1. Distributed Semantic Associations Matt Perry Maciej Janik Conrad Ibanez

2. Motivation Semantic Web, by its web nature is distributed Knowledge will be stored in multiple stores, multiple ontologies Search for semantic paths will have to include many knowledge sources

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

4. Assumptions K-hop limited ρ-path search Entity disambiguation across KBs

5. Problems Search Efficiency How to continue a search from one KB to another When to stop a search in one KB and start it in another How to piece together path fragments

6. Approach Super-Peer Peer KB

7. Border Nodes KB1 KB2 Border Node

8. Distance Between Borders KB2 KB1 KB3 Dist(KB1KB2, KB1KB3) = 3 Dist(KB1KB2, KB2KB3) = 1 Dist(Start, KB1KB2) = 1 Dist(End, KB1KB3) = 1 Start End

9. Query Plan Graph Basic Idea: 1.Add start and end node to QPG 2.Do path search (<= K) through QPG 3.Convert the paths to a set of queries

10. Converting Paths To Queries ρ-path (Start, End, 12) KB1 – ρ-path (Start, KB1/KB2, 6) KB2 - ρ-path (KB1/KB2, KB2/KB3, 5) KB3 - ρ-path (KB2/KB3, End, 7) KB1/KB2 Start KB2/KB3End 4 2 3

11. Super-Peer Level QPG

12. Integration of SP graph and Peer Graph

13. Whole Process 1.Peer asks SP for Query Plan 2.SP finds endpoints and adds them to SP QPG 3.SP finds all Paths through SP QPG 4.SP converts these Paths in subquery plan requests for each SP 5.Each SP uses the process recursively on its peer-level QPG to form peer-level query plan 6.The union of the peer-level query plans is the final query plan 7.The peer then executes this plan

14. Test sets Agent 2054 Athlete 4343 Athlete 2805 Team 8286 Agent 1632 Agent 566 Agent 2457 Agent 2215 Agent 717 Agent 2054 Athlete 6778 Athlete 7028 Athlete 6988 Team 8430 Agent 1808 Agent 2215 Agent 1194 Agent 717 Team 8405 Agent 2054 Athlete 2951 Athlete 6041 Athlete 3108Agent 2418 Test 3Test 1 Test 2 Border 1/2/3 Border 1/2