Solving Sensor Ontology Metamatching Problem with Compact Flower Pollination Algorithm

Junhong Feng,Lingling Fu,Wenwu Lian,Jian-Hong Wang,Xingsi Xue,Xishuan Niu

doi:10.1155/2022/9662517

Abstract

To implement co-operation among applications on the Internet of Things (IoT), we need to describe the meaning of diverse sensor data with the sensor ontology. However, there exists a heterogeneity issue among different sensor ontologies, which hampers their communications. Sensor ontology matching is a feasible solution to this problem, which is able to map the identical ontology entity pairs. This work investigates the sensor ontology meta-matching problem, which indirectly optimizes the sensor ontology alignment’s quality by tuning the weights to aggregate different ontology matchers. Due to the largescale entity and their complex semantic relationships, swarm intelligence (SI) based techniques are emerging as a popular approach to optimize the sensor ontology alignment. Inspired by the success of the flower pollination algorithm (FPA) in the IoT domain, this work further proposes a compact FPA (CFPA), which introduces the compact encoding mechanism to improve the algorithm’s efficiency, and on this basis, the compact exploration and exploitation operators are proposed, and an adaptive switching probability is presented to trade-off these two searching strategies. The experiment uses the ontology alignment evaluation initiative (OAEI)’s benchmark and the real sensor ontologies to test CFPA’s performance. The statistical comparisons show that CFPA significantly outperforms other state-of-the-art sensor ontology matching techniques.

Highlights

To implement the co-operations among applications on the Internet of ings (IoT) [1], we need to describe the meaning of diverse sensor data with the sensor ontology and express them in a machine-interpretable way
The contributions made in this work are as follows: (1) we present the mathematical formula for the sensor ontology meta-matching problem; (2) we propose a problem-specific compact FPA (CFPA) to efficiently address the problem, which uses the compact exploitation operator and compact exploration operator to mimic flower pollination algorithm (FPA)’s evolving process and an adaptive switching probability to trade-off CFPA’s exploitation and exploration; and (3) we employ CFPA on ontology alignment evaluation initiative (OAEI)’s benchmark and the task of matching sensor ontologies
FPA is inspired by the pollination of natural flowers, and its evolving process consists of two distinct operators, i.e., global pollination and local pollination, whose formulas are defined in the following equations: xti+1 xti + L􏼐xti − x∗􏼑, (4)

Summary

Introduction

To implement the co-operations among applications on the Internet of ings (IoT) [1], we need to describe the meaning of diverse sensor data with the sensor ontology and express them in a machine-interpretable way. To overcome the population-based FPA’s disadvantages, such as slow convergence speed [20], this work proposes a compact FPA (CFPA) and uses it to address the sensor ontology meta-matching problem. The alignment is denoted by a matrix with real numbers in [0, 1] as its elements, whose rows and columns are two entity sets, and its element is two corresponding entities’ similarity value. To combine these matchers, we assign the weights for their corresponding similarity matrices and aggregate these matrices into the final one. 􏽘 wi 1, where wi is the i-th weight of the ontology matcher’s corresponding matching matrix and F(W) first uses W to aggregate all the matching matrices and use the function f() to calculate the final matrix’s corresponding alignment’s quality

Compact Flower Pollination Algorithm

Exploration and

Pseudocode of Compact Flower

Experimental Results and Analysis

Statistical Experiment

Conclusion