Knowledge-Based Systems 137 (2017) 94–103
Contents lists available at ScienceDirect
Knowle dge-Base d Systems
journal homepage: www.elsevier.com/locate/knosys
Collaborative ontology matching based on compact interactive
evolutionary algorithm
Xingsi Xue
a , b , ∗
, Jianhua Liu
a , b
a
College of Information Science and Engineering, Fujian University of Technology, Fuzhou, Fujian, 350118, China
b
Fujian Provincial Key Laboratory of Big Data Mining and Applications (Fujian University of Technology), Fuzhou, Fujian, 350118, China
a r t i c l e i n f o
Article history:
Received 11 May 2017
Revised 15 August 2017
Accepted 12 September 2017
Available online 21 September 2017
Keywords:
Semantic web
Ontology matching
Collaborative validation
Memetic algorithm
a b s t r a c t
Ontology is the kernel technology of semantic web, which plays a prominent role for achieving inter-
operability across heterogeneous systems and applications by formally describing the semantics of data
that characterize a particular application domain. However, different ontology engineers might have po-
tentially opposing world views which could yield the different descriptions on the same ontology entity,
raising so called ontology heterogeneous problem. Ontology matching, which aims at identifying the cor-
respondences between the entities of heterogeneous ontologies, is recognized as an effective technology
to solve the ontology heterogeneous problem. Due to the complexity of ontology matching process, ontol-
ogy alignments generated by the automatic ontology matchers should be validated by the users to ensure
their qualities, and the technology that makes multiple users collaborate with each other to help the au-
tomatic tool create high quality matchings in a reasonable amount of time is called collaborative ontology
matching. Such a collaborative ontology matching poses a new challenge of how to reduce users’ work-
load, but at the same time, increase their involvement’s value. To address this challenge, in this paper, we
propose a Compact Interactive Memetic Algorithm (CIMA) based collaborative ontology matching technol-
ogy, which can reduce users’ workload by adaptively determining the time of getting users involved, pre-
senting the most problematic correspondences for users and helping users to automatic validate multiple
conflict mappings, and increase user involvement’s value by propagating the collaborative validation and
decreasing the negative effect brought by the error user validations. The experimental results show that
our proposal is able to efficiently exploit the collaborative validation to improve its non-interactive ver-
sion, and the runtime and alignment quality of our approach both outperform state-of-the-art interactive
ontology matching systems under different user error rate cases.
©2017 Elsevier B.V. All rights reserved.
1. Introduction
Semantic inter-operability represents the capability of two or
more systems to meaningfully and accurately interpret the ex-
changed data so as to produce useful results, which requires the
meanings of any data must be specified in an appropriate detail
in order to resolve the potential ambiguity. Ontology is the ker-
nel technology of semantic web, which plays a prominent role for
achieving interoperability across heterogeneous systems and appli-
cations by formally describing the semantics of data that charac-
terize a particular application domain [1] . So far, ontologies have
been applied in multiple domains of application such as informa-
tion retrieval, medical diagnosis, e-Commerce, knowledge acquisi-
tion, bio-informatics and service-oriented computing [2,3] . How-
∗
Corresponding author.
E-mail address: xxs@fjut.edu.cn (X. Xue).
ever, different ontology engineers might have potentially oppos-
ing world views which could yield the different descriptions on
the same ontology entity, raising so called ontology heterogeneous
problem. Ontology matching, which aims at identifying the corre-
spondences between the entities of heterogeneous ontologies, is
recognized as an effective technology to solve ontology heteroge-
neous problem. Since the purely manual specification of seman-
tic correspondences is highly impractical especially when the scale
of the ontology is large, in recent years, many automatic ontology
matching systems have been proposed. However, due to the com-
plexity of the ontology matching process, ontology alignments gen-
erated by the automatic matching tools should be validated by the
users to ensure their qualities [4] . To make multiple users collabo-
rate with each other to help the automatic tool create high quality
matchings in a reasonable amount of time is called collaborative
ontology matching [5] , but the research on it is still in its infancy.
https://doi.org/10.1016/j.knosys.2017.09.017
0950-7051/© 2017 Elsevier B.V. All rights reserved.