=Paper=
{{Paper
|id=Vol-2180/paper-41
|storemode=property
|title=gOWL: A Fast Ontology-Mediated Query Answering
|pdfUrl=https://ceur-ws.org/Vol-2180/paper-41.pdf
|volume=Vol-2180
|authors=Chenhong Meng,Xiaowang Zhang,Guohui Xiao,Zhiyong Feng,Guilin Qi
|dblpUrl=https://dblp.org/rec/conf/semweb/MengZXFQ18
}}
==gOWL: A Fast Ontology-Mediated Query Answering==
https://ceur-ws.org/Vol-2180/paper-41.pdf
gOWL: A Fast Ontology-Mediated Query Answering
Chenhong Meng1,5 , Xiaowang Zhang1,5,∗ , Guohui Xiao3 , Zhiyong Feng2,5 , and
Guilin Qi4
1
School of Computer Science and Technology, Tianjin University, Tianjin 300350, China,
2
School of Computer Software,Tianjin University, Tianjin 300350, China
3
Faculty of Computer Science, Free University of Bozen-Bolzano, Bolzano I-39100, Italy
4
School of Computer Science and Engineering, Southeast University, Nanjing 211189, China
5
Tianjin Key Laboratory of Cognitive Computing and Application, Tianjin 300350, China
∗
Corresponding author.
Abstract. This poster shows an ontology-mediated query answering system (gOWL)
based on pure materialization to avoid query rewriting online. gOWL shows that
answering queries over a partial materialization of the chase is complete for a
large fragment of practical queries with bounded depth. From a system engineer-
ing perspective, the materialization approach allows us to design a modular ar-
chitecture to integrate off-the-shelf efficient SPARQL query engines. The poster
will be based on DBpedia and UOBM datasets. We will compare the performance
of gOWL with PAGOdA, Ontop, and Pellet (with speedup up to three orders of
magnitude).
1 Introduction
OMQ (Ontology-mediated Query) is a core reasoning task in many applications [1].
OMQs have been studies intensively on lightweight ontology languages, that have canon-
ical model properties. There are basically two approaches for query answering, namely,
materialization-based and query rewriting-based. Materialization-based approaches nor-
mally compute and materialize the chase first and then execute the queries over the ma-
terialization, such as RDFox [7] and PAGOdA [8]. However, materialization is often
infeasible when the chase contains infinitely existentially entailed elements. Instead,
query writing-based approaches avoid materializing the chase but rewrite input queries
by compiling the consequence of the reasoning into the query, such as QuOnto [2],
Clipper [4] and Ontop [3]. Query rewriting comes at the cost of query rewriting and
query evaluation at runtime, and the possibility of missing optimization opportunity at
the data level.
In this poster, we adopt a pure materialization-based approach which allows us to
design a modular architecture to integrate off-the-shelf efficient SPARQL query en-
gines. We implement the proposed approach in a prototype gOWL, and build an OMQ
systems gOWL-3X by employing RDF-3X. The preliminary encouraging experiments
on DBpedia and UOBM show that gOWL outperforms PAGOdA, Ontop, and Pellet.
2 Framework of gOWL
The approach proposed in this Section has been implemented in the gOWL system 6 .
The framework of gOWL contains three modules, namely, Query Processor, Normal-
ized Model Constructor, and Query Execution shown in the following figure.
gOWL Query Execution
SPARQL Query SPARQL
Query Processor Graph SPARQL
Query Model SPARQL Engine Query
Result
Engine
Normalized Selector API Selector (Centralized/
KB RDF Distributed)
Model Dataset
Constructor
Fig. 1: The framework of gOWL
Query Processor Query Processor is a module to compute the depth of a query by
applying a depth-first-search method. We use dep(q) to denote the depth of q, which
represents the length of maximal certain path in q. The nodes in the path must be starting
of a quantified-free variable of q and all other nodes are quantified variables.
