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Edgard Marx, Amrapali Zaveri, Diego Moussallem and Sandro Rautenberg | DBtrends: Exploring query logs for ranking RDF data
- 1. DBtrends Exploring Query Logsfor Ranking RDF Data AKSW Edgard Marx, Amrapali Javeri, Diego Moussallem, Sandro Rautenberg 12th International Conference on Semantic Systems
- 2. Outline • Motivation • Background •Ranking using Query Logs • Evaluation • Results • Discussion • Conclusion • Future Works 2 AKSW
- 3. 3 Personal Data EnterpriseData Motivation Open Data AKSW
- 4. 4 http://linkeddatacatalog.dws.informatik.uni-annheim.de/state/ "The size ofLOD by 2014 was 31 billion triples" "Facebook users generates 2.7 billion Like actions per day and 300 million new photos are uploaded daily" Josh Constine, 2012 We Have Data "Google Processing 20,000 Terabytes A Day, And Growing" Erick Schonfeld, 2008 techcrunch.com techcrunch.com AKSW Motivation
- 5. Not all of datais relevant We Have Data Motivation 5 AKSW
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- 9. Scenarios Search Machine LearningLink Discovery 9 AKSW Motivation
- 10.
- 11. Things 11 Background AKSW Web of Data •Semantic Search • Entity Search • Question Answering • Named Entity Recognition • Link Discovery • Machine Learning Use RDF Data E=MC²
- 12. Ranking Functions (Types) 12 "Giveme all persons" AKSW Retrieve Processing & Ranking Background ...
- 13. Ranking Functions (Types) 13 "Giveme all persons" AKSW Retrieve Persons Sort Processing & Ranking Answer Background ...
- 14. Ranking Functions (Types) 14 "Giveme all persons" AKSW Retrieve Persons Sort Processing & Ranking Answer Background ...Query dependent Query independent
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- 16. Ranking 16 AKSW Background Page et al.1999 2001 Leeet al. Web of Data
- 17. Ranking RDF Data 17 AKSW Background Pageet al. 2011 1999 Cheng et al. (Property) 2001 Lee et al. Web of Data
- 18. Ranking RDF Data 18 AKSW Background Pageet al. Thalhammer et al. 2011 1999 2014 Cheng et al. (Property) 2001 Lee et al. Web of Data
- 19. Benchmarks 19 DBtrends Benchmark (Marx,2016) • 60 users from different countries (USA, India) • 9 entity ranking functions applied to DBpedia Knowledge Base • Users sort relevant classes, properties and entities extracted from the top twenty entities belonging to the top four classes • Task were executed using Amazon Mechanical Turk Previous Benchmarks • Not public available • Evaluate performace of 30 profiles AKSW Background
- 20. Why use querylogs? AKSW 20 Ranking using Query Logs
- 21. Why use querylogs? AKSW 21 Ranking using Query Logs
- 22. Why use querylogs? AKSW 22 Ranking using Query Logs Query Logs search...
