Parallel and Distributed Reasoning for RDF and OWL 2

Transcription

1 Parallel and Distributed Reasoning for RDF and OWL 2 Nanjing University, 6 th July, 2013 Department of Computing Science University of Aberdeen, UK

2 Ontology Landscape Related DL-based standards (OWL, OWL2) are established together with standard semantic annotation language RDF Many DL reasoners available FaCT++, pellet, HermiT, RacerPro, TrOWL The user community is growing fast Swoogle searches over 10,000 online ontologies Larger and larger ontologies SNOMED has 379,691 concepts more and more complicated ontologies FMA (Foundational Model of Anatomy, OWL DL) has 41,647 concepts and 123,564 axioms Impact: From domain applcations to software engineering 2

3 OWL 2: Web Ontology Language OWL 2 Full Undecidable OWL 2 DL SROIQ 2NExpTime- Complete SHOIN OWL 1 DL NExpTime- Complete OWL 2 RL OWL 2 EL EL++ PTime- Complete OWL 2 QL DL-Lite In AC 0 3

4 Three Approaches to Reasoning in OWL 2 1. Sound and complete reasoning in OWL 2 DL 2. Sound and complete reasoning in tractable profiles (RL, QL and EL) 3. Approximate reasoning in OWL 2 DL (from DL to tractable profiles)

5 OWL 2 DL: ABox Reasoning Challenging Even for Ontologies in Profiles 5

6 Materialisation Evaluations (REL reasoner in TrOWL) 6

7 OWL-DBC OWL-DBC Suite TrOWL/REL OWL files OWL- DBC API Jena Adapter for Oracle Database Oracle Spatial and Graph and Oracle Database

8 Question: How to Further Improve Scalability and Efficiency 8

9 Parallel and Distributed Reasoning 1. What is the relation with the three reasoning approaches for OWL 2? 2. Can we separate schema (TBox) reasoning from data (ABox) reasoning?

10 Q1: Approach 2 will be a key 1. Sound and complete reasoning in OWL 2 DL 2. Sound and complete reasoning in tractable profiles (RL, QL and EL) 3. Approximate reasoning in OWL 2 DL (from DL to tractable profiles)

11 Question: Can We Separate Schema Reasoning and Data Reasoning in RDF? 11

12 RDF Inference Rules

13 RDF Inference Rules (exc. non-authorised)

14 Implementation of RDF Inference Rules

15 Evaluation GPU based RDF Reasoning

16 Q2: Separating TBox from ABox Applicable for which language? QL RDF ABox can be distributed to multiple computation nodes Key challenge: distributed joints

17 Distributed Summarisation [Fokoue et al. 2012] C1 B1 U1 source 1 Course Busi ness Prof C4 taughtby B2 C2 taughtby U2 Prof1 source 2 Course Busi ness Prof C3 Prof1 U1 CS2 U3 source 3 hash(c) = hash(h) CS Prof taughtby HofD Find courses taught by a CS professor who is a Head of Dept. C Course H Head of Dept B Business Prof CS CS Prof U University taughby in Prof1 is Head of D: src3 Prof1 is CS Prof: src2 Course CS Prof Independent Local Summaries: Sources independently build and maintain their local summary of their ABox A local summary node represents individuals with same explicit concepts and same hash value Property of summary: completeness preserving approximation of ABox University Summary 1 University Summary 2 Summary 3 University 17

18 Summary Filtering Correctness: University Summary 1 hash(c) Simulate = hash(h) distributed join: Join variable p p1, Course p2, p3 Find courses taught by a CS professor who is a Head of Dept. Answers w.r.t. the subset of the KB corresponding to the hash(s) = hash(n) Course Course Course?c filtered summary are the same as those w.r.t. the original KB. tuaghtby Busi Busi CS HofD ness ness CS Prof?p Prof Prof Prof If query q is minimal w.r.t. its number of terms, then the summary type filtering type is optimal. University University CS HofD Summary 2 Constraints on Summary distributed 3 join Ptof l p1, p2, and p3: same hash Optimality: Course Course?c tuaghtby?h hash hash Relaxed query hash Busi ness Prof Busi ness Prof CS Prof HofD CS Prof?p1?p2?p3 type type Filtered Summary CS Prof HofD 18

19 Experimental Evaluation Result 1: Significant reduction in size Result 2: Significant Performance Gain over with state-of-the-art n Efficient distributed summarization n New approach optimizations outperform state-of-the-art systems In decentralized environment, efficient ontological reasoning to bridge the semantic gap between data and queries can be achieved through a novel KB distributed summarization 19

20 Question: Can We Separate Schema Reasoning and Data Reasoning in ELH bottom,r+? 20

21 Challenge 1 Property hierarchy can not be precomputed if r. then r is a sub-property of all the other properties

22 Challenge 2 A simply approach by reusing the TBox algorithm Internalising the ABox with nominals Treating singleton nominals as atomic concepts However, the data size is usually much bigger than the schema

23 Contexts and Workers Introducing redundancies Worker 1 Worker 2 {a} A scheduled processed has to be maintained in A.scheduled and A.processed, waiting for the derivation of.

24 Separating TBox and ABox Reasoning à C.scheduledà C.processed contains no nominal! Can always be computed earlier than Can be used as side conditions in rules. C does not need to be a context in

25 Separating TBox and ABox Reasoning When the filler is NOT a nominal Extending to ABox rules Applicable NOT applicable nominals nominals When the filler is a nominal

26 Evaluation - Multiple CPU Core Benchmark VICODI ontology NotGalen TBox + synthetic ABox generated by SyGENiA Environment AWS EC2 cloud computing, 64-bit Linux, 7G RAM, each worker GHz Off-the-shelf Reasoners PEL

27 Summary Parallel and distributed reasoning focus: tractable languages (RDF, RL, QL, EL) Separation of TBox and ABox reasoning RDF is harder than people thought Summarisation is useful Future directions Summarisation with distribute TBox reasoning Combinations of parallel and distributed reasoning More expressive DLs via approximation Ontology dynamics: what-if questions/streams

28 Parallel and Distributed Reasoning for RDF and OWL 2 Thank you! questions? Acknowledgement: Achille Fokoue Norman Heino Yuan Ren