1. Peer-to-Peer Discovery of Semantic Associations Matthew Perry, Maciej Janik, Cartic Ramakrishnan, Conrad Ibanez, Budak Arpinar, Amit Sheth 2 nd International Workshop on Peer-to-Peer Knowledge Management, San Diego, California, July 17, 2005

2. From ….. Finding things To ….. Finding out about things Relationships! Semantic Discovery 1 1. http://lsdis.cs.uga.edu/semdis

3. Semantic Associations Relationship-centric nature of Semantic Web data models We can ask questions about the relationships between objects How is entity A related to entity B? Applications –National Security – Insider Threat 1 –Improved Searching – Bio Patent Miner 2 1.B. Aleman-Meza, P. Burns, M. Eavenson, D. Palaniswami, A. Sheth, An Ontological Approach to the Document Access Problem of Insider Threat, Proceedings of the IEEE Intl. Conference on Intelligence and Security Informatics (ISI-2005), May 19-20, 2005 2.Sougata Mukherjea, Bhuvan Bamba, BioPatentMiner: An Information Retrieval System for BioMedical Patents, VLDB 2004.

4. Semantic Associations &r1 &r5 &r6 worksFor “Matt” “Perry” fname lname Semantic Association “LSDIS Lab” name “The University of Georgia” name associatedWith ρ -path Define a set of operators ρ for querying complex relationships between entities (Semantic Associations) 1 1.Adapted From: Kemafor Anyanwu, and Amit Sheth, ρ-Queries: Enabling Querying for Semantic Associations on the Semantic Web, The Twelfth International World Wide Web Conference, Budapest, Hungary, pp. 690-699.

5. Uniqueness of Semantic Association Queries Simple query specification (only the two endpoints) Doesn’t require extensive knowledge of schema ρ-path (A, B)

6. Difficult to express with existing Query Languages SELECT ?startURI, ?property_1, ?endURI FROM (?startURI ?property_1 ?endURI) SELECT ?startURI, ?property_1, ?endURI FROM (?endURI ?property_1 ?start) SELECT ?startURI, ?property_1, ?x, ?property_2, ?endURI FROM (?startURI ?property_1 ?x)(?x ?property_2 ?endURI) WHERE ?startURI ne ?x && ?endURI ne ?x SELECT ?startURI, ?property_1, ?x, ?property_2, ?endURI FROM (?startURI ?property_1 ?x)(?endURI ?property_2 ?x) WHERE ?startURI ne ?x && ?endURI ne ?x SELECT ?startURI, ?property_1, ?x, ?property_2, ?endURI FROM (?x ?property_1 ?startURI)(?x ?property_2 ?endURI) WHERE ?startURI ne ?x && ?endURI ne ?x SELECT ?startURI, ?property_1, ?x, ?property_2, ?endURI FROM (?x ?property_1 ?startURI)(?endURI ?property_2 ?x) WHERE ?startURI ne ?x && ?endURI ne ?x RDQL: Find paths of length at most 2 from startURI to endURI

7. Why Semantic Associations in P2P? Data on the web by its nature is distributed Knowledge will be stored in multiple stores and multiple ontologies Search for semantic paths will have to include many knowledge sources In the spirit of the Semantic Web (collaborative knowledge discovery)

8. Contributions Super-Peer Architecture for Querying Semantic Associations Knowledgebase Borders and Distances between Borders Query Planning Algorithm based on Knowledgebase Borders and Distances

9. Assumptions Pair-wise mapping of resources between peers (solution to Entity Disambiguation / Reference Reconciliation problem) We use URIs to solve Entity Disambiguation problem Main focus is Query Planning over P2P network Not concerned with fault tolerance, details of network formation, etc. at this point

10. &r4 &r8 &r2 Bank Account no FFlyer Payment paidby typeOf(instance) subClassOf(isA) subPropertyOf paidby “Bill” “Jones” &r7 &r1 fname lname purchased “John” “Smith” Ticket Flight forflight String number purchased Client fname lname String Passenger FFNo String Customer Cash fflierno creditedto fflierno creditedto paidby holder &r9 float amount purchased for &r5&r6 purchased for CCard fname lname “Jeff” “Brown ” fname lname “XYZ123” &r3 String ffid &r11 holder paidby holder RDF Instance Graph

11. ρ -path Problem ( k -hop limited) Given: –An RDF instance graph G, vertices a and b in G, an integer k Find: –All simple, undirected paths p, with length less than or equal to k, which connect a and b

