=Paper=
{{Paper
|id=None
|storemode=property
|title=ONTOMS2: an Efficient and Scalable ONTOlogy Management System with an Incremental Reasoning
|pdfUrl=https://ceur-ws.org/Vol-1035/iswc2013_demo_39.pdf
|volume=Vol-1035
|dblpUrl=https://dblp.org/rec/conf/semweb/LeeLKPC13
}}
==ONTOMS2: an Efficient and Scalable ONTOlogy Management System with an Incremental Reasoning==
https://ceur-ws.org/Vol-1035/iswc2013_demo_39.pdf
ONTOMS2: an Efficient and Scalable ONTOlogy
Management System with an Incremental
Reasoning
Min-Joong Lee1 , Jong-Ryul Lee1 , Sangyeon Kim1 , Myung-Jae Park1 , and
Chin-Wan Chung2
Department of Computer Science , KAIST, Daejeon, Republic of Korea
{mjlee,jrlee,sangyeon,jpark}@islab.kaist.ac.kr1 chungcw@kaist.edu2
Abstract. We present ONTOMS2, an efficient and scalable ONTOlogy
Management System with an incremental reasoning. ONTOMS2 stores
an OWL document and processes OWL-QL and SPARQL queries. Espe-
cially, ONTOMS2 supports SPARQL Update queries with an incremen-
tal instance reasoning of inverseOf, symmetric and transitive properties.
1 Introduction
In order to efficiently manage ontology data, various ontology data management
systems [4–6] are proposed based on RDBMS. However, existing ontology data
management systems do not support updates incrementally. To efficiently man-
age such large sized ontology data, we need a way of incremental updating for
ontology data. We propose an incremental update strategy to efficiently handle
a large amount of ontology data and the frequent updates of such ontology data.
Our incremental update strategy provides an insertion and deletion based on
SPARQL Update with the support of some important semantics of ontologies
such as inverseOf, symmetric, and transitive. We apply our incremental update
strategy to ONTOMS which is an efficient and scalable ONTOlogy Management
System proposed in [6].
ONTOMS efficiently manages large sized OWL data based on RDBMS and
using OWL-QL. It stores OWL data into a class based relational schema to in-
crease the query processing performance. Figure 1 describes an example of OWL
data stored into relational tables in ONTOMS. Unlike other approaches, ON-
TOMS generates a class based relational schema, where one relation is created
for each class. Each class relation contains associated properties as its attributes.
For more details of ONTOMS, please refer [6].
We denote the new version of ONTOMS where our incremental update strat-
egy and SPARQL processor are applied as ONTOMS2. We designed the archi-
tecture of ONTOMS2 as depicted in Figure 2. The core modules of ONTOMS2
are OWL Data Storage Module, Instance Inference Module, SPARQL Module,
and OWL-QL Module. To apply our incremental update strategy to ONTOMS,
we create SPARQL Module in Query Processing Module and modified Instance
Inference Module for the incremental reasoning. OWL Data Storage Module ob-
tains class and property hierarchy information from OWL Reasoner, Pellet, and
OWL
Reasoner
GraduateStudent (Pellet) User
UID degreeFrom degreeFrom_S degreeFrom_E Class, Property Class, Property OWL Data Query Query
GS1 Hierarchies Definitions Result
Professor ONTOMS2
Query Processing
UID degreeFrom degreeFrom_S degreeFrom_E Module
Prof1 Univ1 6 7 OWL Data Instance Incremental
Storage Inference Reasoning SPARQL OWL-QL
Course GraduateStudent_ Professor_ Module Module Module Module
UID takesCourse teachesCourse
C1 UID Value UID Value
Tuples for New Properties,
C2 GS1 C1 Prof1 C1 Class, Instances Instances
Property, SQL Query
GS1 C2 Prof1 C2 Instance Query Result
RDBMS in Tuples
Fig. 1. Relational Tables in ONTOMS Fig. 2. The Architecture of ONTOMS2
generates relationship information among classes and properties when parsing
the given OWL data. Once OWL data is stored, Instance Inference Module per-
forms instance reasoning with properties which are collected along with their
values from RDBMS, and stores newly generated instances into RDBMS. The
details of instance reasoning for initial OWL data can be found at Section 6
in [6]. The Query Processing Module processes OWL-QL query and SPARQL
query. When a SPARQL update query is given, SPARQL Module processes it
through Instance Inference Module for the incremental reasoning.
2 Ontology Data Update
SPARQL Update There are several query languages for OWL such as OWL-
QL and SQWRL . However, all of them support a read-only(select) query only.
The previous version [6] of ONTOMS2 uses a OWL-QL to retrieve instances.
