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Even More TopX: Relevance Feedback PowerPoint PPT Presentation


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Ralf Schenkel. Even More TopX: Relevance Feedback. Joint work with Osama Samodi, Martin Theobald. TopX Results with INEX 2007. 660,000 XMLified English Wikipedia articles 107 topics with structural query (CAS) nonstructural (aka keyword) query (CO) informal description of information need

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Even More TopX: Relevance Feedback

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Ralf schenkel l.jpg

Ralf Schenkel

Even More TopX:Relevance Feedback

Joint work with Osama Samodi, Martin Theobald


Topx results with inex 2007 l.jpg

TopX Results with INEX 2007

  • 660,000 XMLified English Wikipedia articles

  • 107 topics with

    • structural query (CAS)

    • nonstructural (aka keyword) query (CO)

    • informal description of information need

    • assessed answers (text passages)

  • Evaluation metric based on recall/precision:

    fraction of relevant characters retrieved

result list

C: #characters retrieved

R: #relevant characters retrieved

P[0.01]=R/C

1% recall


Results with inex 2007 l.jpg

Results with INEX 2007

structure queries

keyword queries

improved structure

queries

(unchecked)

improved keyword

queries

document retrieval

Structural constraints can improve result quality


Users vs structural xml ir l.jpg

Users vs. Structural XML IR

  • Structural query languagesdo not work in practise:

  • Schema is unknown or heterogeneous

  • Language is too complex

  • Humans don‘t think XPath

  • Results often unsatisfying

//professor[contains(.,SB)

and contains(.//course,IR]

I need information about a professor in SB who teaches IR.

  • System support to generate „good“ structured queries:

  • User interfaces („advanced search“)

  • Natural language processing

  • Interactive query refinement


Relevance feedback for interactive query refinement l.jpg

Relevance Feedback for Interactive Query Refinement

XML

1

IR

IR

2

index

3

Fagin

index

4

index

XML

IR

query evaluation

XMLnot(Fagin)

1. User submits query

2. User marks relevant and nonrelevant docs

  • Feedback for XML IR:

  • Start with keyword query

  • Find structural expansions

  • Create structural query

3. System finds best terms to distinguish between relevant and nonrelevant docs

4. System submits expanded query


Structural features l.jpg

Structural Features

User marksrelevant result

article

frontmatter

body

backmatter

sec

sec

author„Baeza-Yates“

sec

„Semistructured data…“

subsec„XML has evolved…“

subsec

p

p

p„With the advent of XSLT…“

Possible features:

Tag+Content of descen-dants of ancestors

Tag+Contentof ancestors

Content ofresult

Tag+Content ofdescendants

AD: article//author[Baeza]

C: XML

D: p[XSLT]

A: sec[data]


Feature selection l.jpg

Feature Selection

Order features by Robertson Selection Value:

wherepf probability that f occurs in relevant result,qf probability that f occurs in nonrelevant result

Compute Robertson-Sparck-Jones weight for each feature (also used as weight in query):

whererfnumber of relevant results with fR number of relevant resultseffnumber of elements that contain fEnumber of all elements


Query construction l.jpg

Query Construction

descendant-or-self axis

article

author[Baeza]

sec[data]

p[XSLT]

Initial query:

query evaluation

needs schemainformation!

*[query evaluation]

*[query evaluation XML]

Tag+Content of descen-dants of ancestors

Tag+Contentof ancestors

Content ofresult

Tag+Content ofdescendants

AD: article//author[Baeza]

C: XML

D: p[XSLT]

A: sec[data]


More fancy query construction l.jpg

More Fancy Query Construction

p[XSLT]

article

sec[data]

author[Baeza]

*[query evaluation]

*[query evaluation XML]

  • No valid NEXI query, but XPath (ancestor axis)

  •  DAG queries in TopX

  • needs disjunctive evaluation


Example pyramids of egypt l.jpg

Example: „pyramids of egypt“


Architecture l.jpg

Architecture

query

TopX SearchEngine

INEX Tools & Assessments

results

query +

results

feedback

Candidate Classes

expanded query

C

Module

D

Module

AD

Module

A

Module

Weighting + Selection


Rf in the topx 2 0 interface l.jpg

RF in the TopX 2.0 Interface


Evaluation methodology l.jpg

Evaluation Methodology

Goal: avoid „training on the data“

  • Freeze known results at the top

  • Remove known results+X from the collection

    • resColl-result: remove results only (~doc retrieval)

    • resColl-desc: remove results+descendants

    • resColl-anc: remove results+ancestors

    • resColl-path: remove results+desc+anc

    • resColl-doc: remove whole doc with known results


Evaluation inex 2003 2004 l.jpg

Evaluation: INEX 2003&2004

  • INEX collection(IEEE-CS journal and conference articles):

    • 12,107 XML docs with 12 mio. elements

    • queries with manual relevance assessments

  • 52 keyword queries from 2003 & 2004 with our TopX Search Engine [VLDB05]

  • Baseline run with MAP~0.1, [email protected]=0.174

  • Automatic feedback for top-k from relevance assessments

  • Evaluation ignores results used for feedback and descendants of results (rescoll-desc)


Inex 2003 2004 rescoll desc l.jpg

INEX 2003&2004, rescoll-desc

All dimensions together are best.

Reasonable results for INEX 2005 RF Track


Results for inex 2005 track l.jpg

Results for INEX 2005 Track

  • INEX IEEE collection (scientific articles)

  • Feedback for the top-20 from the assessments (with the strict quantisation -> only „relevant“ and „nonrelevant“)

  • top 10 expansion features

  • runs with top 1500 results

  • MAP with inex_eval (with strict quantisation)


Some results for inex 2006 rf track l.jpg

(Some) Results for INEX 2006 RF Track

  • INEX Wikipedia collection

  • Feedback for the top-20 from the assessments (with the generalized quantisation -> graded relevance)

  • top 10 expansion features

  • runs with top 100 results for first 50 topics (time…)

  • MAP with inex_eval (with generalised quantisation)

  • Significance tests (Wilcoxon signed-rank, t-test)


Conclusions l.jpg

Conclusions

  • Queries with structural constraints to improve result quality

  • Relevance Feedback to create such queries

  • Structure of collection matters a lot


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