FTRLIM: Distributed Instance Matching Framework for Large-Scale Knowledge Graph Fusion [PDF]
Entropy, 2021 Instance matching is a key task in knowledge graph fusion, and it is critical to improving the efficiency of instance matching, given the increasing scale of knowledge graphs.
Hongming Zhu +5 more
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Integrating Sensor Ontologies with Niching Multi-Objective Particle Swarm Optimization Algorithm [PDF]
Sensors, 2023 Sensor ontology provides a standardized semantic representation for information sharing between sensor devices. However, due to the varied descriptions of sensor devices at the semantic level by designers in different fields, data exchange between sensor
Yucheng Zhuang, Yikun Huang, Wenyu Liu
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FOntCell: Fusion of Ontologies of Cells [PDF]
Frontiers in Cell and Developmental Biology, 2021 High-throughput cell-data technologies such as single-cell RNA-seq create a demand for algorithms for automatic cell classification and characterization. There exist several cell classification ontologies with complementary information.
Javier Cabau-Laporta +10 more
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Matching Biomedical Ontologies through Adaptive Multi-Modal Multi-Objective Evolutionary Algorithm [PDF]
Biology, 2021 To integrate massive amounts of heterogeneous biomedical data in biomedical ontologies and to provide more options for clinical diagnosis, this work proposes an adaptive Multi-modal Multi-Objective Evolutionary Algorithm (aMMOEA) to match two ...
Xingsi Xue, Pei-Wei Tsai, Yucheng Zhuang
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An Alignment-Based Implementation of a Holistic Ontology Integration Method [PDF]
MethodsX, 2021 Despite the intense research activity in the last two decades, ontology integration still presents a number of challenging issues. As ontologies are continuously growing in number, complexity and size and are adopted within open distributed systems such ...
Inès Osman +3 more
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Matching sensor ontologies with unsupervised neural network with competitive learning [PDF]
PeerJ Computer Science, 2021 Sensor ontologies formally model the core concepts in the sensor domain and their relationships, which facilitates the trusted communication and collaboration of Artificial Intelligence of Things (AIoT).
Xingsi Xue, Haolin Wang, Wenyu Liu
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Tackling the challenges of matching biomedical ontologies [PDF]
Journal of Biomedical Semantics, 2018 Background Biomedical ontologies pose several challenges to ontology matching due both to the complexity of the biomedical domain and to the characteristics of the ontologies themselves.
Daniel Faria +5 more
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Matching disease and phenotype ontologies in the ontology alignment evaluation initiative [PDF]
Journal of Biomedical Semantics, 2017 Background The disease and phenotype track was designed to evaluate the relative performance of ontology matching systems that generate mappings between source ontologies.
Ian Harrow +9 more
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Interactive biomedical ontology matching. [PDF]
PLoS ONE, 2019 Due to continuous evolution of biomedical data, biomedical ontologies are becoming larger and more complex, which leads to the existence of many overlapping information.
Xingsi Xue, Zhi Hang, Zhengyi Tang
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Automatic background knowledge selection for matching biomedical ontologies. [PDF]
PLoS ONE, 2014 Ontology matching is a growing field of research that is of critical importance for the semantic web initiative. The use of background knowledge for ontology matching is often a key factor for success, particularly in complex and lexically rich domains ...
Daniel Faria +4 more
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