An automated template selection framework for keyword query over linked data
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Transcript of An automated template selection framework for keyword query over linked data
An Automated Template Selection Framework forKeyword Query over Linked Data
Md-Mizanur Rahoman, Ryutaro Ichise
December 2-4, 2012, JIST2012: The 2nd Joint International Semantic Technology
Conference, Nara, Japan
Introduction Related Work Proposed Method Experiments Conclusion and Future Work
Outline
IntroductionRelated WorkProposed Method
Template management (for Two Keywords)Resource ManagerTemplate ConstructorBest Template Selector
Template management (for More than Two Keywords)Best Template ConstructorComparatorRefinerMerger
ExperimentExperimental DataResult AnalysisComparision with Other System
Conclusion and Future WorkMd-Mizanur Rahoman, Ryutaro Ichise |An Automated Template Selection Framework for Keyword Query over Linked Data | 2
Introduction Related Work Proposed Method Experiments Conclusion and Future Work
Introduction
Linked data295 data sets, 31 billion RDF triples (as of Sep. 2011).Difficult data access option for general purpose users.
Keyword-based linked data accessUser frinedly for general purpose users.Difficult in implementation.Template, a good implementation option.
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Introduction Related Work Proposed Method Experiments Conclusion and Future Work
Problem
Current template fitted keyword-based linked data accessLacks of guideline on template construction.Holds poor template ranking strategy.
Md-Mizanur Rahoman, Ryutaro Ichise |An Automated Template Selection Framework for Keyword Query over Linked Data | 4
Introduction Related Work Proposed Method Experiments Conclusion and Future Work
Solution
Construct templates according to link data structure.
Rank templates using dataset’s inside statistics.
Md-Mizanur Rahoman, Ryutaro Ichise |An Automated Template Selection Framework for Keyword Query over Linked Data | 5
Introduction Related Work Proposed Method Experiments Conclusion and Future Work
Related Work
GoRelations: An Intuitive Query System for DBpedia. [Han, et al.,2011]
User needs to learn special kind of query formation technique.
Template-Based Question Answering Over RDF Data. [Unger, etal., 2012]
NL tool based QA system where NL tools sometime lead to incorrecttemplate construction.
Keyword-driven SPARQL Query Generation Leveraging BackgroundKnowledge. [Shekarpour, et al., 2011]
Needs to know some part of schema information such as instance orclass type information.Handle query with at most two keywords.
Md-Mizanur Rahoman, Ryutaro Ichise |An Automated Template Selection Framework for Keyword Query over Linked Data | 6
Introduction Related Work Proposed Method Experiments Conclusion and Future Work
Proposed Method
OverviewConstruct keyword fitted query templates.Rank all query templates and select the best query template.Construct final SPARQL query from the best query template.
Md-Mizanur Rahoman, Ryutaro Ichise |An Automated Template Selection Framework for Keyword Query over Linked Data | 7
Introduction Related Work Proposed Method Experiments Conclusion and Future Work
Proposed Method
Template managementAssumed for orderly given keywords.Follow linear binary progressive approach
Construct query templates for two adjacent keywords.Extend template construction linearly, if more than two keywords.
Md-Mizanur Rahoman, Ryutaro Ichise |An Automated Template Selection Framework for Keyword Query over Linked Data | 8
Introduction Related Work Proposed Method Experiments Conclusion and Future Work
Proposed Method
Template management (for two keywords)
Templates Arranged In
1 2
Query
s
Md-Mizanur Rahoman, Ryutaro Ichise |An Automated Template Selection Framework for Keyword Query over Linked Data | 9
Introduction Related Work Proposed Method Experiments Conclusion and Future Work
Proposed Method
Template management (for two keywords)
Templates Arranged In
1 2
Query
s
Md-Mizanur Rahoman, Ryutaro Ichise |An Automated Template Selection Framework for Keyword Query over Linked Data | 10
Introduction Related Work Proposed Method Experiments Conclusion and Future Work
Resource Manager
Extract and classify of keyword related resources
1 2
s
Md-Mizanur Rahoman, Ryutaro Ichise |An Automated Template Selection Framework for Keyword Query over Linked Data | 11
Introduction Related Work Proposed Method Experiments Conclusion and Future Work
Resource Manager
Extract related resources
1 2
s
Md-Mizanur Rahoman, Ryutaro Ichise |An Automated Template Selection Framework for Keyword Query over Linked Data | 12
Introduction Related Work Proposed Method Experiments Conclusion and Future Work
Resource Manager
Related resource for keyword, k
RR(k) = {s | ∃ < s, p, o >∈ KB∧(p ∈ rtag)∧(m(o, k) = true)}
wherertag is a set of resources representing tags such as label, name,prefLabel, etc.m(o, k) is a boolean function between the triple object o and thekeyword k, whether they match exactly or not.
