TIJAH @ INEX 2003

The Cirquid Team

CWI and University of Twente

Overview

Introduction Content-Only (CO) (Pattern-Based) Structured Querying Conclusions and Future Work Questions/Discussion

Content-Only (CO)

Same model as for INEX 2002

Exhaustivity (content-based relevance)– Statistical Language Model [Hiemstra

2000]

Specificity– Log-normal distribution– Component size, mean at ~2500 words

Structured Querying (SCAS/VCAS)

Pattern-Based Structured Querying

Collection of 3 patterns– Base pattern for determining a single

subtree (pattern 1)– More complex combinations of pattern 1

instances (patterns 2 and 3)

Use of ALL topic’s keywords to process EACH OF the about clauses

//article[about(./,’IR’) AND about(.//sec,’XML’)]

About Function

Use of ALL topic’s keywords to process EACH OF the about clauses

//article[about(./,’IR’) AND about(.//sec,’XML’)]

//article[about(./,’IR XML’) AND

about(.//sec,’IR XML’)]

About Function

Pattern 1

Simplest pattern instance Topic 69

VCAS and SCAS

/article/bdy/sec[about(.//st, ‘…’)]

Pattern 1 – VCAS and SCAS

article

bdy…

sec… …

Nodeset selections

Containment

Relevance

Containmentst

Average ranked containing

Previous operation is ranked containing

Multiple subtrees within the target element are averaged

sec

st st0.2 0.1

Average ranked containing

Previous operation is ranked containing

Multiple subtrees within the target element are averaged

sec

st st0.2 0.1

sec 0.15

Pattern 2 Topic 73

VCAS– Absence of subtree does not render target

irrelevant completely SCAS

– All subtrees specified need to be present for relevance

//article[about(.//st, ‘…’) AND about(.//bib, ‘…’)]

Pattern 2 – VCAS

st

article

……

bib

Split up into set of pattern 1 instances

Combine resultsets– OR -> max– AND -> min (non zero)

Pattern 2 – SCAS

article

……

st

bib

Split up into set of pattern 1 instances

Combine resultsets– AND -> min– OR -> max

Pattern 2 - Example

//article[about(.//st, ‘+comparison’) AND about(.//bib, ‘machine learning’)]

//article[about(.//st, ‘comparison machine learning’)]//article[about(.//bib, ‘comparison machine learning’)]

1.- Execution of 2 pattern 1

2.- Combining resultsArt 1 0.2

Art 2 0

Art 1 0.1

Art 2 0.3

AND

Art 1 0.1

Art 2 0.3

Art 1 0.1

Art 2 0

VCAS

SCAS

Pattern 3

Topic 64 CAS//article[about(., ‘…’)]//sec[about(., ‘…’)]

VCAS– What does the first about mean?– Drop all about-calls, except those specified for target element

SCAS– Split up into set of pattern 1 instances– Topdown structural correlation to correct nodeset

Pattern 3 – VCAS

article

……

sec (about 2)

(about 1)

//article//sec[about(., ‘…’)]

Pattern 3 – SCAS

article

……

sec

1.- about 1

2.- about 2

3.- containment

Ranked by the scores of the target element

Pattern 3 - Example

//article[about(./, ‘hollerith’)]//sec[about(., ‘DEHOMAG’)]

//article [about(./, ‘hollerith DEHOMAG’)]//article//sec [about(./, ‘hollerith DEHOMAG’)]

1.- Execution of 1 or 2 pattern 1

2.- Ranked containingArt 1 0.2

Art 2 0

sec 1 0.1

sec 2 0.3

sec 1 0.1

sec 2 0.3VCAS

SCAS sec1 0.1

In case sec 1 belongs to art 1 and sec 2 do not

Only second about

Physical Query Plan - Pattern 1

/article/bdy/sec[about(.//st, ‘…’)]

/article/bdy/sec//st

W Q

about

/article/bdy/sec

avg-groupby

Conclusions

CO model works pretty well– Article run still

‘Keep it simple’ approach

Any questions?