Inclusion of Temporal Semantics over Keyword-basedLinked Data Retrieval

Md-Mizanur Rahoman, Ryutaro Ichise

June 7, 2013

Outline

Introduction

Linked dataMotivationProblem and probable solution

Proposed Method

Query text processingSemantic query

Experiment

Conclusion

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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

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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

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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

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QA system [Rahoman, et al., 2012]

Take ordered keyword 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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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: music artist birth date on 29th December, 1960signal word: onQ-FKS: {music artist, birth date}Q-RKS: {on 29th December, 1960}

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Temporal semantics over linked data

Proposed systemQuery text processing

Setup ordering key between Q-FKS and Q-RKS [Saquete, et al., 2009]and, then format Q-RKS related temporal feature to a common format(i.e., TIMEX3)

Semantic queryExecute QA system [Rahoman, et al., 2012], format temporal featurerelated output to TIMEX3 and then filter formatted output

Phase 1: Query Text Processing

Phase 2: Semantic Query

Final Output

Query Keywords

Ordering Key Generator

QA System

with Time Filter

Q-FKS, Ordering Key,

Formatted Time

Time Formatter

Q-FKS, Ordering Key, Q-RKS

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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 December, 1960Signal word: onQ-FKS: {music artist, birth date}Q-RKS: {on 29th December, 1960}Ordering key: Q-FKS = Q-RKS

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Query text processing

Ordering key generatorDivide input keywords according to signal wordPut ordering key between Q-FKS and Q-RKS

Time formatterUse Stanford parser [Chang, et al., 2012] over Q-RKS and generateTIMEX3 formatted temporal values

Phase 1: Query Text Processing

Phase 2: Semantic Query

Final Output

Query Keywords

Ordering Key Generator

QA System

with Time Filter

Q-FKS, Ordering Key,

Formatted Time

Time Formatter

Q-FKS, Ordering Key, Q-RKS

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Semantic query

QA system with time filterExtract temporal feature related outputConsist 3 steps:

Step 1: Execute QA system over Q-FKSStep 2: Format part of temporal feature related output to TIMEX3Step 3: Filter Q-FKS related formatted output considering ordering keyand TIMEX3 value of Q-RKS

Phase 1: Query Text Processing

Phase 2: Semantic Query

Final Output

Query Keywords

Ordering Key Generator

QA System

with Time Filter

Q-FKS, Ordering Key,

Formatted Time

Time Formatter

Q-FKS, Ordering Key, Q-RKS

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Semantic query

Example

Q-FKS: {music artist, birth date}Q-RKS: {on 29th December, 1960}Formatted value of Q-RKS: {1960-12-29}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 Iikka Paananen ... 1960-12-29

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Experiment

Experimental Data

Question Answering over Linked Data 1 (QALD-1) open challengedata

Consist natural language questionsSorted out for questions which relate temporal feature in answering

DBPedia test case - 4 questionsMusicBrainz test case - 18 questions

Input

Ordered input keywords (with singal word)

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Performance of proposed QA system

Check average recall, average precision and average F1 measure foreach participant dataset

Participant # of Performance of proposed systemquestion set questions

Recall Precession F1 MeasureDBPedia QALD-1 4 1.000 1.000 1.000MusicBrainz QALD-1 18 0.765 0.765 0.765

DBPedia dataset achieve gold-standardPerformance drop over MusicBrainz dataset

QA system not able to generate Q-FKS related information

Successfully adapts

signal keyword, ordering key and Stanford parser over temporal featurerelated linked data information retrieval

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Conclusion

Our study

Adapt temporal semantics over an keyword-based linked data retrievalframeworkReduce data heteroginity by converting all temporal value to acommon formatShow implementation result for real linked implementation

Future work

Want to introduce more shophisticated keyword matching sincecurrent system depends on exact matching

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