TLDRet: A Temporal Semantic Facilitated Linked DataRetrieval Framework
Md-Mizanur Rahoman, Ryutaro Ichise
November 29, 2013
Outline
Introduction
MotivationProblem and probable solution
Proposed Retrieval Framework: TLDRet
Query text processingSemantic query
Experiment
Conclusion
Md-Mizanur Rahoman, Ryutaro Ichise | 2
Introduction
Linked data
Represent knowledge using simpletechnique like subject, predicate, objectCan be presented by graph-like structureUse loose data publishing strategy
data publisher can publish data usingtheir own data schema
Can hold temporal feature related data
date, time or event related information
Iikka Paananen
music_artist
....
December,
29, 1960
birthDate
profession
Michael Jackson
Indiana
29th August
1958
birthDate
profession
deathDate
birthPlace
....
birthPlace
deathDate
2009-06-25
Md-Mizanur Rahoman, Ryutaro Ichise | 3
Temporal feature related data
Two types of temporal features [Rula et al., 2012]
Document-centric
Time points are associated to the RDF triplesUsed to inform modification of the RDF triples
Fact-centric
Time points inform various factsUsed to present historical informatione.g., <res:Michael Jackson prop:birthDate 29-Aug-1958>
Current research investigates fact-centric temporal features
Md-Mizanur Rahoman, Ryutaro Ichise | 4
Motivation
Temporal feature influences linked dataretrieval
Various challenge among temporalfeature related information retrieval
Link data hold data heterogeneityLink data allow all kind of temporalfeature presentation strategies
Very few study over temporal featurerelated linked data information retrieval
Iikka Paananen
music_artist
....
birthDate
profession
Michael Jackson
IndianabirthDate
profession
deathDate
birthPlace
....
birthPlace
deathDate
2009-06-2529th August
1958
December,
29, 1960
Md-Mizanur Rahoman, Ryutaro Ichise | 5
Problem & Solution
Retrieval of temporal feature related information
Problem
Difficult in adaptation over linked data perspective
Solution
Convert all temporal features to a common formatAdapt a keyword-based linked data QA system [Rahoman et al., 2012]for the formatted data
Md-Mizanur Rahoman, Ryutaro Ichise | 6
QA system [Rahoman et al., 2012]
Take ordered keywords as inputUse some templates and tries to relate part of the linked dataset
Construct templates for each two adjacent keywordsMerge templates, if input keywords are more than two
Examplefor keywords: music artist and birth date
templates relation over dataset result
?
music_artist
birthDate
?
music_artist
?
birthDate
?
.. ..
Iikka Paananen
music_artist
....
birthDate
profession
Michael Jackson
IndianabirthDate
profession
deathDate
birthPlace
....
birthPlace
deathDate
2009-06-2529th August
1958
December,
29, 1960
Iikka Paananen ...Michael Jackson ...
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Extension of QA system
QA system
Not sufficient in capturing temporal semanticsRequire at least two input keywordsGenerate only one particular information, not the other associatedinformation
e.g., {War, involved, President Jackson} informs the name of the wars,not the time of the involvements
TLDRet
Can generate information for single keyword
Single keyword can hold temporal information e.g., {World War I}
Generate all associated information that are related to keywords
Particular information might not hold temporal information
Md-Mizanur Rahoman, Ryutaro Ichise | 8
Adaptation of temporal semantics
Assumption
Question hold temporal feature indicating word called signal word[Saquete et al., 2009]Signal word prior keywords considered as question focus keywords(Q-FKS)Signal word follower keywords considered as question restrictionkeywords (Q-RKS)Example
question:Q1. music artist birth date on 29th August, 1958Q2. music artist birth date during World War Isignal word: on (Q1), during (Q2)Q-FKS: {music artist, birth date} (Q1 and Q2)Q-RKS: {on 29th August, 1958} (Q1), {during World War I} (Q2)
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TLDRet: Temporal Linked Data RetrievalFramework
Proposed systemQuery text processing
Divide input keywords into Q-FKS and Q-RKS, annotate Q-RKS relatedtemporal feature to a common format (i.e., TIMEX3)
Semantic queryExecute extended QA system, annotate Q-FKS related temporal featureto TIMEX3 and then impose a time filter between Q-RKS and Q-FKSrelated annotated output
Input Keywords
with Temporal Features
Q-FKS, Q-RKS
Q-RKS
Time Converter
Q-FKS, Signal Word
Q-RKS_exp
Input Divider
Extended-QA System with Time Filter
Step 1Step 2Step 3
Q-FKS Input Keywords
Related Result
TIMEX3 Annotated Q-FKS
Input Keywords Related ResultFiltered Output
Final Result
Phase 1
: Q
uery
Text
Pro
cessin
g
Phase 2
: Sem
anti
c
Query
Md-Mizanur Rahoman, Ryutaro Ichise | 10
TLDRet: Temporal Linked Data RetrievalFramework
Proposed systemQuery text processing
Divide input keywords into Q-FKS and Q-RKS, annotate Q-RKS relatedtemporal feature to a common format (i.e., TIMEX3)
Semantic queryExecute extended QA system, annotate Q-FKS related temporal featureto TIMEX3 and then impose a time filter between Q-RKS and Q-FKSrelated annotated output
Input Keywords
with Temporal Features
Q-FKS, Q-RKS
Q-RKS
Time Converter
Q-FKS, Signal Word
Q-RKS_exp
Input Divider
Extended-QA System with Time Filter
Step 1Step 2Step 3
Q-FKS Input Keywords
Related Result
TIMEX3 Annotated Q-FKS
Input Keywords Related ResultFiltered Output
Final Result
Phase 1
: Q
uery
Text
Pro
cessin
g
Phase 2
: Sem
anti
c
Query
Md-Mizanur Rahoman, Ryutaro Ichise | 11
Query text processing
Input divider
Divide input keywords into Q-FKS and Q-RKS according to the signalword
Divided keywords are used to set up time precedence with the help ofordering key
Q-RKS time converter
Decide whether Q-RKS holds explicit temporal value (e.g., date ortime) or event informationAnnotate Q-RKS to TIMEX3 by a parser, if Q-RKS holds explicittemporal value
DATE/TIME/DURATION type named entity recognition can produceTIMEX3 value
Execute extended QA system and annotate Q-RKS related temporalfeature to TIMEX3, if Q-RKS holds event information
Md-Mizanur Rahoman, Ryutaro Ichise | 12
Ordering key
Order temporal feature attachment between Q-FKS and Q-RKSaccording to the signal word [Saquete et al., 2009]
Signal word Ordering keyIn/On Q-FKS = Q-RKSAfter Q-FKS > Q-RKSBefore Q-FKS < Q-RKS... ...
