahoo Search Engine
Yahoo! America
?????Yahoo!
http//www.yahoo.com
????
??????
Japan
Taiwan
China
31Term Translation Extraction from Different
Resources
WebSpider
Term Extraction
Search Engine
SimilarityEstimation
Source Query
Target Translation
National Palace Museum
???????, ??, ?????
32LiveTrans Cross-language Web Search
33More Examples
34More Examples
35Multimedia IR
- Different forms of information need
- Image retrieval
- Speech information retrieval
- Music information retrieval
- Video information retrieval
36Image Retrieval
- Content-based
- Query by image content
- Query by example (????)
- Similarity in visual features
- Color, texture, shape,
- Relevance feedback
- Text-based
- Annotation
37Content-Based Image Retrieval (CBIR)
- Example systems
- CIRES (Content-based Image Retrieval System)
http//amazon.ece.utexas.edu/qasim/research.htm - SIMPLIcity http//www-db.stanford.edu/IMAGE/
- National Museum of History http//210.201.141.12/
cgi-bin/cbir-query.cgi?tid-1 -
38Relevance Feedback (RF)
Source Dr. Cheng
Image
Similar images (no RF)
39Similar Images Using Relevance Feedback
Image
Similar images using RF
40Automatic Image Annotation
Problem 1
Keywords?
Visual Similarity
polar bear ice snow
white bear snow tundra
polar bears snow fight
Image Banks with Annotations
41Spoken Document Retrieval
- Spoken document retrieval
- Indexing speech messages using speech recognition
- Retrieving relevant messages for a text/speech
query - Techniques
- Document Processing acoustic change detection,
speech/non-speech detection, Mandarin/non-Mandarin
detection, story segmentation, speaker
recognition/clustering - Speech Recognition
- Indexing/Retrieval
42SoVideo
43Music Information Retrieval
- Finding a song by similar melody
- Query by singing
- Query by humming
- Singer identification
- Background noise
- Singer voice model
44Video Information Retrieval
- Difference with CBIR
- Temporal information
- Structural organization
- Complexity of querying system
- Techniques
- Video segmentation
- Keyframe identification
45Semantic Retrieval
- HTML vs. XML
- Semantic Web (Agent, Ontology, RDF)
46Common Language of the Web
- HTML
- Link Pi ? Pj
- URL (URI), anchor text
- Part-of
National Taiwan University
http//www.ntu.edu.tw/
NTU
47Link Analysis Hubs Authorities in PageRank
48Current Web Search
- Keyword-based search (e.g., Google)
- Full text indexing
- Page authority (link analysis)
- Page popularity (query log and users click)
- Problems
- Not specific
- Data in pages have no semantic annotations
- Yo-yo Mas most recent CD
- No topic disambiguation
- Documents with different topics mix together
- Yo-yo Mas CDs, concerts, biography, gossips,
49Search on Semantic Web
- Metadata search
- To increase precision and flexibility
- Topic-based search
- To help contextualize queries and overlay results
in terms of a knowledge base
50XML (Extensible Markup Language)
- More flexible tags
- DTD (Data Type Definition)
- Definition of the tags
51XML Search
- XML Text Search Engines
- Amberfish (Etymon)
- X3 (X-cubed) (DocSoft)
- UltraSeek (Verity)
- XML Structured Query Engines
- Fxgrep
- Cheshire II (UC Berkeley)
- XML Query Languages
- XQuery (W3C XMLQuery)
- XQL
- XML-QL
52Semantic Web
- "The Semantic Web is an extension of the current
Web in which information is given well-defined
meaning, better enabling computers and people to
work in cooperation." -- Tim Berners-Lee, James
Hendler, Ora Lassila, The Semantic Web,
Scientific American, May 2001
53Semantic Web
Agent
Agent
RDF
ontology
Agent
54Semantic Web
- RDF (Resource Description Framework)
- Common language
- Ontology
- Knowledge representation
- Agent
55Why Semantic Web?
- Standardizing knowledge sharing and reusability
on the Web - Interoperable (independent of devices and
platforms) - Machine readableenabling intelligent processing
of information
56An Example of Semantic Relation
author
work
written by
publisher
publish
57What is a Software Agent?
- A paradigm shift of information utilization from
direct manipulation to indirect access and
delegation - A kind of middleware between information demand
(client) and information supply (server) - A software that has autonomous, personalized,
adaptive, mobile, communicative, social, decision
making abilities
58What is Ontology?