12. Distributed ρ -path problem: Find all paths from a start node to an end node over the distributed RDF graphs Knowledge bases - ontologies

13. What do we need? Efficiently explore node neighborhoods When to stop a search in one peer and continue it in another Determine the search distance in each peer Determine which peers to include in the search

14. Peer Approach Super-Peer Peer KB Peer KB Peer KB Peer KB Peer KB Peer KB RDF data store (sesame, bhrams) ρ-path (a, b, k) returns subgraph No data store Responsible for Query Planning ρ-path ρ-sub-plan ρ-plan ρ-path subgraph

15. Knowledgebase Borders Peer 1 Peer 2 Border Node Overlap (Peer_1:Peer_2 Border)

16. Distance Between Borders Peer 3 Peer 1 Peer 2 dist (P1:P2, P1:P3) = 3 dist (P1:P2, P2:P3) = 1 Dist (P1:P3, P2:P3) = 1 Start End P1:P3 P2:P3 P1:P2 Border node Query end point

17. Query Planning Graph Directed Graph Node for each distinct border For each pair of connected borders, create 2 edges (one in each direction) Weight is the minimum of the minimum distances (reported by peers) –For example you can get from A:B to A:B:C through either A or B

18. A B C Borders AB AC BC ABC Minimum Distances dist (AB, BC) = 4 dist (AB, AC) = 3 dist (AB, ABC) = 2 dist (BC, AC) = 5 dist (BC, ABC) = 3 dist (AC, ABC) = 2 dist (AB, BB) = 3 dist (AC, AC) = 3 dist (BC, BC) = 2 dist (ABC, ABC) = ∞ Query Planning Graph AB AC ABC BC 3 3 2 4 2 3 5 3 2

19. Using the Query Planning Graph end start A C B 1) Find Start and End Points 2) Compute Distances to Borders 4 2 2 2 2 3 Example Query: r-path (start, end, 10)

20. 3) Add this Information to QPG AB AC ABC BC 3 3 2 4 2 3 5 3 2 end start 2 3 2 4 2 2 4) Find all paths from start to end (including cycles) <= k (10) In this case 22 paths

21. 5) Convert Set of Paths to Set of Queries start – 2  Peer_A:Peer_B – 3  Peer_A:Peer_C – 3  end end start A C B 2 3 3 2 2 2 start – 2  Peer_B:Peer_C – 2  Peer_B:Peer_C – 2  end

22. Converting Paths to Queries Each edge (pair of endpoints) represents a query For example, ρ -path (start, Peer_A:Peer_B, 2) start 2 3 What is the correct hop-limit? hop-limit = edge weight + (k – path weight) ρ-path (start, Peer_A:Peer_B, 4) ρ-path (Peer_A:Peer_B, Peer_A:Peer_C, 5) ρ-path (Peer_A:Peer_C, end, 5) k = 10 end 3 A:B A:C

23. Find the maximum hop-limit for each pair of end points PairHop-limit (start, Peer_A:Peer_B)5 (start, Peer_A:Peer_B:Peer_C)7 (start, Peer_B:Peer_C)8 (Peer_A:Peer_B, Peer_A:Peer_C)5 (Peer_A:Peer_B, Peer_A:Peer_B:Peer_C)5 (Peer_A:Peer_B, Peer_A:Peer_B)3 (Peer_A:Peer_B, Peer_B:Peer_C)6 (Peer_A:Peer_C, Peer_A:Peer_B:Peer_C)3 (Peer_A:Peer_C, Peer_B:Peer_C)6 (Peer_A:Peer_C, end)5 (Peer_B:Peer_C, end)8 (Peer_B:Peer_C, Peer_B:Peer_C)6 (Peer_B:Peer_C, Peer_A:Peer_B:Peer_C)5 (Peer_A:Peer_B:Peer_C, end)6

24. Which Peer gets each query? ρ-path (Peer_B:Peer_A, Peer_A:Peer_C, 5) Peer_A Peer_C Peer_B 5 Peer_A ρ-path (Peer_B:Peer_C, Peer_B:Peer_C, 5) Peer_B and Peer_C

