Optimizing Hydrography Ontology Alignment Through Compact Particle Swarm Optimization Algorithm

Abstract

With the explosive growth in generating data in the hydrographical domain, many hydrography ontologies have been developed and maintained to describe hydrographical features and the relationships between them. However, the existing hydrography ontologies are developed with varying project perspectives and objectives, which inevitably results in the differences in terms of knowledge representation. Determining various relationships between two entities in different ontologies offers the opportunity to link hydrographical data for multiple purposes, though the research on this topic is in its infancy. Different from the traditional ontology alignment whose cardinality is 1:1, i.e. one source ontology entity is mapping with one target ontology entity and vice versa, and the relationship is the equivalence, matching hydrography ontologies is a more complex task, whose cardinality could be 1:1, 1:n or m:n and the relationships could be equivalence or subsumption. To efficiently optimize the ontology alignment, in this paper, a discrete optimal model is first constructed for the ontology matching problem, and then a Compact Particle Swarm Optimization algorithm (CPSO) based matching technique is proposed to efficiently solve it. CPSO utilizes the compact real-value encoding and decoding mechanism and the objective-decomposing strategy to approximate the PSO’s evolving process, which can dramatically reduce PSO’s memory consumption and runtime while at the same time ensure the solution’s quality. The experiment exploits the Hydrography dataset in Complex track provided by the Ontology Alignment Evaluation Initiative (OAEI) to test our proposal’s performance. The experimental results show that CPSO-based approach can effectively reduce PSO’s runtime and memory consumption, and determine high-quality hydrography ontology alignments.

Metaheuristics in the Balance: A Survey on Memory-Saving Approaches for Platforms with Seriously Limited Resources

“…Simulated Annealing variants nuSA [167] Q-SA [168] ISPO variants ISPO [170] AdpISPO [173] ISPO-restart [177] VISPO [177] Memetic Computing approaches RIS [185] 3SOME [180] MTS [178] State-estimation-based algorithms ssSKF [189] SAFIRO [191] Methods based on Evolution Strategies (1+1)-ES with 1/5 Success Rule (1+1)-CMA-ES [163] Methods based on Gradient GD [164] SPSA [162] Other algorithms SNUM [21] MSMS [179] VS [192] SEO [40] Compact optimisation algorithms compact Evolutionary Algorithms rcGA variants rcGA [197,198] UCGA [211] cross-rcGA [273] cSNUM [21] cDE variants cDE [199] DEcDE [274] cODE [275] CDE-CLS [276] cDE-light [279] CScDE [277] compact Swarm Intelligence algorithms cPSO variants cPSO [42] rcSPSO [281] cAPSO [282] cBFO [283] cABC variants cABC [304] EcABC [207] cBA [208] cFAs [38,285,286] cCSO [287] cPIO [291] cFPA [209,290] cCS …”

“…It uses the Gaussian model described in Section 5.4.2 and only requires storing the elite individual, a temporary solution, and PV to perform the search. A similar variant is proposed in [273], which, despite being named a compact PSO algorithm, displays the same working mechanism of rcGA (we will refer to it as cross-rcGA). Te peculiarity of this variant is that it performs a decomposition of the problem into three subproblems.…”

A multi-objective particle swarm optimization with density and distribution-based competitive mechanism for sensor ontology meta-matching

“…The saving of matching time and memory are particularly important while handle large-scale ontology matching problems. Recently, a multi-objective discrete optimization model is constructed by Wang et al [40] for the hydrological ontology matching problem, using a compact MOPSO to tune model's parameters. Meanwhile, Xue et al [48] also propose a compact MOPSO algorithm to solve the biomedical ontology matching problem, using a method called maximum-minimum to determine the winner solution, and experimental results prove that the approach can ameliorate the matching efficiency effectively.…”

“…Wang et al [40], Xue et al [48] The virtual population strategy results in a smaller population size, and the accuracy of the matching results may be reduced tages and limitations of the above methods, this work first constructs a multi-objective continuous optimization model for ontology meta-matching, applies different similarity measures to compute similarity, and uses improved MOPSO with a more reasonable decimal coding mechanism to automatically optimize the integrated weights of the measures.…”

Optimizing Ontology Alignment through Linkage Learning on Entity Correspondences

“…To improve the matching efficiency, Araújo et al [15] presented the matching system through parallel computing (PC) technique and Amin et al [16] matching ontology based on cloud computing (CC). At the same time, SIA-based technique has achieved great performance in the ontology matching [1,2,[17][18][19][20] domain [21][22][23][24][25][26].…”