BoTLRet: A Template-based Linked Data Information Retrieval

BoTLRet: A Template-based Linked Data InformationRetrieval

Md-Mizanur Rahoman

July 11, 2013

Introduction Related Work Proposed Method More than two keywords Experiments Conclusion Future Work Publication

Outline

Introduction

Linked dataLinked data access

Related Work

Problem in current state of the artProbable solution

BoTLRet: Proposed System

BoTLRet for two-input-keywords queryBoTLRet for more than two-input-keywords query

Experiment

Experimental setupComparison with other systems

Conclusion

Future Work & PublicationMd-Mizanur Rahoman | 2


Introduction

Linked dataRepresent knowledge using simpletechnique like subject, predicate, objectCan be presented by graph-like structureUse SQL-like expressive query calledSPARQL queryStore 295 datasets, 31 billion RDF triples,as of Sep. 2011

res:Barack_Obama

res:Hawaii

res:Shizuya_Hayashi

prop:placeOfBirth

prop:placeOfDeath

prop:placeOfBirth

B

A

C

z

y

K

i

onto:spouse

res:Michelle_Obama

x onto:spouse

prop:placeOfBirth

2.8311e+10

onto:areaTotal

yago/resource:Shizuya_Hayashi

owl:sameAs

Md-Mizanur Rahoman | 3


Data access over linked data

Linked data accessRequires know-how on linked data underlying technologies likeschema information or query formationImposes adaptation difficulty to general-purpose usersCould be considered for familiar keyword-based retrieval but introduceimplementation difficulty

Mere keyword does not hold semantics

Could be implemented by introducing template for familiarkeyword-based retrieval

Template holds special semantics



Related Work

GoRelations: An Intuitive Query System for DBpedia [Han, et al.,2011]

A comparatively easy kind of query formation technique but user stillneeds to learn query

Template-Based Question Answering Over RDF Data [Unger, et al.,2012]

A NL tool based QA system where templates are constructed using NLtools (e.g., parser)Suffers in entity inferencing which is essential

Keyword-driven SPARQL Query Generation Leveraging BackgroundKnowledge [Shekarpour, et al., 2011]

A keyword-based linked data retrieval system but user still needs toknow dataset underlying technologiesOnly can handle query with at most two keywords



Problem & Solution

Current template fitted linked data access

Problem

Lacks of guideline on template constructionHolds poor template ranking strategy

Solution

Construct templates according to link data structureRank templates using dataset’s inside statistics



Overview of Our Proposed System

Binary Progressive Template Paradigm over Linked DataRetrieval (BoTLRet)

Assumes keywords are given orderlyConstructs keyword fitted query templates for

Two-input-keywordsMore than two-input-keywords

Ranks all query templates and selects best query templates for eachtwo adjacent keywords by a binary progressive approach

Construct query templates for each adjacent two-input-keywordsExtend template construction progressively, if more thantwo-input-keywords

Constructs final SPARQL query from all best query templates for eachtwo adjacent keywords



BoTLRet for two-input-keywords query

Resource Manager

1 2Keywords k , k

Related Resources,

Resource Types

Templates Arranged

in Affinity Matrix

Template Constructor Best Template

Selector

Best Possible

Query Template




Resource ManagerFinds related resources by matching keywords and dataset resourcesClassifies each related resource to predicate and non-predicate bytheir frequent appearance position

Resource Manager

1 2Keywords k , k

Related Resources,

Resource Types

Templates Arranged

in Affinity Matrix


Selector

Best Possible

Query Template




Template ConstructorFinds some kind of semantic structure (query template) for twoadjacent keyword related resources and their resource types to getrelated triplesRanks all query templates with their appearance frequencies andrelated resource closeness (depth level) capability

Resource Manager

1 2Keywords k , k

Related Resources,

Resource Types

Templates Arranged

in Affinity Matrix


Selector

Best Possible

Query Template



Query Template

Assume

r1 is keyword related resources from keyword k1r2 is keyword related resources from keyword k2

Query Template Depth level Network with SPARQL Querymerge-point

[‖?uri, r1, r2 ‖] 1

?uri

r2

r1

SELECT ?uri WHERE {?uri r1 r2.}... ... ... ...