25. Final Query Plan Queries for Peer_A FROM: Peer_A:Peer_B:Peer_C TO: Peer_A:Peer_C Hop Limit: 3 FROM: Peer_A:Peer_B TO: Peer_A:Peer_C Hop Limit: 5 FROM: Peer_A:Peer_B TO: Peer_A:Peer_B:Peer_CHop Limit: 5 FROM: Peer_A:Peer_B TO: Peer_A:Peer_B Hop Limit: 3 Queries for Peer_B FROM: Peer_B:Peer_C TO: Peer_B:Peer_C Hop Limit: 6 FROM: Peer_B:Peer_C TO: start Hop Limit: 8 FROM: Peer_A:Peer_B TO: start Hop Limit: 5 FROM: Peer_A:Peer_B TO: Peer_A:Peer_B:Peer_C Hop Limit: 5 FROM: Peer_A:Peer_B TO: Peer_A:Peer_B Hop Limit: 3 FROM: Peer_A:Peer_B:Peer_C TO: Peer_B:Peer_C Hop Limit: 5 FROM: Peer_A:Peer_B TO: Peer_B:Peer_C Hop Limit: 6 FROM: Peer_A:Peer_B:Peer_C TO: start Hop Limit: 7 Queries for Peer_C FROM: Peer_B:Peer_C TO: end Hop Limit: 8 FROM: Peer_B:Peer_C TO: Peer_B:Peer_C Hop Limit: 6 FROM: Peer_A:Peer_C TO: Peer_B:Peer_C Hop Limit: 5 FROM: Peer_A:Peer_B:Peer_C TO: end Hop Limit: 6 FROM: Peer_A:Peer_B:Peer_C TO: Peer_A:Peer_C Hop Limit: 3 FROM: Peer_A:Peer_C TO: end Hop Limit: 5 FROM: Peer_A:Peer_B:Peer_C TO: Peer_B:Peer_C Hop Limit: 5

26. Query Execution at Peer Input: Set of Queries: { ρ-path ({uri, …}, {uri, …}, k), …} Algorithm: Graph Traversal of Main Memory representation Bi-directional BFS Results in a set of statements Output: Union of each set of statements

27. Query Execution at Peer Peer does not enumerate paths Returns a subgraph (set of triples) Benefits –Eliminates redundant data transfer –Saves computation time

28. Scalability: Multiple Super-Peers Super-Peer_1 Peer_B Peer_A Peer_C Super-Peer_3 Super-Peer_2 Super-Peer/Super-Peer Borders Super-Peer_1:Super-Peer_2 Super-Peer_1:Super-Peer_3 Super-Peer_2:Super-Peer_3 Super-Peer/Peer Borders Peer_B:Super-Peer_2 Peer_A:Super-Peer_3 Peer_C:Super-Peer_3 Super-Peer_1

29. Integration of SP graph and Peer Graph A:B A:B:C B:C A:C 2 4 3 2 3 5 A:SP3 B:SP2 C:SP3 SP1:SP3 SP1:SP2 2 0 0 0 4 2 3 3 2 4 5 4 Super-Peer_1’s new Peer-Level QPG

30. Query Planning Algorithm SP2 SP1 SP3 start end B A C 1) Find start and end points 2) Compute distances to borders D E

31. SP2:SP3 SP1:SP3SP1:SP2 start end 4) Find all directed paths <= k connecting start to end in the Super-Peer QPG 10 2 6 3 6 6 3 4 3 4 2 start – 6  SP1/SP3 – 2  SP1/SP3 – 2  end start – 6  SP1/SP3 – 2  end start – 3  SP1/SP2 – 6  end start – 10  end k = 10 3) Add temporary information for endpoints (both peer and super-peer QPG) Super-Peer QPG

32. 5) Form a list of sub-query-plan requests for each super-peer Super-Peer_1 FROM: start TO: end Hop-Limit: 10 FROM: start TO: Super-Peer_1:Super-Peer_2 Hop-Limit: 4 FROM: SuperPeer_1:Super-Peer_2 TO: end Hop-Limit: 7 FROM: start TO: Super-Peer_1:Super-Peer_3 Hop-Limit: 8 FROM: Super-Peer_1:Super-Peer_3 TO: Super-Peer_1:Super-Peer_3 Hop-Limit: 2 FROM: Super-Peer_1:Super-Peer_3 TO: end Hop-Limit: 4 Super-Peer_3 FROM: Super-Peer_1:Super-Peer_3 TO: Super-Peer_1:Super-Peer_3 Hop-Limit: 2