We enhanced ONTOMS to have an ability to update ontology data by adding
the SPARQL processor in addition to the OWL-QL processor. This is because
OWL is developed as a vocabulary extension of RDF, SPARQL is a de facto
query language for RDF, and recently W3C published a recommendation [2] for
the SPARQL update.
According to the SPARQL Update recommendation proposed by W3C, SPARQL
Update provides three operations related to update : INSERT DATA, DELETE
DATA, and DELETE/INSERT. The INSERT DATA operation is to add new
triples into the ontology data while the DELETE DATA operation is to remove
triples from the ontology data. Lastly, the DELETE/INSERT operation is to re-
move triples and add new triples into the ontology data with the WHERE clause.
ONTOMS2 supports all INSERT DATA, DELETE DATA, and DELETE/INSERT
operations. Due to the space limitation, we omitted the detailed syntax. Please
refer Chapter 3 SPARQL 1.1 Update Language in [2] for the detailed syntax.
Incremental Reasoning Only some of existing ontology data management
systems support the update for ontology data. Even though there are some
existing systems with the update feature, the instance reasoning for the updates
has to be conducted from the scratch due to the constraints of the system while
ONTOMS supports an incremental reasoning. Thus, the instance reasoning of
the existing systems for each update is performed by processing all the stored
triples. Eventually, it degrades the update processing performance.
OWL defines several types of properties. However, only inverseOf, symmet-
ric and transitive properties may generate new facts(triples). The incremental
reasoning for the inverseOf and symmetric properties can be done in a straight
forward way. For the insertion and deletion of transitive property triples, we
adapt the SQL-based transitive closure maintenance algorithm presented in [3]
to effectively maintain the transitive properties. We cut down the step for finding
truly new tuples among the generated tuples by unifying the transitive closure
table and multi-value class property table. This reduces costly table join opera-
tions and it also reduces a storage size.
3 Experiments
80000 90000
Inferred triples 80000 JENA insertion
70000
ONTOMS insertion
60000 70000
Inferred triples
JENA deletion
60000
50000 ONTOMS deletion
Time (s)
50000
40000
40000
30000
30000
20000 20000
10000 10000
0 0
0 2000 4000 6000 8000 10000 0 2000 4000 6000 8000 10000
Insert triples Insert / delete triples
(a) The number of inferred triples (b) The update processing time
Fig. 3. Experimental results
The most important enhanced point of ONTOMS2 over ONTOMS is that
ONTOMS2 supports an efficient ontology data update with an incremental rea-
soning. Therefore, we focused on the incremental reasoning performance when
ontology data is updated. We compare ONTOMS2 with JENA which is one of the
most popular ontology data management systems. JENA provides the SPARQL
update feature through ARQ. However, ARQ only supports the instance reason-
ing with non-update(SELECT) queries1 . Therefore, we implemented a simple
SPARQL update interface for JANA which uses OntModel. Among reasoners in
JENA, OW L M EM M ICRO RU LE IN F is used.
Note that, in JENA, the instance reasoning should be re-run from the scratch
for each update query and the update processing time for JENA does not contain
the time for storing the results of the instance reasoning into the relational tables
while ONTOMS2 does the incremental reasoning and the update processing time
for ONTOMS2 contains the storing time. Our experiments were performed on
2.27GHz Intel Xeon with 12GB of main memory. We implemented ONTOMS2
using MS SQL Server 2008 and Java. For JENA [1], Jena-2.6.4 version is used.
Figure 3(a) shows the number of triples to be inferred for various sizes of
insertion triples(facts). All fact triples have same transitive property. New triples
to be inserted as a facts are generated such that their subjects and objects are
randomly selected from subjects and objects of previously inserted fact triples.
1
ARQ supports a SPARQL update query on basic Model only, not on OntModel or
InfModel. However, JENA uses OntModel or InfModel for the reasoning.
In JENA, the instance reasoning for each insertion is conducted from the scratch
while the instance reasoning of ONTOMS2 is incrementally conducted. As a
result, ONTOMS2 outperforms JENA for the insertion and deletion due to the
incremental update strategy. The results are as shown in Figure 3(b).
4 Demo
Fig. 4. Query interface and Query wizard of ONTOMS2
During the demonstration, we will illustrate how our system handles SPARQL
queries over ontology data such as retrieving instances of classes or proper-
ties, inserting/removing instances which satisfy a specific condition with an
incremental reasoning. Our system also contains a GUI-based wizard to help
a user to create a SPARQL query easily. The screen shots of ONTOMS2 are
depicted in Figure 4 and the recorded video can be found at our demo page
(http://islab.kaist.ac.kr/ONTOMS2/).
Acknowledgments This work was supported by the National Research Foun-
dation of Korea grant funded by the Korean government (MSIP) (No. NRF-
2009-0081365).
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3431, 2013.
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