[‖?uri, ?p1, r1 ‖,‖?uri, ?p2, r2 ‖] 2

?uri

r1 r2

?p1 ?p

2

SELECT ?uriWHERE {?uri?p1r1.?uri?p2r2.}

... ... ... ...




Best Template SelectorSelects best query template from all ranked query templatesGenerates SPARQL query for best query template

Resource Manager

1 2Keywords k , k

Related Resources,

Resource Types

Templates Arranged

in Affinity Matrix


Selector

Best Possible

Query Template




Examplekeyword1 = spousekeyword2 = Barack Obamarelated resource1 = onto:spouserelated resource2 = res:Barack Obamaresource type1 = predicateresource type2 = non-predicateqt1 = [‖ ?uri , onto:spouse, res:Barack Obama ‖]qt2 = [‖ res:Barack Obama, onto:spouse, ?uri ‖]qt1 relates<res:Michelle Obama, onto:spouse, res:Barack Obama>qt2 relates<res:Barack Obama, onto:spouse, res:Michelle Obama>

res:Barack_Obama

res:Hawaii

res:Shizuya_Hayashi

prop:placeOfBirth

prop:placeOfDeath

prop:placeOfBirth

B

A

C

z

y

K i

onto:spouse

res:Michelle_Obama

xonto:spouse

prop:placeOfBirth

2.8311e+10

onto:areaTotal

yago/resource:Shizuya_Hayashi

owl:sameAs



BoTLRet for more than two-input-keywords query

Best Template

Constructor

Best Possible Query Templates,

Their Related Keywords and

Weights

Keywords

k , k , ... k1 2 n

Comparator

Retained Template,

Not Retained Keyword

Refiner

Refined Retained Template,

Adjusted Template

Final Merged

Query Template

Merger




Best Template ConstructorConstructs best query templates for each two adjacent keywords

Best Template

Constructor



Weights

Keywords

k , k , ... k1 2 n

Comparator

Retained Template,


Refiner


Adjusted Template

Final Merged

Query Template

Merger




ComparatorCompares each two adjacent best query templates, then

keeps one query template as retained query templatefinds one input keyword (called as not retained keyword) that is notkept by retained query template

Finds common input keywords which are associated to “retained querytemplates” and “not retained keywords”

Best Template

Constructor



Weights

Keywords

k , k , ... k1 2 n

Comparator

Retained Template,


Refiner


Adjusted Template

Final Merged

Query Template

Merger




RefinerDiscards common input keywords from previous processFinds query templates (called as adjusted template) for “not retainedkeywords” that are not discarded as common input keywordsAccumulates best “retained query template” (called as refinedretained template) and “adjusted template”

Best Template

Constructor



Weights

Keywords

k , k , ... k1 2 n

Comparator

Retained Template,


Refiner


Adjusted Template

Final Merged

Query Template

Merger




MergerMerges all “refined retained templates” and “adjusted templates”

Best Template

Constructor



Weights

Keywords

k , k , ... k1 2 n

Comparator

Retained Template,


Refiner


Adjusted Template

Final Merged

Query Template

Merger




Assume, a process flow with 5 input keywordsk1, k2, k3, k4 and k5

K K K K K

qt qt qt qt

1 2 3 4 5

1 2 3 4

rt

nrk

rt

nrkrtnrk

1 2

1 2

33

=

=

=

=

=

=qtk

qtk

qtk1

33

243

Keyword

Best Query Template

Retained Query Template


Refined retained template

Refined not retained keyword

Adjusted template

mgtMerged template

rrt = qtrrt = qt

12

1

4

rnk = k1 3

adt1from k3



Experimental Setup

Input

Ordered keywords to find intended result

Experimental Data

Question Answering over Linked Data (QALD) challenge dataConsist natural language questions