Help filtering Q-FKS output by restricting temporal feature ofQ-RKS
Example
Input keywords: music artist, birth date, on 29th August, 1958Signal word: onQ-FKS: {music artist, birth date}Q-RKS: {on 29th August, 1958}Ordering key: Q-FKS = Q-RKS
Md-Mizanur Rahoman, Ryutaro Ichise | 13
Semantic query
Extended QA system with time filterExtract temporal feature related outputConsist 3 steps:
Step 1: Execute extended QA system over Q-FKSStep 2: Annotate part of Q-FKS temporal feature related output toTIMEX3Step 3: Filter Q-FKS annotated output considering ordering key andTIMEX3 value of Q-RKS
Input Keywords
with Temporal Features
Q-FKS, Q-RKS
Q-RKS
Time Converter
Q-FKS, Signal Word
Q-RKS_exp
Input Divider
Extended-QA System with Time Filter
Step 1Step 2Step 3
Q-FKS Input Keywords
Related Result
TIMEX3 Annotated Q-FKS
Input Keywords Related ResultFiltered Output
Final Result
Phase 1
: Q
uery
Text
Pro
cessin
g
Phase 2
: Sem
anti
c
Query
Md-Mizanur Rahoman, Ryutaro Ichise | 14
Filtering Q-FKS annotated output
Q-RKS imposes the basis of filtering constraint by selectingtemporal picking point (TPP)
TPP decides whether filtering should be done for the point of time orfor the interval
Ordering key Ordering key Temporal Picking Pointtype (TPP/TPPi/TPPf)Point of time Q-FKS < Q-RKS Pick lowest Q-RKS TIMEX3 value among all such values as TPP
Q-FKS = Q-RKS Pick every Q-RKS TIMEX3 values as TPP... ...
Interval Q-RKSi <= Q-FKS Pick lowest Q-RKS TIMX3 value among all such values as TPPi<= Q-RKSf Pick highest Q-RKS TIMEX3 value among all such values as TPPf... ...
Filtered output retains output of Q-FKS after filtering Q-FKSTIMEX3 value and TPP
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Semantic query
Example
Q-FKS: {music artist, birth date}Q-RKS: {on 29th August, 1958}Formatted value of Q-RKS: {1958-08-29}Ordering key: {Q-FKS = Q-RKS}
Execution of QA system with time filter
Step Result1 Iikka Paananen ... December,29,1960
Michael Jackson ... 29th August,19582 Iikka Paananen ... 1960-12-29
Michael Jackson ... 1958-08-293 Michael Jackson ... ... 1958-08-29
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Experiment
Experimental Data
Question Answering over Linked Data 1 and 2 (i.e., QALD-1 andQALD-2) open challenge data
Consist natural language questionsSorted out for questions which relate temporal feature in answering
Questions from DBPedia test case4 (QALD-1) and 9 (QALD-2)Questions from MusicBrainz test case18 (QALD-1) and 20 (QALD-2)
Input
Ordered input keywords (with signal word)
Tool
Stanford parser
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Performance of TLDRet over QALD temporalfeature related questions
Check average precision, average recall and average F1 measure foreach participant dataset
Participant question set # of questions Performance of TLDRetPrecision Recall F1 Measure
DBPedia QALD-1 4 1.000 1.000 1.000QALD-2 9 1.000 1.000 1.000
Average 1.000 1.000 1.000
MusicBrainz QALD-1 18 0.722 0.722 0.722QALD-2 20 0.750 0.750 0.750
Average 0.737 0.737 0.737
DBPedia dataset achieve gold-standard
Successfully adaptssignal keyword, ordering key and parser over temporal feature relatedlinked data information retrieval
MusicBrainz dataset achieve low performance
QA system not able to generate Q-FKS related information
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Performance comparison with other systems
Evaluate performance for DBPedia QALD-2 temporal featurerelated questions
Evaluate average precision, average recall and average F1 measurefor each challenge participant systems
System Average Precision Average Recall Average F1 MeasureSemSeK 0.400 0.400 0.400Alexandria 0.000 0.000 0.000MHE 0.400 0.400 0.400QAKiS 0.000 0.000 0.000TLDRet 1.000 1.000 1.000
TLDRet outperforms other systemsSuccessful adaptation of temporal semantics increases system’susability
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Conclusion
TLDRet
Adapt temporal semantics over an keyword-based linked data retrievalframeworkReduce data heterogeneity by converting all temporal value to acommon formatShow implementation result for real linked implementation
Future work
Want to exploit a retrieval framework that can adapt document-centrictemporal semantics
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Questions?
Md-Mizanur Rahoman, [email protected] Ichise, [email protected]
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