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Introduction Related Work Proposed Method Experiments Conclusion and Future Work
Resource Manager
Calculate positional frequencies for related resource
1 2
s
Md-Mizanur Rahoman, Ryutaro Ichise |An Automated Template Selection Framework for Keyword Query over Linked Data | 14
Introduction Related Work Proposed Method Experiments Conclusion and Future Work
Resource Manager
Positional frequency for related resource, r
PFs(r) =| {< r , p, o >| ∃ < r , p, o >∈ KB} |
PFp(r) =| {< s, r , o >| ∃ < s, r , o >∈ KB} |
PFo(r) =| {< s, p, r >| ∃ < s, p, r >∈ KB} |
Md-Mizanur Rahoman, Ryutaro Ichise |An Automated Template Selection Framework for Keyword Query over Linked Data | 15
Introduction Related Work Proposed Method Experiments Conclusion and Future Work
Resource Manager
Perform type classification for related resource
1 2
s
Md-Mizanur Rahoman, Ryutaro Ichise |An Automated Template Selection Framework for Keyword Query over Linked Data | 16
Introduction Related Work Proposed Method Experiments Conclusion and Future Work
Resource Manager
Type classifying function uType for related resource, r
uType(r) =
{PR iff (PFp(r) > PFs(r)) ∧ (PFp(r) > PFo(r))NP otherwise
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Introduction Related Work Proposed Method Experiments Conclusion and Future Work
Resource Manager
Extract and classify of keyword related resources
1 2
s
Md-Mizanur Rahoman, Ryutaro Ichise |An Automated Template Selection Framework for Keyword Query over Linked Data | 18
Introduction Related Work Proposed Method Experiments Conclusion and Future Work
Proposed Method
Template management (for two keywords)
Templates Arranged In
1 2
Query
s
Md-Mizanur Rahoman, Ryutaro Ichise |An Automated Template Selection Framework for Keyword Query over Linked Data | 19
Introduction Related Work Proposed Method Experiments Conclusion and Future Work
Template Constructor
Construct query templates
Find all possible query templates for two adjacent keywords.Rank query templates using affinity matrix
Md-Mizanur Rahoman, Ryutaro Ichise |An Automated Template Selection Framework for Keyword Query over Linked Data | 20
Introduction Related Work Proposed Method Experiments Conclusion and Future Work
Template Constructor
Query TemplatePossible semantic query structure for given keywords.
Table: Query templates for resources r1 and r2
Template Group Query Template
TG1 <?uri , r1, r2 >< r2, r1, ?uri >
TG2 < r1, ?uri , r2 >< r2, ?uri , r1 >
<?uri , ?p1, r1 > <?uri , ?p2, r2 >< r1, ?p1, ?uri > < r2, ?p2, ?uri >< r1, ?p1, ?uri > <?uri , ?p2, r2 ><?uri , ?p1, r1 > < r2, ?p2, ?uri >
Md-Mizanur Rahoman, Ryutaro Ichise |An Automated Template Selection Framework for Keyword Query over Linked Data | 21
Introduction Related Work Proposed Method Experiments Conclusion and Future Work
Template Constructor
Query TemplatePossible semantic query structure for given keywords.
