TLDRet: A Temporal Semantic Facilitated Linked Data Retrieval Framework

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


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


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


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


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 ...


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


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)


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


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


Q-FKS, Q-RKS

Q-RKS

Time Converter

Q-FKS, Signal Word

Q-RKS_exp

Input Divider


Step 1Step 2Step 3


Related Result



Final Result

Phase 1

: Q

uery

Text

Pro

cessin

g

Phase 2

: Sem

anti

c

Query


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


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


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


Q-FKS, Q-RKS

Q-RKS

Time Converter

Q-FKS, Signal Word

Q-RKS_exp

Input Divider


Step 1Step 2Step 3


Related Result



Final Result

Phase 1

: Q

uery

Text

Pro

cessin

g

Phase 2

: Sem

anti

c

Query


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


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


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


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


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


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


Questions?

Md-Mizanur Rahoman, [email protected] Ichise, [email protected]


Technology

TLDRet: A Temporal Semantic Facilitated Linked Data Retrieval Framework