Discovery Hub: on-the-fly linked data exploratory search
Nicolas Marie, Fabien Gandon, Myriam Ribière
Florentin Rodio, Dam...
CONTEXT
PROPOSITION
EVALUATION
CONCLUSION
Search…
ExploratoryLookup
???« members » + « The Beatles»
Precise information need Fuzzy information need
you are here
related work…
Aemoo Kaminskas & al. LED MORE Seevl Yovisto
Purpose Explorator
y search
Cross-domain
recommendation
Explora...
composite interest queries
knowing my interest for X and Y what can I
discover/learn which is related to all these resourc...
CONTEXT
PROPOSITION
EVALUATION
CONCLUSION
principle
results selection
ranking
sorting/categorization
explanations
1
2
3
4
http://dbpedia.org/resource/Ken_Loach
…dbp...
research questions
1. How can we discover linked resources of interest
to be explored ?
2. How to address remote LOD sourc...
semantic adaptation of spreading activation
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example of semantic spreading activation
Album, Band, Film,
Musical Artist, Music
Genre, Person, Radio
Station, Single, Song,
Television Show
Company, Election, Fi...
research questions
1. How can we discover linked resources of interest
to be explored ?
2. How to address remote LOD sourc...
sampling algorithm
1.sparql endpoint = http://xxx/sparql
2.seeds = xxx//The_Beatles, xxx/Ken_Loach
3. compute the propagat...
iterative import
Local Kgram instance
Online LOD source
magic numbers
1.sparql endpoint = http://xxx/sparql
2.seeds = xxx//The_Beatles, xxx/Ken_Loach
3. compute the propagation d...
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KendallTau
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Convergence, top 100 results maxPulse
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Response time histogram
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seconds
Queries response time histogram
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20
research questions
1. How can we discover linked resources of interest
to be explored ?
2. How to address remote LOD sourc...
Discovery Hub 1.0
1. Start from what you like
or are interested in
3. Be redirected on third-party
platforms to continue t...
Discovery Hub 1.0
short demo
CONTEXT
PROPOSITION
EVALUATION
CONCLUSION
composite queries
• randomly combining Facebook likes of 12 users
• two queries for each participants to judge the top 20 ...
overall
•61.6% of the results were rated as strongly relevant
or relevant by the participants.
•65% of the results were ra...
Explanatory features evaluation
Common prop. Wiki-based Graph-based OverallCommon prop. Wiki-based Graph-based Overall
Ver...
comparison SSA(Discovery Hub) vs. sVSM (More)
• Hypothesis 1: SSA gives results at least as relevant as sVSM.
• Hypothesis...
CONTEXT
PROPOSITION
EVALUATION
CONCLUSION
•semantic spreading activation
algorithm coupled to a graph
sampling to address remote
LOD sources.
•faceted browsing and
...
current work:
- propagation over multiple data sources in parallel.
- redesign of the interface: Discovery Hub 2.0 release...
multi-lingual mode
dbpedia:Charles_Baudelaire sameAs fr.dbpedia:Charles_Baudelaire
French
English
http://discoveryhub.co/
@discovery_hub
werarediscoveryhub@gmail.com
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Discovery Hub: on-the-fly linked data exploratory search

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I-Semantics 2013 (#isem2013) presentation of Discovery Hub on-the-fly linked data exploratory search engine.

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Discovery Hub: on-the-fly linked data exploratory search