Table: Query templates for resources r1 and r2
Template Group Query Template
TG1 < ?uri , r1, r2 >< r2, r1, ?uri >
TG2 < r1, ?uri , r2 >< r2, ?uri , r1 >
< ?uri , ?p1, r1 > < ?uri , ?p2, r2 >< r1, ?p1, ?uri > < r2, ?p2, ?uri >< r1, ?p1, ?uri > < ?uri , ?p2, r2 >< ?uri , ?p1, r1 > < r2, ?p2, ?uri >
Md-Mizanur Rahoman, Ryutaro Ichise |An Automated Template Selection Framework for Keyword Query over Linked Data | 22
Introduction Related Work Proposed Method Experiments Conclusion and Future Work
Template Constructor
Query TemplatePossible semantic query structure for given keywords.
Table: Query templates for resources r1 and r2
Template Group Query Template
TG1 <?uri , r1, r2 >(PR − NP) < r2, r1, ?uri >
TG2 < r1, ?uri , r2 >< r2, ?uri , r1 >
<?uri , ?p1, r1 > <?uri , ?p2, r2 >< r1, ?p1, ?uri > < r2, ?p2, ?uri >< r1, ?p1, ?uri > <?uri , ?p2, r2 ><?uri , ?p1, r1 > < r2, ?p2, ?uri >
Md-Mizanur Rahoman, Ryutaro Ichise |An Automated Template Selection Framework for Keyword Query over Linked Data | 23
Introduction Related Work Proposed Method Experiments Conclusion and Future Work
Template Constructor
Query TemplatePossible semantic query structure for given keywords.
Table: Query templates for resources r1 and r2
Template Group Query Template
TG1 <?uri , r1, r2 >< r2, r1, ?uri >
TG2 < r1, ?uri , r2 >(NP − NP) < r2, ?uri , r1 >
<?uri , ?p1, r1 > <?uri , ?p2, r2 >< r1, ?p1, ?uri > < r2, ?p2, ?uri >< r1, ?p1, ?uri > <?uri , ?p2, r2 ><?uri , ?p1, r1 > < r2, ?p2, ?uri >
Md-Mizanur Rahoman, Ryutaro Ichise |An Automated Template Selection Framework for Keyword Query over Linked Data | 24
Introduction Related Work Proposed Method Experiments Conclusion and Future Work
Template Constructor
Affinity matrixQuery templates storing and ranking structure.
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TG1
TGq
TGq
TGq
TGq
TGq TGq
TGq
TGq
query template weight
<?uri kr kr >
<rk kr ?uri>
1,s 2,t
1,s2,tW2
rk1,1
rk1,s
rk1,m
rk2,1
rk2,t
rk2,n W1
. . . .
. . . .
(a)
(b)
Md-Mizanur Rahoman, Ryutaro Ichise |An Automated Template Selection Framework for Keyword Query over Linked Data | 25
Introduction Related Work Proposed Method Experiments Conclusion and Future Work
Template Constructor
Calculate weight of query template:
Frequency of query template fq(QT ).Frequency of resource r , given query template QT .
fq(r ,QT (r1, r2)) =
PFs(r) if r is on subject in QTPFp(r) if r is on predicate in QTPFo(r) if r is on object in QT
The final weight FW (QT ) of query template QT
FW (QT ) = fq(QT ) ∗ fq(r1,QT ) ∗ fq(r2,QT )
Md-Mizanur Rahoman, Ryutaro Ichise |An Automated Template Selection Framework for Keyword Query over Linked Data | 26
Introduction Related Work Proposed Method Experiments Conclusion and Future Work
Proposed Method
Template management (for two keywords)
Templates Arranged In
1 2
Query
s
Md-Mizanur Rahoman, Ryutaro Ichise |An Automated Template Selection Framework for Keyword Query over Linked Data | 27
Introduction Related Work Proposed Method Experiments Conclusion and Future Work
Best Template Selector
Select best query template:
Weight.Depth level.
How closely resources r1 and r2 are attached in triples for a querytemplate.for example
<?uri , r1, r2 > depth level is 1.<?uri , ?p1, r1 > <?uri , ?p2, r2 > depth level is 2.