- 23. Why use querylogs? AKSW 23 Ranking using Query Logs
- 24. Why use querylogs? • Query logs provide relevant information about user's preference • They refer to the real-world entities E=MC² AKSW 24 Ranking using Query Logs
- 25. Questions • How tomap real-world entities to Web of Data? • How to measure it's relevance? • Where to find a good and trustable query log? AKSW 25 Ranking using Query Logs
- 26. How to mapreal world resources? • Rocha et al. (2004) • Ding et al. (2005) • Hogan et al. (2006) • Alsarem et al (2015) AKSW 26 Ranking using Query Logs Query Logs search... Web of Data
- 27. How to measurethe resource's relevance? AKSW 27 Ranking using Query Logs • Users search (more often) for things that are relevant • Query logs register how often something is searched • Query logs can be used for better estimate resource's relevance by looking how often it is searched
- 28. Where to finda good and trustable query log? AKSW 28 Ranking using Query Logs
- 29. Where to finda good and trustable query log? AKSW 29 Ranking using Query Logs
- 30. Where to finda good and trustable query log? • Public API • Filters Geographic • Country • State • City Period Day Week Month Year AKSW 30 Ranking using Query Logs
- 31. DBtrends Ranking Function AKSW 31 Rankingusing Query Logs
- 32. DBtrends Ranking Function AKSW 32 Rankingusing Query Logs 36 Trendsdbr:New_York_City “New York” dbo:City dbo:Place 2 1 1 • First, the labels of the entities are extracted and used to acquire the search history in query logs e.g. GoogleTrends ( )2-
- 33. DBtrends Ranking Function 18 36 Trendsdbr:New_York_City “NewYork” dbo:City dbo:Place 1 2 3 4 9 • First, the labels of the entities are extracted and used to acquire the search history in query logs e.g. GoogleTrends ( ) • Thereafter, the entity ranks are used as a base to propagate the rank to the classes ( )3 4- 2- AKSW 1 33 Ranking using Query Logs
- 34. Entity Ranking Functions •DBtrends • MIXED-RANK • DB-IN • DB-OUT • DB-RANK • PAGE-IN • PAGE-OUT • PAGE-RANK • E-PAGE-IN • SEO-PA • SHARED-LINKS + Evaluation 34 AKSW
- 35. Property/Class Ranking Functions • Instances •Instances Property Class AKSW 35 Evaluation • Relin • RandomRank • Instances • Instances
- 36. Results AKSW • PAGE-RANK • E-PAGE-IN •SHARED-LINKS • SEO-PA • DB-OUT • PAGE-IN • PAGE-OUT • DB-IN • DB-RANK 36 Evaluation Entity
- 37. Results AKSW • MIXED-RANK • PAGE-RANK •E-PAGE-IN • SHARED-LINKS • SEO-PA • DB-OUT • PAGE-IN • DBtrends • PAGE-OUT • DB-IN • DB-RANK 37 Evaluation Entity
- 38. Discussion AKSW • Functions thattake into consideration external information provide more insights about resource's relevance • RDF Links reflect natural connections rather than resouce's relevance • MIXED-RANK • PAGE-RANK • E-PAGE-IN • SHARED-LINKS • SEO-PA • DB-OUT • PAGE-IN • DBtrends • PAGE-OUT • DB-IN • DB-RANK Entity 38 Evaluation
- 39. Discussion AKSW • There isno pattern in the impact distribution of query longs • Queries (not necessarly) help to improve a ranking functions • Internal agreement ~63% 39 Evaluation Entity
- 40. Results AKSW • RandomRank • Relin •Instances • Instances • Instances • Instances Property Class 40 Evaluation
- 41. Discussion AKSW • RandomRank • Relin •Instances • Instances • Internal agreement ~37% • Ranks are very sparse • Not conclusive 41 Evaluation Property
- 42. Discussion AKSW • Internal agreement~67% • Instances • Instances 42 Evaluation Class
- 43. Discussion AKSW dbo:PopulatedPlace dbo:Settlement dbo:Place owl:Thing A simple sort canbe very effective 43 Evaluation dbo:PopulatedPlace dbo:Settlement dbo:Place owl:Thing • Instances • Instances Class
- 44. Discussion AKSW • Confidence inexecuting the tasks: Indians 90% Americans 60% • Ranks produced by Indians were more sparse • Abstract entities appear before entities 44 Evaluation Caviats
- 45. Summary AKSW • Entity Rankingfunctions produce better results when considering external information • A simple sort of the number of instances can be very effective for ranking classes • Query logs can (not necessarily) improve entity ranking functions 45 Evaluation
- 46. Benchmark AKSW • Benchmark • Rankingfunctions • Library (Java) 46 Evaluation dbtrends.aksw.org
- 47. Future Works AKSW • Extendthe evaluation to other countries and ranking functions • Evaluate the impact of contex-aware ranking functions • Use others similarity ranking functions 47
- 48.