DBPedia test case - 42 questions over DBPedia datasetMusicBrainz test case - 46 questions over MusicBrainz dataset

Construction of Keywords

Consider QALD question and underlying datasetExample

In which films directed by Garry Marshall was Julia Roberts starring?keywords: Film, starring, Julia Roberts, Director, Garry Marshall



Experimental Perspectives

Analyze BoTLRet for

Completeness vs computational cost effectiveness

Construct another exhaustive systems (called as MTS) which use similartemplate construction but ignore resource classificationCompare recall, precision and F1 measure of MTS and BoTLRet w.r.ttheir incurred computational cost

Competitiveness over other state of the art systems

Execute experimental data over 2 recent template-based linked dataretrieval initiativesCompare recall, precision and F1 measure over those systems



DBPedia test case - Completeness vs ComputationalCost Effectiveness

Table: Performance analysis between MTS and BoTLRet

Keyword Group MTS BoTLRet

Precision Recall F1 Measure Precision Recall F1 Measure

2 0.935 0.963 0.941 0.898 0.926 0.9043 0.559 0.705 0.595 0.559 0.705 0.5954 0.333 0.333 0.333 0.333 0.333 0.3335 1.000 1.000 1.000 1.000 1.000 1.000

Average 0.851 0.795 0.808 0.827 0.771 0.785

Table: Computational cost consumption by MTS and BoTLRet

Used No of Templates Computational Cost by BoTLRet w.r.t MTS

MTS BoTLRetPR − NP case 49 3 0.061(=3/49)NP − NP case 49 8 0.163(=8/49)

TOT case 49 11 0.224(=11/49)

BoTLRet achieve very close performance to MTS with quite lowcomputational cost



DBPedia test case - Competitiveness

Table: Performance comparison between GoRelations and BoTLRet

Precision Recall F1 Measure

GoRalations 0.687 0.722 0.704BoTLRet 0.793 0.825 0.801

Table: Performance comparison between KSQGLBK and BoTLRet

Precision Recall F1 Measure

KSQGLBK 0.687 1.00 0.814BoTLRet 0.854 0.917 0.867

BoTLRet performs comparatively well over other state of the arttemplate-based linked data information retrieval initiatives



MusicBrainz test case - Completeness vsComputational Cost Effectiveness

Table: Performance analysis between MTS and BoTLRet

Keyword Group MTS BoTLRet

Precision Recall F1 Measure Precision Recall F1 Measure

2 0.944 0.944 0.944 0.944 0.944 0.9443 0.846 0.846 0.846 0.846 0.846 0.8464 1.000 1.000 1.000 0.000 0.000 0.000

Average 0.909 0.909 0.909 0.864 0.864 0.864

Table: Computational cost consumption by MTS and BoTLRet

Used No of Templates Computational Cost by BoTLRet w.r.t MTS

MTS BoTLRetPR − NP case 49 3 0.061(=3/49)NP − NP case 49 8 0.163(=8/49)

TOT case 49 11 0.224(=11/49)

BoTLRet achieve very close performance to MTS with quite lowcomputational cost



Conclusion

BoTLRet

Implements predefined templates for automatic incorporation ofsemantics in keyword-based linked data retrievalPresents concrete guideline for template construction and templaterankingShows implementation result for real linked implementation with somecomparison



Future Work

Work 1: Inclusion of Temporal Semantics over Keywrod-basedLinked Data Retrieval

Motivation - Temporal attributes hold concise semantics in any kind ofsearch paradigmBrief Research Plan

Start Submission to Conference Submission to Journal

March 2013 August 2013 October 2013

Work 2: Inclusion of Automatic Inferencing Framework toExtended-BoLTRet System over Multiple Linked Datasets Access

Motivation - Inference engine will able to produce more intuitive resultBrief Research Plan

Start Submission to Conference Submission to Journal

November 2013 August 2014 December 2014



Publication

Rahoman M., Ichise R.: An Automated Template SelectionFramework for Keyword Query over Linked Data. In Proceeding of2nd Joint International Semantic Technology Conference, pages175-190, (2012).