Best query template in an affinity matrixlowest depth level and highest weight
Md-Mizanur Rahoman, Ryutaro Ichise |An Automated Template Selection Framework for Keyword Query over Linked Data | 28
Introduction Related Work Proposed Method Experiments Conclusion and Future Work
Query template conversion to SPARQL query
SPARQL query conversionPut variable resource ?uri in SELECT clausePut query template inside WHERE clause
Table: Related SPARQL queries for all query templates
TG SPARQL Query for QT
< ?uri, r1, r2 > SELECT ?uri WHERE {?uri r1 r2.}< r2, r1, ?uri > SELECT ?uri WHERE {r2 r1 ?uri.}< r1, ?uri, r2 > SELECT ?uri WHERE {r1 ?uri r2.}< r2, ?uri, r1 > SELECT ?uri WHERE {r2 ?uri r1.}
< ?uri, ?p1, r1 > < ?uri, ?p2, r2 > SELECT ?uri WHERE {?uri ?p1 r1. ?uri ?p2 r2.}< r1, ?p1, ?uri > < r2, ?p2, ?uri > SELECT ?uri WHERE {r1 ?p1 ?uri. r2 ?p2 ?uri.}< r1, ?p1, ?uri > < ?uri, ?p2, r2 > SELECT ?uri WHERE {r1 ?p1 ?uri. ?uri ?p2 r2.}< ?uri, ?p1, r1 > < r2, ?p2, ?uri > SELECT ?uri WHERE {?uri ?p1 r1. r2 ?p2 ?uri.}
Md-Mizanur Rahoman, Ryutaro Ichise |An Automated Template Selection Framework for Keyword Query over Linked Data | 29
Introduction Related Work Proposed Method Experiments Conclusion and Future Work
Proposed Method
Template management (for more than two keywords)
1 2 n Constructor
Md-Mizanur Rahoman, Ryutaro Ichise |An Automated Template Selection Framework for Keyword Query over Linked Data | 30
Introduction Related Work Proposed Method Experiments Conclusion and Future Work
Proposed Method
Template management (for more than two keywords)
1 2 n Constructor
Md-Mizanur Rahoman, Ryutaro Ichise |An Automated Template Selection Framework for Keyword Query over Linked Data | 31
Introduction Related Work Proposed Method Experiments Conclusion and Future Work
Best Template Constructor
Construct best query templates for every two adjacentkeywords
Keywords:
{k1, k2, k3, .., kn}Best query templates:
QT1 for {k1, k2}QT2 for {k2, k3}.........................QTn−1 for {kn−1, kn}
Md-Mizanur Rahoman, Ryutaro Ichise |An Automated Template Selection Framework for Keyword Query over Linked Data | 32
Introduction Related Work Proposed Method Experiments Conclusion and Future Work
Proposed Method
Template management (for more than two keywords)
1 2 n Constructor
Md-Mizanur Rahoman, Ryutaro Ichise |An Automated Template Selection Framework for Keyword Query over Linked Data | 33
Introduction Related Work Proposed Method Experiments Conclusion and Future Work
Comparator
Compare two adjacent best query templatesCompare by depth levels and weights.Retain retain query template
Query template that is lower depth and higher weight .
Forward not retained keyword to Refiner process
Keyword that is not retained by retained template.
For example:
Keywords: k1, k2, k3Best query templates: QT1 for {k1, k2} and QT2 for {k2, k3}Retain template: QT1, if QT1 has lower depth and higher weight thanQT2
Not retained keyword: k3
Md-Mizanur Rahoman, Ryutaro Ichise |An Automated Template Selection Framework for Keyword Query over Linked Data | 34
Introduction Related Work Proposed Method Experiments Conclusion and Future Work
Proposed Method
Template management (for more than two keywords)
1 2 n Constructor
Md-Mizanur Rahoman, Ryutaro Ichise |An Automated Template Selection Framework for Keyword Query over Linked Data | 35
Introduction Related Work Proposed Method Experiments Conclusion and Future Work
Refiner
Refine not retained keywordsRefine individual not retained keyword.Construct individual keyword based modified query template.
