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 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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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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Matching disease and phenotype ontologies in the ontology alignment evaluation initiative. [PDF]
J Biomed 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.
Harrow I +9 more
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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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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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SemTab 2019: Resources to Benchmark Tabular Data to Knowledge Graph Matching Systems [PDF]
The Semantic Web17th International Conference, 2020 Tabular data to Knowledge Graph matching is the process of assigning semantic tags from knowledge graphs (e.g., Wikidata or DBpedia) to the elements of a table. This task is a challenging problem for various reasons, including the lack of metadata (e.g.,
Jiménez-Ruiz E +4 more
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Ontology Alignment Architecture for Semantic Sensor Web Integration [PDF]
Sensors, 2013 Sensor networks are a concept that has become very popular in data acquisition and processing for multiple applications in different fields such as industrial, medicine, home automation, environmental detection, etc.
Bernardo Alarcos +3 more
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Biomedical ontology alignment: an approach based on representation learning. [PDF]
J Biomed Semantics, 2018 While representation learning techniques have shown great promise in application to a number of different NLP tasks, they have had little impact on the problem of ontology matching.
Kolyvakis P +3 more
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