33. 7) Each super-peer goes through the previous process on its peer QPG to form a list of ρ-path queries for its peers 8) Querying peer now communicates directly with other peers to execute the ρ-path queries Queries for Peer B: FROM: A:B TO: A:B Hop Limit: 3 FROM: A:B TO: B:C Hop Limit: 6 FROM: A:B:C TO: B:SP2 Hop Limit: 4 FROM: A:B TO: B:SP2 Hop Limit: 2 FROM: A:SP2 TO: start Hop Limit: 4 FROM: B:C TO: B:SP2 Hop Limit: 5 FROM: B:C TO: start Hop Limit: 8 FROM: B:C TO: B:C Hop Limit: 6 FROM: A:B TO: start Hop Limit: 5 FROM: A:B TO: A:B:C Hop Limit: 5 FROM: A:B:C TO: start Hop Limit: 7 FROM: A:B:C TO: B:C Hop Limit: 5 Queries for Peer A: FROM: A:B TO: A:B Hop Limit: 3 FROM: A:B:C TO: A:SP3 Hop Limit: 4 FROM: A:B TO: A:SP3 Hop Limit: 6 FROM: A:B TO: A:C Hop Limit: 5 FROM: A:B TO: A:B:C Hop Limit: 5 FROM: A:B:C TO: A:C Hop Limit: 3 FROM: A:C TO: A:SP3 Hop Limit: 3 Queries for Peer C: FROM: A:B TO: B:C Hop Limit: 5 FROM: A:B TO: endHop Limit: 5 FROM: A:B:C TO: end Hop Limit: 6 FROM: B:C TO: end Hop Limit: 8 FROM: B:C TO: B:C Hop Limit: 6 FROM: B:C TO: C:SP3 Hop Limit: 6 FROM: A:C TO: C:SP3 Hop Limit: 3 FROM: A:B:C TO: A:C Hop Limit: 3 FROM: A:B:C TO: B:C Hop Limit: 5 FROM: A:B:C TO: C:SP3 Hop Limit: 4 FROM: C:SP3 TO: end Hop Limit: 4 Queries for Peer E: FROM: E:SP1 TO: E:SP1 Hop Limit: 2

34. Conclusions and Future Work Presented a Query-Planning Algorithm for r-path queries over distributed data set Problems –Efficiently compute node neighborhoods –How to continue searches across KBs –How to check for the many possible cases –How to determine search length in each KB

35. Conclusions and Future Work Future Work –Performance Testing –Effect of relative border size –Different criteria for group formation –How to accommodate other types of queries

36. Questions?

37. Computing Borders Super-Peer maintains Sorted Map of URIs Peer Border –Traverse new list and update Sorted Map Super Peer Border –Don’t care about other URIs not in this group –Keep total data transferred at a minimum

38. Forming the Network SP1 SP2 SP3 P2 P1 P New I want to join the network 1) Broadcast 2) I am a super-peer 3) List of URIs

39. Forming the Network SP1 SP2 SP3 P2 P1 P New 4) SPs compute overlap O(n log k) (maintain border information) 5) Send overlap count to new peer 6) New peer picks one super-peer accept reject

40. Forming the Network SP1 SP2 SP3 P2 P1 P New 9) Here are your borders 7) SP1 updates permanent uri index 8) SP1 recomputes SP borders 10) Peers send minimum distances

41. Computing Super-Peer Borders C E L M U A B G J S SP1 SP2 (SP1, C, false) (SP2, G, false) (SP1, H, false) H H (SP2, J, true) (SP1, K, false) (SP2, R, true) R R (SP1, U, true) (SP2, null, null) H H R R K K K K

42. A B C Super- Peer 3 Super- Peer 2 Super-Peer 1 Super-Peer Level QPG Minimum Distances dist (AB, BC) = 4 dist (AB, AC) = 3 dist (AB, ABC) = 2 dist (BC, AC) = 5 dist (BC, ABC) = 3 dist (AC, ABC) = 2 dist (AC, A/SP3) = 3 dist (AB, A/SP3) = 4 dist (ABC, A/SP3) = 3 dist (AC, C/SP3) = 2 dist (BC, C/SP3) = 4 dist (ABC, C/SP3) = 2 dist (AB, B/SP2) = 2 dist (BC, B/SP2) = 2 dist (ABC, B/SP2) = 2 Borders AB AC BC A/SP3 B/SP2 C/SP3