Normalized Model Constructor In Normalized Model Constructor, we compute n-
n 0 n
step universal models UK (i.e., UK . . . UK ) for given a KB K = (T , A) a natural number
0
n. n represents the count of expanded steps from ABox. For example, UK means to
1
expand 0 step from ABox, which equivalent to itself; UK means to expand 1 step from
0 0 0
UK (i.e., ABox), the expansion condition is (T , UK ) |= ∃ R1 (a) but R1 (a, b) 6∈ UK ,
0 n (n−1)
for all b ∈ Ind(UK ); UK means to expand 1 step from UK (i.e., n-step from ABox),
(n−1) (n−1)
the expansion condition is (T , UK ) |= ∃ R1 (a) but R1 (a, b) 6∈ UK , for all
(n−1)
b ∈ Ind(UK ).
Intuitively speaking, n-step universal models of a KB are hierarchical extensions of
the ABox via the TBox. In fact, based on the statistical analysis of practical SPARQL
queries, over 96% of queries contain at most 7 triple patterns (i.e.,7 triples in a SPARQL
query) [5]. Therefore, we only consider n no more than 7 in this poster.
Query Execution The module of Query Execution contains four parts, namely Model
Selector, SPARQL API, Engine Selector, and SPARQL Query Engine. Model Selector
i
is used to select a suitable UK for a given query that i = dep can be satisfied. Through
SPARQL API, the query and dataset are passed on Engine Selector which utilizes the
information of them and the characteristics of each engine to recommend the suitable
one. Finally we employ SPARQL Query Engine to return solutions.
6
https://github.com/liulovemeng/gOWL.git
3 Experiments and evaluations
The experiments are carried out on a machine running Linux, which has 4 CPUs with
6 cores and 64GB memory. RDF-3X is used as the underlying SPARQL query engines.
We utilized UOBM and DBpedia data as a standard of evaluation.
We evaluate on a dataset (around 12 million triples) of DBpedia ontology in [8] and
U7K is computed in 48 hours. The experimental results of 10 queries (Q1 ∼ Q10 ) over
three engines (i.e., gOWL-3X, PAGOdA, Pellet) are shown in the Figure 2. In a same
way, the evaluate on UOBM [8] over three engines (i.e., gOWL-3X, PAGOdA, Ontop)
are shown in the Figure 3 and Figure 4 (Note that we only list experimental results in
24 hours and the ordinate represents the total online time of query answering).
108
gOWL-3X PAGOdA Pellet
107
106
105
Query time (in ms)
104
103
102
101
100
Q1 Q2 Q3 Q4 Q5 Q6 Q7 Q8 Q9 Q10
Fig. 2: Evaluations on DBpedia
107
gOWL-3X PAGOdA Ontop
106
105
Query time (in ms)
104
103
102
101
100
Q1 Q2 Q3 Q4 Q5 Q6 Q7 Q8 Q9 Q10 Q11
Fig. 3: Evaluations on UOBM100
We find that gOWL-3X significantly improve the performance of all 21 queries
comparing to PAGOdA, Ontop and Pellet which represent materialization-based ap-
proach, query rewriting-based approach and traditional reasoning machine, respectively.
Since Ontop doesn’t have mapping information about DBpedia, it cannot handle DBpdiea
dataset. From the figures we can see that gOWL-3X is several orders of magnitude
higher than other methods, because it has the advantage of changing data processing
to offline before the query coming. In addition, its performance remains good with
data size increases (UOBM100 ∼ UOBM1000 ). Under the same conditions, PAGOdA
couldn’t handle large-scale dataset effectively because its query execution level is de-
pendent on RDFox, which finishes processing the data online, so the memory require-
ments are much stronger.
107
gOWL-3X
Ontop
106
105
Query time (in ms)
104
103
102
101
100
Q1 Q2 Q3 Q4 Q5 Q6 Q7 Q8 Q9 Q10 Q11
Fig. 4: Evaluations on UOBM1000
4 Conclusions
In this poster, we have presented the gOWL system for ontology-mediated query an-
swering. The approach take advantage of high-performance of off-on-shelf SPARQL
query engines for supporting large-scale ontology query answering in an efficient and
simple way. Based on DBpedia and UOBM datasets, gOWL outperforms existing en-
gines significantly.
Acknowledgments
This work is supported by the National Natural Science Foundation of China (61502336),
the National Key R&D Program of China (2016YFB1000603), the Key Technology
R&D Program of Tianjin (16YFZCGX00210), and the Seed Foundation of Tianjin Uni-
versity (2018XZC-0016).
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