Table: Modified query templates for resources r1
Modified ModifiedTemplate Group Query Template
MTG1 <?uri , r1, ?o1 >MTG2 < r1, ?p1, ?uri >
<?uri , ?p1, r1 >
Find adjust query template by the frequency of modified querytemplates.
Md-Mizanur Rahoman, Ryutaro Ichise |An Automated Template Selection Framework for Keyword Query over Linked Data | 36
Introduction Related Work Proposed Method Experiments Conclusion and Future Work
Proposed Method
Template management (for more than two keywords)
1 2 n Constructor
Md-Mizanur Rahoman, Ryutaro Ichise |An Automated Template Selection Framework for Keyword Query over Linked Data | 37
Introduction Related Work Proposed Method Experiments Conclusion and Future Work
Merger
Merge of all retained and adjusted query templates
Keep variable resource identifier ?uri intack.Use different variable resource identifiers for others.
For example. if identifier ?p1 and ?p2 are already used, use anotheridentifier (e.g., ?p3).
Merge query templates at ?uri .
Convert merged query template to SPARQL query.
Md-Mizanur Rahoman, Ryutaro Ichise |An Automated Template Selection Framework for Keyword Query over Linked Data | 38
Introduction Related Work Proposed Method Experiments Conclusion and Future Work
Experiments
How does system perform in keyword query?
How does system perform comparing to other system(s)?
Md-Mizanur Rahoman, Ryutaro Ichise |An Automated Template Selection Framework for Keyword Query over Linked Data | 39
Introduction Related Work Proposed Method Experiments Conclusion and Future Work
Experimental Data
QALD-1 DBpedia Test set
Reason of choosing:
Real linked data implementation.Recognize linked data retrieval challenge.
Use 42 natural language questions out of 50.Construct keywords by considering question and underlying data.
Q# 29: In which films directed by Garry Marshall was Julia Robertsstarring?keywords: Film, starring, Julia Roberts, Director, Garry Marshall.
Md-Mizanur Rahoman, Ryutaro Ichise |An Automated Template Selection Framework for Keyword Query over Linked Data | 40
Introduction Related Work Proposed Method Experiments Conclusion and Future Work
Result Analysis
Table: Proposed system’s recall, precision, and F1 measure grouped bynumber of keywords.
# of # of Recall Precision F1 Measure*Keywords Questions (avg) (avg) (avg)
2 30 0.933 0.884 0.8993 8 0.472 0.442 0.4544 3 0.000 0.000 0.0005 1 1.000 1.000 1.000
Average 0.780 0.740 0.753
Md-Mizanur Rahoman, Ryutaro Ichise |An Automated Template Selection Framework for Keyword Query over Linked Data | 41
Introduction Related Work Proposed Method Experiments Conclusion and Future Work
Comparision with Other System
Table: Performance comparison between GoRelations[Han, et al., 2011] andour System
Recall Precision F1 Measure*
GoRelations 0.722 0.687 0.704Proposed System 0.780 0.740 0.753
Md-Mizanur Rahoman, Ryutaro Ichise |An Automated Template Selection Framework for Keyword Query over Linked Data | 42
Introduction Related Work Proposed Method Experiments Conclusion and Future Work
Conclusion and Future Work
ConclusionShow automated keyword-based linked data query using predefinedtemplate.Construct concrete guideline for template construction and templateranking.Show our implementation result for real linked implementation withsome comparsion.
Future Work
Explore automated ontology incorporation and feedback incorporation.Introduce more shophisticated keyword matching.Explore benefits of off-line statistical parameter inclusion.
Md-Mizanur Rahoman, Ryutaro Ichise |An Automated Template Selection Framework for Keyword Query over Linked Data | 43
Questions?
Md-Mizanur Rahoman, [email protected] Ichise, [email protected]
Md-Mizanur Rahoman, Ryutaro Ichise |An Automated Template Selection Framework for Keyword Query over Linked Data | 44