T.Sharon - A.Frank1

Internet Resources Discovery (IRD)

Web IR

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Web IR

• What’s Different about Web IR?

• Web IR Queries

• How to Compare Web Search Engines?

• The ‘HITS’ Scoring Method

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What’s different about the Web?

• Bulk ……………... (500M); growth at 20M/month• Lack of Stability ..… Estimates: 1%/day--1%/week• Heterogeneity

– Types of documents …. text, pictures, audio, scripts...

– Quality

– Document Languages ……………. 100+

• Duplication• Non-running text• High Linkage ..…………. 8 links/page average> =

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Taxonomy of Web Document Languages

SGML

HyTime

XMLMetalanguages

Languages

SMILMathMLRDFXHTML

HTMLTEI Lite

DSSSL

XSL

CSS

Style sheets

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Non-running Text

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What’s different about the Web Users?

Make poor queries– short (2.35 terms average)– imprecise terms– sub-optimal syntax (80%

without operators)– low effort

Wide variance on– Needs– Expectations– Knowledge– Bandwidth

Specific behavior– 85% look over one

result screen only

– 78% of queries not modified

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Why don’t the Users get what they Want?

User need

User request(verbalized)

Query toIR system

Results

Translationproblems

PolysemySynonymy

Example

I need to get rid of mice in the basement

What’s the best way to trap mice alive?

Mouse trap

Computer suppliessoftware, etc

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Alta Vista: Mouse trap

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Alta Vista: Mice trap

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Challenges on the Web

• Distributed data

• Dynamic data

• Large volume

• Unstructured and redundant data

• Data quality

• Heterogeneous data

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Web IR Advantages

High LinkageInteractivity Statistics

– easy to gather

– large sample sizes

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Evaluation in the Web Context

• Quality of pages varies widely

• Relevance is not enough

• We need both relevance and high quality = value of page

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Example of Web IR Query Results

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How to Compare Web Search Engines?

Search engines hold huge repositories!

Search engines hold different resources!

Solution: Precision at top 10 – % of top 10 pages that are

relevant (“ranking quality”)

Retrieved(Ret)

Resources

RR

RelevantReturned

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The ‘HITS’ Scoring Method

• New method from 1998:– improved quality– reduced number of retrieved documents

• Based on the Web high linkage • Simplified implementation in Google

(www.google.com)• Advanced implementation in Clever

Reminder: Hypertext -nonlinear graph structure

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‘HITS’ Definitions

• Authorities: good sources of content

• Hubs: good sources of links

A

H

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‘HITS’ Intuition

• Authority comes from in-edges.Being a hub comes from out-edges.

• Better authority comes from in-edges from hubs.Being a better hub comes from out-edges to authorities.

A H

A

H

H

H

H

A

A

A

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v

‘HITS’ Algorithm

A

w1

Hw2

wk

...

u1

u2

uk

...

Repeat until HUB and AUTH converge:

Normalize HUB and AUTH

HUB[v] := AUTH[ui] for all ui with Edge(v,ui)

AUTH[v] := HUB[wi] for all wi with Edge(wi,v)

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Google Output: Princess Diana

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Prototype Implementation (Clever)

Base

Root

1. Selecting documents using index (root)

2. Adding linkeddocuments

3. Iterating tofind hubs and authorities

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By-products

• Separates Web sites into clusters.

• Reveals the underlying structure of the World Wide Web.