Similarity Measures for Query Expansion in TopX

Caroline Gherbaoui

Universität des Saarlandes Naturwissenschaftlich-Technische Fak. I

Fachrichtung 6.2 - Informatik

Max-Planck-Institut für InformatikAG 5 - Datenbanken und Informationssysteme

Prof. Dr. Gerhard Weikum

Overview

background knowledgesimilarity measures for the query expansionevaluation of the computed similarity valueschanges in TopXconclusion

Background

top-k query processing provides k most relevant results

query expansion extends source query terms

word sense disambiguation extracts correct meaning

ontology amount of terms with their meanings and

semantic relations

Word Sense Disambiguation

„java, coffee“

„java “

„island“

„coffee“

„programming language“

…

Query Expansion

„COFFEE“ „drink, espresso“

TopX

top-k retrieval enginetext and XML dataword sense disambiguationquery expansionontology

TopX – WordNet Ontology

lexicon for the English languagehierarchical relationsone relation one direction~160,000 words~120,000 synsets~210,000 relations

TopX – YAGO Ontology

Wikipedia and WordNethierarchical and not hierarchical relationsone relation two directions~2,100,000 words~2,200,000 concepts~6,000,000 relations

Similarity Measures

Dice similarity the already used measure in TopX

NAGA similarity applied measure for YAGO

Best WordNet similarity measure with best result among WordNet

measures

Dice Similarity Measure

sdfsdf

measures the intersection of two regions

BA

BABADICE

2

,

BFREQAFREQ

BAFREQBADICE

,2

,

NAGA Similarity Measure

sdfasfsdf

combination of the confidence of a relation and the informativeness of a relation

BABAconfBANAGA ,inf1,,

n

iii wtrustwBAacc

nBAconf

1

,,1

,

)(

,,inf

AFREQ

BAFREQBA

)(

,,inf

BFREQ

BAFREQAB

Best WordNet Similarity Measure

sdfsdfsdf

product of the transfer function of the path length and the transfer function of the concept depth

hflfBAWordNet 21,

lelf 1

hh

hh

ee

eehf

2

Evaluation

All Relation Types

0,00%

10,00%

20,00%

30,00%

40,00%

50,00%

60,00%

70,00%

80,00%

90,00%

0 0 < 0.01 < 0.025 < 0.1 < 0.25 < 0.5 < 1

Dice (WordNet ontology)

Dice

NAGA backward

NAGA forward

Best WordNet

am

ou

nt

in %

Evaluation

DICE measure applicable also on the YAGO ontology

NAGA measure applicable with omitting of the forward direction

Best WordNet measure not applicable due to the density of YAGO

Changes for TopX

tuning of some procedures Dijkstra algorithm word sense disambiguation query expansion

extension of configuration file

Conclusion

larger knowledge basemore flexibilityincreased complexityfurther measure for the similarity

computation NAGA similarity

Questions?