Two-Phase Similarity Feature Construction for Enhancing Sensor Knowledge Graph Alignment via Genetic Programmings
Created by W.Langdon from
gp-bibliography.bib Revision:1.8344
- @Article{Xingsi_Xue:ieeeIOT,
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author = "Xingsi Xue and Jerry Chun-Wei Lin",
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title = "Two-Phase Similarity Feature Construction for
Enhancing Sensor Knowledge Graph Alignment via Genetic
Programmings",
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journal = "IEEE Internet of Things Journal",
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keywords = "genetic algorithms, genetic programming, Semantics,
Optimisation, Standards, Measurement, Accuracy,
Knowledge graphs, Internet of Things, Data mining,
Internet of Everything, Sensor Knowledge Graph
Matching, Similarity Feature Construction",
-
ISSN = "2327-4662",
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DOI = "
doi:10.1109/JIOT.2025.3560031",
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abstract = "The rapid evolution of the Internet of Everything
(IoE) has increased data complexity in urban traffic
networks, necessitating the use of the Semantic Sensor
Web (SSW) to integrate semantic metadata with sensor
data via Sensor Knowledge Graphs (SKGs). However, the
heterogeneity of SKGs, with varying focus, terminology
and structure, poses challenges for accurate sensor
data analysis. To identify semantically identical
entities across different SKGs, Similarity Features
(SFs) capture entity similarity from multiple
perspectives, but the multidimensional heterogeneity of
SKGs prevents any single SF from being universally
effective. To improve SKG alignment, this paper
presents a novel two-phase SKG alignment method, which
consists of three new components. First, an automated
SF construction framework is developed, which uses
Multi-Objective GP (MOGP) and Single-Objective GP
(SOGP) to automatically construct and combine the
high-quality SFs. Second, new fitness functions are
designed to guide the search direction of MOGP and
SOGP, without relying on standard alignments. Lastly,
lexicase crossover and mutation are proposed to
adaptively enhance population diversity, ensuring
high-quality SKG alignment. Experiment uses two KG
datasets from the Ontology Alignment Evaluation
Initiative (OAEI), along with ten pairs of practical
IoE SKGs, were used to evaluate the performance of our
approach. The results show that our method outperforms
state-of-the-art matching methods, particularly in
handling complex entity heterogeneity.",
-
notes = "Also known as \cite{10963864}",
- }
Genetic Programming entries for
Xingsi Xue
Jerry Chun-Wei Lin
Citations