RDFox is an optimized RDF triple store and parallel Datalog reasoner that supports OWL 2 RL. It features low memory consumption per triple, high scalability with CPU threads, and integration as a Java or C++ library. Evaluation shows RDFox achieves near-linear speedup on multi-core systems and outperforms competitors for query answering large datasets.

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RDFox
. Optimised RDF Triple Store
. Parallel Datalog reasoner (OWL 2 RL)
. Faster than all competitors
O F O F
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RDFox
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Features
. Low memory consumption per triple
. Highly scalable w.r.t. CPU threads
. Easy integration as Java or C++ library
. SPARQL 1.1 querying support
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Preliminaries
A Knowledge Base (KB) comprises
. Data — e.g. RDF triples
. Ontology — e.g. Datalog Program or OWL 2 ontology
Ontologies relate notions and concepts; used for
. Ontology Based Data Access (OBDA)
. Enhanced query answering
Example
Data Hotel(Hilton), B&B(Blue Ox), Hostel(YMCA)
Ontology B&B (x) → Accom(x), Hotel (x) → Accom(x), Hostel (x) → Accom(x)
ery Accom(?x) ≈ ‘Which accomodations are there?’
Answers Hilton, Blue Ox, YMCA.
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Motivation
Core service of KBs: ery Answering !
. Boom-up Approach: compute all logical consequences (the
materialisation) of the KB beforehand
+ short query times (no reasoning at query time)
– higher memory usage (materialisation has to be stored)
Datalog is a rule & query language
. Underpins Semantic Web languages (RDFS, pD∗, OWL 2 RL, SWRL)
. OWL 2 EL reducible to Datalog
. Recursive language
. Increasing popularity in Semantic Web Community
Modern computer systems have
. Multi-core processors
. More and more RAM (>1TB)
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Our Novel Solution
RDFox has
. Optimised indexing of RDF data — fast reasoning/querying
. Almost lock-free update of indexes — low contention
. Per-fact parallelisation of materialisation — high scalability
RDFox supports
. Multithreading — exploits modern hardware
. RDF — standardised wide spread data format
. Datalog — covers a range of OWL fragments
. SPARQL 1.1 — standardised versatile query language
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Evaluation
Shows speedup factor achieved by n threads w.r.t. 1 thread
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2 Threads
Speedup
.. Clar..osL . Clar.osLE . DBpe.diaL . DBpe.diaLE LUBML01K . LUBM.U01K
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2 Threads
Speedup
. UOBM.L01K . UOBM.U010 . LUBM.LE01K . LUBM.L05K . LUBM.LE05K . LUBM.U05K
ClarosL ClarosLE DBPediaL DBPediaLE LUBML01K LUBMU01K
Triples (mill) 96 555 118 1530 323 219
Mem (GB) 4.2 18.0 6.1 52 9.3 11.1
import (s) 58 367 215
timeKB(1) 2477 4128 158 8457 73 135
timeKB(max n) 127 285 24 602 7 10
UOBML01K UOBMU010 LUBMLE01K LUBML05K LUBMLE05K LUBMU05K
Triples (mill) 430 36 333 911 1661 1094
Mem (GB) 20.2 1.1 13.8 49.0 75.5 53.3
import (s) 783 6 415 2999
timeKB(1) 532 2738 947 442 4859 635
timeKB(max n) 31 168 71 39 745 54.3
Performance
. Still rises with hyperthreading (>16 cores)
. Almost linear speed-up up-to 8 threads — flaens due to bus conjestion
. Outruns all competitors [1]
Publication with technical details and in depth information:
[1 ] Materialisation of Datalog Programs in Centralised, Main-Memory RDF
Systems, Motik et al., AAAI2014.
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Current Work & Outlook
Active Development
. Constantly developed and maintained
. Parallel reasoning with owl:sameAs added
. Incremental reasoning implemented
Future work
. Parallelising incremental reasoning
. Improving query processing
. Using RDFox in distributed computer networks
Visit www.rdfox.org
Boris Motik, Yavor Nenov, Robert Piro, Ian Horrocks forname.lastname@cs.ox.ac.uk