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Inclusion of Temporal Semantics over Keyword-based Linked Data Retrieval
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Inclusion of Temporal Semantics over Keyword-based Linked Data Retrieval

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Temporal features, such as date and time or time of an event, always expose some concise semantics over any …

Temporal features, such as date and time or time of an event, always expose some concise semantics over any
kind of information retrieval, and so over linked data information retrieval. On one hand, we see, contemporary
research tries to adapt linked data information retrieval with easy and familiar keyword-based retrieval to hide
the complexity of data’s underlying technologies. On the other hand, we find, linked data information retrieval
perspective, most of them overlook the power of temporal feature inclusion. Considering the both, this study,
investigates the importance of temporal feature inclusion over linked data information retrieval. We propose a
keyword-based linked data information retrieval framework which can incorporate temporal features and can give
more concise results. Our investigation justify the significance of temporal feature inclusion over linked data
retrieval.

Published in Technology , Design
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  • 1. Inclusion of Temporal Semantics over Keyword-based Linked Data Retrieval Md-Mizanur Rahoman, Ryutaro Ichise June 7, 2013
  • 2. Outline Introduction Linked data Motivation Problem and probable solution Proposed Method Query text processing Semantic query Experiment Conclusion Md-Mizanur Rahoman, Ryutaro Ichise | 2
  • 3. Introduction December, 29, 1960 .... Linked data Represent knowledge using simple technique like subject, predicate, object Can be presented by graph-like structure Use loose data publishing strategy data publisher can publish data using their own data schema Can hold temporal feature related data date, time or event related information deathDate birthDate .... birthPlace Iikka Paananen profession music_artist profession birthPlace Indiana Michael Jackson deathDate birthDate 2009-06-25 29th August 1958 Md-Mizanur Rahoman, Ryutaro Ichise | 3
  • 4. Motivation December, 29, 1960 .... Temporal feature influences linked data retrieval Various challenge among temporal feature related information retrieval Link data hold data heterogeneity Link data allow all kind of temporal feature presentation strategies Very few study over temporal feature related linked data information retrieval deathDate birthDate .... birthPlace Iikka Paananen profession music_artist profession birthPlace Indiana Michael Jackson deathDate birthDate 2009-06-25 29th August 1958 Md-Mizanur Rahoman, Ryutaro Ichise | 4
  • 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 format Adapt a keyword-based linked data QA system [Rahoman, et al., 2012] for the formatted data Md-Mizanur Rahoman, Ryutaro Ichise | 5
  • 6. QA system [Rahoman, et al., 2012] Take ordered keyword as input Use some templates and tries to relate part of the linked dataset Construct templates for each two adjacent keywords Merge templates, if input keywords are more than two Example for keywords: music artist and birth date templates relation over dataset result December, 29, 1960 .... deathDate birthDate .... birthPlace Iikka Paananen profession music_artist profession ? birthPlace birthDate ? ? birthDate music_artist Indiana ? .. music_artist .. Michael Jackson deathDate birthDate 2009-06-25 29th August 1958 Iikka Paananen ... Michael Jackson ... Md-Mizanur Rahoman, Ryutaro Ichise | 6
  • 7. 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 restriction keywords (Q-RKS) Example question: music artist birth date on 29th December, 1960 signal word: on Q-FKS: {music artist, birth date} Q-RKS: {on 29th December, 1960} Md-Mizanur Rahoman, Ryutaro Ichise | 7
  • 8. Temporal semantics over linked data Proposed system Query 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 query Execute QA system [Rahoman, et al., 2012], format temporal feature related output to TIMEX3 and then filter formatted output Query Keywords Q-FKS, Ordering Key, Q-RKS Ordering Key Generator Q-FKS, Ordering Key, Formatted Time Time Formatter Phase 1: Query Text Processing Final Output QA System with Time Filter Phase 2: Semantic Query Md-Mizanur Rahoman, Ryutaro Ichise | 8
  • 9. Ordering key Order temporal feature attachment between Q-FKS and Q-RKS according to the signal word [Saquete, et al., 2009] Signal word In/On After Before ... Ordering key Q-FKS = Q-RKS Q-FKS > Q-RKS Q-FKS < Q-RKS ... Help filtering Q-FKS output by restricting temporal feature of Q-RKS Example Input keywords: music artist, birth date, on 29th December, 1960 Signal word: on Q-FKS: {music artist, birth date} Q-RKS: {on 29th December, 1960} Ordering key: Q-FKS = Q-RKS Md-Mizanur Rahoman, Ryutaro Ichise | 9
  • 10. Query text processing Ordering key generator Divide input keywords according to signal word Put ordering key between Q-FKS and Q-RKS Time formatter Use Stanford parser [Chang, et al., 2012] over Q-RKS and generate TIMEX3 formatted temporal values Query Keywords Q-FKS, Ordering Key, Q-RKS Ordering Key Generator Q-FKS, Ordering Key, Formatted Time Time Formatter Phase 1: Query Text Processing Final Output QA System with Time Filter Phase 2: Semantic Query Md-Mizanur Rahoman, Ryutaro Ichise | 10
  • 11. Semantic query QA system with time filter Extract temporal feature related output Consist 3 steps: Step 1: Execute QA system over Q-FKS Step 2: Format part of temporal feature related output to TIMEX3 Step 3: Filter Q-FKS related formatted output considering ordering key and TIMEX3 value of Q-RKS Query Keywords Q-FKS, Ordering Key, Q-RKS Ordering Key Generator Q-FKS, Ordering Key, Formatted Time Time Formatter Phase 1: Query Text Processing Final Output QA System with Time Filter Phase 2: Semantic Query Md-Mizanur Rahoman, Ryutaro Ichise | 11
  • 12. 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 1 2 3 Result Iikka Paananen ... December,29,1960 Michael Jackson ... 29th August,1958 Iikka Paananen ... 1960-12-29 Michael Jackson ... 1958-08-29 Iikka Paananen ... 1960-12-29 Md-Mizanur Rahoman, Ryutaro Ichise | 12
  • 13. Experiment Experimental Data Question Answering over Linked Data 1 (QALD-1) open challenge data Consist natural language questions Sorted out for questions which relate temporal feature in answering DBPedia test case - 4 questions MusicBrainz test case - 18 questions Input Ordered input keywords (with singal word) Md-Mizanur Rahoman, Ryutaro Ichise | 13
  • 14. Performance of proposed QA system Check average recall, average precision and average F1 measure for each participant dataset Participant question set DBPedia QALD-1 MusicBrainz QALD-1 # of questions 4 18 Performance of proposed system Recall 1.000 0.765 Precession 1.000 0.765 F1 Measure 1.000 0.765 DBPedia dataset achieve gold-standard Performance 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 feature related linked data information retrieval Md-Mizanur Rahoman, Ryutaro Ichise | 14
  • 15. Conclusion Our study Adapt temporal semantics over an keyword-based linked data retrieval framework Reduce data heteroginity by converting all temporal value to a common format Show implementation result for real linked implementation Future work Want to introduce more shophisticated keyword matching since current system depends on exact matching Md-Mizanur Rahoman, Ryutaro Ichise | 15