Multi-source knowledge graph reasoning for ocean oil spill detection from satellite SAR images

Liu, Xiaojian; Zhang, Yongjun; Zou, Huimin; Wang, Fei; Cheng, Xiuzhen; Wu, Wenpin; Liu, Xinyi; Li, Yansheng

doi:10.1016/j.jag.2022.103153

Cited by 18 publications

(10 citation statements)

References 48 publications

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“…For instance, Chen et al (2022) utilized migration data from official provincial and city websites to craft a knowledge graph that maps the activities of COVID-19 patients, facilitating tracking, visualization, and reporting efforts in managing the pandemic. In the maritime sphere, Liu et al (2023c) developed a Transformer-based model employing multi-convolution bidirectional encoders for extracting essential information from maritime pollution prevention regulations and laws, aiding in port state control inspections to minimize ship pollution. Furthermore, Gan et al (2023) analyzed ship collision investigation reports issued by the China Maritime Safety Administration, proposing a novel approach for constructing knowledge graphs to elucidate the underlying factors of maritime accidents, thereby aiming to improve maritime traffic safety.…”

Section: Overview Of Related Work On Knowledge Graphsmentioning

confidence: 99%

Construction of a large-scale maritime element semantic schema based on knowledge graph models for unmanned automated decision-making

Li,

Liu,

Wang

et al. 2024

Front. Mar. Sci.

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In maritime logistics optimization, considerable research efforts are focused on the extraction of deep behavioral characteristics from comprehensive shipping data to discern patterns in maritime vessel behavior. The effective linkage of these characteristics with maritime infrastructure, such as berths, is critical for the enhancement of ship navigation systems. This endeavor is paramount not only as a research focus within maritime information science but also for the progression of intelligent maritime systems. Traditional methodologies have primarily emphasized the analysis of navigational paths of vessels without an extensive consideration of the geographical dynamics between ships and port infrastructure. However, the introduction of knowledge graphs has enabled the integration of disparate data sources, facilitating new insights that propel the development of intelligent maritime systems. This manuscript presents a novel framework using knowledge graph technology for profound analysis of maritime data. Utilizing automatic identification system (AIS) data alongside spatial information from port facilities, the framework forms semantic triplet connections among ships, anchorages, berths, and waterways. This enables the semantic modeling of maritime behaviors, offering precise identification of ships through their diverse semantic information. Moreover, by exploiting the semantic relations between ships and berths, a reverse semantic knowledge graph for berths is constructed, which is specifically tailored to ship type, size, and category. The manuscript critically evaluates a range of graph embedding techniques, dimensionality reduction methods, and classification strategies through experimental frameworks to determine the most efficacious methodologies. The findings reveal that the maritime knowledge graph significantly enhances the semantic understanding of unmanned maritime equipment, thereby improving decision-making capabilities. Additionally, it establishes a semantic foundation for the development of expansive maritime models, illustrating the potential of knowledge graph technology in advancing intelligent maritime systems.

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Section: Overview Of Related Work On Knowledge Graphsmentioning

confidence: 99%

Construction of a large-scale maritime element semantic schema based on knowledge graph models for unmanned automated decision-making

Li,

Liu,

Wang

et al. 2024

Front. Mar. Sci.

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“…In public health domain, to identify and explain misleading Covid-19 statements on social media, Kou et al [3] integrated the knowledge of both experts and non-experts to construct a Covid-19 knowledge graph. In environmental protection, Liu et al [4] built a multi-source oil spill detection knowledge graph using rule reasoning and GNN DOI reference number: 10.18293/SEKE2023-067 Fig. 1: Flowchart of Social Crisis Management to address information isolation in oil spill detection.…”

Section: Knowledge Graph Construction and Inference For Social Crisis...mentioning

confidence: 99%

Short Review of Intention Mining in Social Crisis Management through Automatic Technologies

Wang,

Li,

Yao

et al. 2023

International Conferences on Software Engineering and Knowledge Engineering

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In the current social environment, social crisis events occur frequently with significant impacts. Group intention mining through automatic technologies for managing social crises has gained extensive attention. This paper presents an overview of research on group intention mining in social crisis events, covering three areas: knowledge graph inference, intention attribution, and risk management. Knowledge graph inference facilitates the detection of group intention in crisis events. It is supported by the construction of crisis knowledge graphs, which organize crisis elements and inter-element relations into structured semantic knowledge. The interpretable semantics in the crisis knowledge graphs enables attribution of intention. Group intention mining consists of intention detection and intention attribution, serving the risk management of social crisis events. To gain insights into the process of group intention mining in social crises, the Covid-19 event is selected as a case study. Finally, the paper proposes future research directions to solve the limitations of existing intention mining methods in social crises.

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“…Ge et al [22] proposed a disaster prediction KG by integrating remote sensing data, relevant geographical data, and expert knowledge in disaster analysis. Liu et al [23] presented a KG-based oil spill inference method. It integrated various oil spill locations extracted from remote sensing images, vector data, text, and atmospheric-oceanic models.…”

Section: Deep Learning and Ontology Reasoningmentioning

confidence: 99%

Construction of Remote Sensing Indices Knowledge Graph (RSIKG) Based on Semantic Hierarchical Graph

Wang,

Shi,

2023

Remote Sensing

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Remote sensing indices are widely used in various fields of geoscience research. However, there are limits to how effectively the knowledge of indices can be managed or analyzed. One of the main problems is the lack of ontology models and research on indices, which makes it difficult to acquire and update knowledge in this area. Additionally, there is a lack of techniques to analyze the mathematical semantics of indices, making it difficult to directly manage and analyze their mathematical semantics. This study utilizes an ontology and mathematical semantics integration method to offer a novel knowledge graph for a remote sensing index knowledge graph (RSIKG) so as to address these issues. The proposed semantic hierarchical graph structure represents the indices of knowledge with an entity-relationship layer and a mathematical semantic layer. Specifically, ontologies in the entity-relationship layer are constructed to model concepts and relationships among indices. In the mathematical semantics layer, index formulas are represented using mathematical semantic graphs. A method for calculating similarity for index formulas is also proposed. The article describes the entire process of building RSIKG, including the extraction, storage, analysis, and inference of remote sensing index knowledge. Experiments provided in this article demonstrate the intuitive and practical nature of RSIKG for analyzing indices knowledge. Overall, the proposed methods can be useful for knowledge queries and the analysis of indices. And the present study lays the groundwork for future research on analysis techniques and knowledge processing related to remote sensing indices.

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Multi-source knowledge graph reasoning for ocean oil spill detection from satellite SAR images

Cited by 18 publications

References 48 publications

Construction of a large-scale maritime element semantic schema based on knowledge graph models for unmanned automated decision-making

Construction of a large-scale maritime element semantic schema based on knowledge graph models for unmanned automated decision-making

Short Review of Intention Mining in Social Crisis Management through Automatic Technologies

Construction of Remote Sensing Indices Knowledge Graph (RSIKG) Based on Semantic Hierarchical Graph

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