Rahoman M., Ichise R.: Inclusion of Temporal Semantics overKeyword-based Linked Data Retrieval. In Proceeding of 27thAnnual Conference of Japanese Society for Artificial Intelligence,(2013).


Questions?

Md-Mizanur Rahoman, [email protected]


Extra Slides



Query Template

TG Query Template Network with SPARQL Query for qt(r1, r2)qt(r1, r2) merge-point

TG1 [‖?uri, r1, r2 ‖]

?uri

r2

r1

SELECT ?uri WHERE {?uri r1 r2.}

[‖ r2, r1, ?uri ‖]

r

?uri

r1

2

SELECT ?uri WHERE {r2 r1 ?uri.}

TG2 [‖ r1, ?uri, r2 ‖]

r

r

1

2

?uri

SELECT ?uri WHERE {r1 ?uri r2.}

[‖ r2, ?uri, r1 ‖]

r

r1

2

?uri

SELECT ?uri WHERE {r2 ?uri r1.}

[‖?uri, ?p1, r1 ‖, ‖?uri, ?p2, r2 ‖]

?uri

r1 r2

?p1 ?p

2

SELECT ?uri WHERE {?uri ?p1 r1. ?uri ?p2 r2.}

[‖?uri, ?p1, r1 ‖, ‖ r2, ?p2, ?uri ‖]

?uri

r1 r2

?p1?p2

SELECT ?uri WHERE {?uri ?p1 r1. r2 ?p2 ?uri.}

[‖ r1, ?p1, ?uri ‖, ‖?uri, ?p2, r2 ‖] ?uri

r1

r2

?p1

?p2

SELECT ?uri WHERE {r1 ?p1 ?uri. ?uri ?p2 r2.}

[‖ r1, ?p1, ?uri ‖, ‖ r2, ?p2, ?uri ‖] ?uri

r1 r2

?p1

?p2

SELECT ?uri WHERE {r1 ?p1 ?uri. r2 ?p2 ?uri.}



Calculation of appearance frequency for querytemplate

Frequency of query template fqQT (qt(r1, r2))

Frequency of resource r , given query template qt(r1, r2)

fqR(r , qt(r1, r2)) =

PFs(r) if r is on subject in qt(r1, r2)PFp(r) if r is on predicate in qt(r1, r2)PFo(r) if r is on object in qt(r1, r2)

The Final appearance frequency flW (qt(r1, r2)) of query templateqt(r1, r2)

flW (qt(r1, r2)) = fqQT (qt(r1, r2))∗fqR(r1, qt(r1, r2))∗fqR(r2, qt(r1, r2))



Adjusted Template

Construct modified query template for each refined notretained keyword

Modified Modified Network with SPARQL Query for mqt(r1)Template Group Query Template merge-point

MTG1 [‖?uri, r1, ?o1 ‖]

?uri

?o

r1

1 SELECT ?uri WHERE {?uri r1 ?o1.}

MTG2 [‖ r1, ?p1, ?uri ‖]

r

?uri

?p1

1

SELECT ?uri WHERE {r1 ?p1 ?uri.}

[‖?uri, ?p1, r1 ‖]

?uri

r

?p1

1 SELECT ?uri WHERE {?uri ?p1 r1.}

Find adjusted template from modified query template foreach refined not retained keyword



Merger

Example

Merge all refined retained andadjusted query templates

Maintain keyword order in query templatemergingFind merge-points for each refinedretained and adjusted query templateMerge query templates in merge-points

?uri

rk

?p1

1,1

?uri

?o

rk

1

?uri

rk

rk

(i) (ii) (iii)

2,1 3,1

4,k

?uri

rk

?p1

1,1

?uri

?o

rk

1

?uri

rk

rk

(iii)

2,13,1

4,1

?uri

rk

?p1

1,1

?uri

?o

rk

1?o

rk

(i) (ii) (iii)

2,1

3,1

+

rk4,1

1

+

(a)

(b)

(c)

(i) (ii)+