  1. 1. Discovery Hub: on-the-fly linked data exploratory search Nicolas Marie, Fabien Gandon, Myriam Ribière Florentin Rodio, Damien Legrand
  2. 2. CONTEXT PROPOSITION EVALUATION CONCLUSION
  3. 3. Search… ExploratoryLookup ???« members » + « The Beatles» Precise information need Fuzzy information need you are here
  4. 4. related work… Aemoo Kaminskas & al. LED MORE Seevl Yovisto Purpose Explorator y search Cross-domain recommendation Exploratory search on ICT domain Film recommendati on Musical recommendati on Video exploratory search Data DBpedia EN + external services DBpedia EN subset DBpedia + external services DBpedia EN subset DBpedia EN subset DBpedia EN+DE subset Multi-domain Yes Cross two domains No No, cinema No, music Yes Query Entity search Entity selection in a pre-processed list Entity search Entity search Entity recognition from Youtube. Entity recognition in keywords Algorithm EKP filtered view weighted activation DBpedia Ranker sVSM algo. DBrec algorithm Set of heuristics Ranking No Yes Yes Yes Yes Yes Explanations Wikipedia- based Path-based No Shared prop. Shared properties No Offline proc. Yes , EKP part Yes Yes Yes Yes Yes goal: domain-independent, customizable, on the fly, remote sources
  5. 5. composite interest queries knowing my interest for X and Y what can I discover/learn which is related to all these resources? The Beatles Ken Loach
  6. 6. CONTEXT PROPOSITION EVALUATION CONCLUSION
  7. 7. principle results selection ranking sorting/categorization explanations 1 2 3 4 http://dbpedia.org/resource/Ken_Loach …dbpedia.org/resource/The_Beatles
  8. 8. research questions 1. How can we discover linked resources of interest to be explored ? 2. How to address remote LOD sources for this? 3. How to present and explain the results to the user for an exploratory objective ? http://fr.dbpedia.org/sparql http://es.dbpedia.org/sparql http://it.dbpedia.org/sparql
  9. 9. semantic adaptation of spreading activation 1 0,2 0,2 0,2 0,2 0,1 0,6 0,6 1 0,8 1
  10. 10. example of semantic spreading activation
  11. 11. Album, Band, Film, Musical Artist, Music Genre, Person, Radio Station, Single, Song, Television Show Company, Election, Film, Journalist, Musical Artist, Newspaper, Office Holder, Organisation, Politician, School, Single, Television Show, Writer propagation domain propagation domain
  12. 12. research questions 1. How can we discover linked resources of interest to be explored ? 2. How to address remote LOD sources for it? 3. How to present and explain the results to the user for an exploratory objective ? http://fr.dbpedia.org/sparql http://es.dbpedia.org/sparql http://it.dbpedia.org/sparql
  13. 13. sampling algorithm 1.sparql endpoint = http://xxx/sparql 2.seeds = xxx//The_Beatles, xxx/Ken_Loach 3. compute the propagation domain (w(i,o)) 4. find a path between the seeds 5. import path nodes & their neighbors 6. for(i=1; i<=maxPulse; i++){ 7. pulse(); 8. if(sampleSize <= maxSampleSize){ 9. extend the sample 10. } 11.}
  14. 14. iterative import Local Kgram instance Online LOD source
  15. 15. magic numbers 1.sparql endpoint = http://xxx/sparql 2.seeds = xxx//The_Beatles, xxx/Ken_Loach 3. compute the propagation domain (w(i,o)) 4. find a path between the seeds 5. import path nodes & their neighbors 6. for(i=1; i<=maxPulse; i++){ 7. pulse 8. if(sampleSize <= maxSampleSize){ 9. extend the sample 10. } 11.}
  16. 16. 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 0 500 1000 1500 2000 2500 3000 3500 4000 4500 0 5000 10000 15000 20000 KendallTau ResponseTime Triples loading limit Sample size influence on top 100 results, maxSampleSize
  17. 17. Convergence, top 100 results maxPulse 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 0 10 20 30 40 50 60 70 80 90 100 1 2 3 4 5 6 7 8 9 10 Kendall-Tau Sharedresults Iterations
  18. 18. Response time histogram 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 seconds Queries response time histogram 5 20
  19. 19. research questions 1. How can we discover linked resources of interest to be explored ? 2. How to address remote LOD sources for it? 3. How to present and explain the results to the user for an exploratory objective ? http://fr.dbpedia.org/sparql http://es.dbpedia.org/sparql http://it.dbpedia.org/sparql
  20. 20. Discovery Hub 1.0 1. Start from what you like or are interested in 3. Be redirected on third-party platforms to continue the discovery experience Book 2. Explore, understand, disco ver …
  21. 21. Discovery Hub 1.0
  22. 22. short demo
  23. 23. CONTEXT PROPOSITION EVALUATION CONCLUSION
  24. 24. composite queries • randomly combining Facebook likes of 12 users • two queries for each participants to judge the top 20 results - The result interests me [Strongly Disagree … Strongly Agree ] - The result is unexpected [Strongly Disagree … Strongly Agree ] Very interesting Not interesting at all
  25. 25. overall •61.6% of the results were rated as strongly relevant or relevant by the participants. •65% of the results were rated as strongly unexpected or unexpected. •35.42% of the results were rated both as strongly relevant or relevant and strongly unexpected or unexpected.
  26. 26. Explanatory features evaluation Common prop. Wiki-based Graph-based OverallCommon prop. Wiki-based Graph-based Overall Very Helpful Not helpful at all
  27. 27. comparison SSA(Discovery Hub) vs. sVSM (More) • Hypothesis 1: SSA gives results at least as relevant as sVSM. • Hypothesis 2: SSA has a weaker degradation than sVSM (better end-lists). • Hypothesis 3: results less relevant but newer to users at the end of the lists. • Hypothesis 4: advanced search gives better results compared to standard query. Measure Algo Rank Mean St. Dev. Relevance SSA 1-10 1.54 0.305 11-20 1.28 0.243 sVSM 1-10 1.42 0.294 11-20 0.93 0.228 Discovery SSA 1-10 1.10 0.247 11-20 1.21 0.228 sVSM 1-10 1.14 0.251 11-20 1.50 0.205 0 0.5 1 1.5 2 2001 Erin Term Princess Fight Overall SCORE SSA sVSM
  28. 28. CONTEXT PROPOSITION EVALUATION CONCLUSION
  29. 29. •semantic spreading activation algorithm coupled to a graph sampling to address remote LOD sources. •faceted browsing and multiple explanations of the results. •on-going extensive user evaluation •publicly available http://discoveryhub.co Discovery Hub : enabling exploratory search starting from several interests using linked data sources 1 0,2 0,2 0,2 0,6 0,6 1 0,8 1
  30. 30. current work: - propagation over multiple data sources in parallel. - redesign of the interface: Discovery Hub 2.0 released perspective: other applications of semantic spreading activation
  31. 31. multi-lingual mode dbpedia:Charles_Baudelaire sameAs fr.dbpedia:Charles_Baudelaire French English
  32. 32. http://discoveryhub.co/ @discovery_hub werarediscoveryhub@gmail.com

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