- An ontology is a formal and explicit
specification of shared conceptualization of a
domain of interest (T. Gruber) - Formal semantics
- Consensus of terms
- Machine readable and processible
- Model of real world
- Domain specific
59What is Ontology?(2)
- Generalization of
- Entity relationship diagrams
- Object database schemas
- Taxonomies
- Thesauri
- Conceptualization contains phenomena like
- Concepts/classes/frames/entity types
- Constraints
- Axioms, rules
60Agents and Ontology
- Agents must have domain knowledge to solve
domain-specific problems - Agents must have common sharable ontology to
communicate and share knowledge with each other - The common sharable ontology must be represented
in a standard format so that all software agents
can understand and communicate
61Agents and Semantic Web
- Semantic Web provides the structure for
meaningful content of Web pages, so that software
agents roaming from page to page will carry out
sophisticated tasks - An agent coming to a clinics web page will know
Dr. Henry works at the clinic on Monday,
Wednesday and Friday without having the full
intelligence to understand the text - Assumption is Dr. Henry make the page using an
off-the-shelf tool, as well as the resources
listed on the Physical Therapy Associations site
62Knowledge Representation on the Web
- The challenge of the Web is to provide a language
to express both data and rules for reasoning
about the data meta-data that allows rules from
any existing knowledge representation system to
be exported onto the Web - Adding logic to the Web means to use rules to
make inference, choose actions and answer
questions. The logic must be powerful enough but
not too complicated for agents to consider a
paradox
63Language Layers on the Web
Trust
DAML-L (logic)
Declarative Languages OIL, DAMLOnt
PICS
DC
XHTML SMIL
RDF
XML
HTML
Semantic web infrastructure is built on RDF data
model
64Languages on the Web
- HTMLURL
- XMLDTD (Data Type Definition)
- RDFRDF schema
65Statements RDF
- The basic structure of RDF is object-attribute-va
lue - In terms of labeled graph O-A-gtV
A
O
V
66Semantic Web Search Engine
- Swoogle http//swoogle.umbc.edu/ CIKM 2004
- SHOE (Simple HTML Ontology Extensions)
http//www.cs.umd.edu/projects/plus/SHOE/search/ - SWSE http//www.swse.org/
- http//www.semanticwebsearch.com/
67Applications to IR
- Advanced Google
- Meta Search
- Search Result Clustering
68What do Users Really Want?
- Topic-based vs. keyword-based
- NTU
- How to improve current search engines?
- Resources about Search Engines
- Search Engine Watch http//searchenginewatch.com/
- Research Buzz http//researchbuzz.com/
69Advanced Google
- Is Google good enough?
- NTU
- NTU university
- NTU university Singapore
- More and more Services
- Google Web, Image, News, Video, Google Desktop
Search , - Google Groups, Gmail, Google Talk, Google
Calendar, - Google Mobile, Google SMS, Google Local,
- Google Print (Book Search), Google Maps, Google
Earth, - Google Scholar, Translate, Finance, Docs, Reader,
- More about Google Services
- http//www.google.com/options/
- Google Labs http//labs.google.com/
70More Types of Document Search
- Google Web, Image, News, Groups, Desktop
(Office, mail), - Microsoft Lookout (mail)
- Yahoo Stata (mail), Adobe (PDF)
71Searching Different Media
- Multimedia Search MP3, Blog, messenger, mobile,
- Baidu.com MP3, image, news,
- Singingfish.com (AOL) audio/video,
- GoFish.com audio, video, mobile, games
- AllTheWeb.com pictures, audio, video,
- Blog search engines
- Daypop, Bloogz, Waypath,
- A9.com (by Amazon)
- Books, movies,
- Bookmark, history, discover, diary
- Mobissimo.com
- Airfare search, hotel search
- Yahoo-OCLC toolbar library search
- Searching Open WorldCat (OCLC union catalog)
72Different Forms of Presentation
- Clusty.com (by Vivisimo)
- Clustering engine
- Snap.com (by Idealab)
- Sorting by popularity, satisfaction, Web
popularity, Web satisfaction, domain, - Alexa.com (by Amazon)
- Average user review ratings,
- Visualization
- TouchGraph Google Browser http//www.touchgraph.c
om/TGGoogleBrowser.html - Kartoo.com a visual meta search engine
- Girafa
- ConceptSpace
- LostGoggles (formerly MoreGoogle) thumbnail
preview
73Focused Search Engines
- Scirus http//scirus.landingzone.nl
- For scientific information only
- Google Scholar http//scholar.google.com/
- For scholarly literature
74Some Google Hacks and Searching Tricks
- References
- Tara Calishain and Rael Dornfest, Google Hacks,
OReilly - Kevin Hemenway and Tara Calishain, Spidering
Hacks, OReilly - http//douweosinga.com/projects/googlehacks
- Tara Calishain, Web Search Garage, Prentice
Hall - Chris Sherman, Google Power Unleash the Full
Potential of Google, McGraw Hill
75Further Utilizing Google
- Google API http//www.google.com/apis/
- 1,000 automated queries per day
- Google Hacks
- Google Talk
- Word Color
- Google Battle
- Google Date
- Google Best Time to Visit
- Google Protocol
76Meta (Federated) Search
- To search simultaneously several individual
search engines and their databases of web pages - Ixquick, Metacrawler, Dogpile,
- Clustering meta-searchers
- Vivisimo, KillerInfo,
- Meta-search engines for deep digging
- SurfWax, Copernic Agent,
77Meta Search Engine
Web
SE1
MetaSearchEngine
SE2
User
SEn
78Search Result Clustering
- Why search result clustering?
- Why is SRC different from document clustering?
- In assessment of algorithms quality
- Precision, recall vs. user-oriented, subjective
assessment
79Example of Search Result Clustering
National Taiwan University
NTU Hospital
NTU?
Nanyang Technological University, Singapore
80Example Clustering Search Engines
- Vivisimo.com
- Clusty.com
- WebClust.com
- KillerInfo.com
- InfoNetWare.com
- SnakeT (Snippet Aggregation for Knowledge
ExTraction) http//roquefort.unipi.it/ - A hierarchical clustering engine for snippets
- Mooter.com
81Example on Vivisimo
82Vivisimo (cont.)
83Clusty.com
84InfoNetWare.com
85Thanks for Your Attention!