Ontology-Based Data Access for Maritime Security

Brüggemann, Stefan; Bereta, Konstantina; Xiao, Guohui; Koubarakis, Manolis

doi:10.1007/978-3-319-34129-3_45

Cited by 34 publications

(26 citation statements)

References 4 publications

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“…Different facets are used in the SEM to represent the ship behaviors. A system (RMSAS), proposed by Brüggemann S [27], combines static data from different sources using semantic techniques. Its applications verify that the system can increase the value of data and improve the processing workflow in the maritime domain.…”

Section: Literature Reviewmentioning

confidence: 99%

Semantic Modelling of Ship Behavior in Harbor Based on Ontology and Dynamic Bayesian Network

Wen

Zhang

Huang

et al. 2019

IJGI

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Recognizing ship behavior is important for maritime situation awareness and intelligent transportation management. Some scholars extracted ship behaviors from massive trajectory data by statistical analysis. However, the meaning of the behaviors, i.e., semantic meanings of behaviors and their relationships, are not explicit. Ship behaviors are affected by navigational area and traffic rules, so their meanings can be obtained only in specific maritime situations. The work establishes the semantic model of ship behavior (SMSB) to represent and reason the meaning of the behaviors. Firstly, a semantic network is built based on maritime traffic rules and good seamanship. The corresponding detection methods are then proposed to identify basic ship behaviors in various maritime scenes, including dock, anchorage, traffic lane, and general scenes. After that, dynamic Bayesian network (DBN) is used to reason potential ship behaviors. Finally, trajectory annotation and semantic query of the model are validated in the different scenes of harbor. The basic behaviors and potential behaviors in all typical scenes of any harbor can be obtained accurately and expressed conveniently using the proposed model. The model facilitates the ships behavior research, contributing to the semantic trajectory analysis.

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Section: Literature Reviewmentioning

confidence: 99%

Semantic Modelling of Ship Behavior in Harbor Based on Ontology and Dynamic Bayesian Network

Wen

Zhang

Huang

et al. 2019

IJGI

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“…It supports the evaluation of stSPARQL/GeoSPARQL queries over virtual RDF graphs defined through R2RML mappings to a relational database. It is a mature system that has already been used in a number of applications [11,12]. Handling geometric information in raw files (e.g., shapefiles) or made available through the scientific data access service OPeNDAP has been added to Ontop-spatial [22] by integrating the relational engine madIS [23].…”

Section: Transforming Geospatial Data Into Rdfmentioning

confidence: 99%

“…Users can store and query an RDF graph generated by GeoTriples using a geospatial RDF store like Strabon. 11 They can also interlink this graph with other linked geospatial data using tools like the temporal and geospatial extension of Silk 12 developed in our group [9] or the more recent tool Radon developed with the participation of our group [10]. For example, it might be useful to infer links involving topological relationships e.g., A geo:sfContains F where A is the area covered by a remotely sensed multispectral image I, F is a geographical feature of interest (field, lake, city etc.)…”

Section: Introductionmentioning

confidence: 99%

“…It is often the case in applications that relevant geospatial data is stored in spatially-enabled relational databases (e.g., PostGIS) or files (e.g., shapefiles), and its owners do not want to explicitly transform it into linked data [11,12]. For example, this might be because these data sources get frequently updated and/or are very large.…”

Section: Introductionmentioning

confidence: 99%

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GeoTriples: Transforming geospatial data into RDF graphs using R2RML and RML mappings

Kyzirakos

Savva

Vlachopoulos

et al. 2018

Journal of Web Semantics

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A lot of geospatial data has become available at no charge in many countries recently. Geospatial data that is currently made available by government agencies usually do not follow the linked data paradigm. In the few cases where government agencies do follow the linked data paradigm (e.g., Ordnance Survey in the United Kingdom), specialized scripts have been used for transforming geospatial data into RDF. In this paper we present the open source tool GeoTriples which generates and processes extended R2RML and RML mappings that transform geospatial data from many input formats into RDF. GeoTriples allows the transformation of geospatial data stored in raw files (shapefiles, CSV, KML, XML, GML and GeoJSON) and spatially-enabled RDBMS (PostGIS and MonetDB) into RDF graphs using well-known vocabularies like GeoSPARQL and stSPARQL, but without being tightly coupled to a specific vocabulary. GeoTriples has been developed in European projects LEO and Melodies and has been used to transform many geospatial data sources into linked data. We study the performance of GeoTriples experimentally using large publicly available geospatial datasets, and show that GeoTriples is very efficient and scalable especially when its mapping processor is implemented using Apache Hadoop.

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“…Two approaches have been reported to detect anomalies by the grid-based approaches and the methods using vectorial representation of traffic[36]. Unfortunately, AIS data transferred and received can be manipulated or hindered with a limited trustworthiness due to the fact that every vessel owner can manipulate the system or can completely turn it off[39]…”

mentioning

confidence: 99%

Island Activities Detected by VIIRS and Validation with AIS

Asanuma¹,

Hasegawa²,

Yamaguchi³

et al. 2018

ARS

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A possibility to monitor the reclamation activities by remote sensing was discussed. The lights observed in the night time by Day Night Band (DNB) of Visible Infrared Imaging Radiometer Suite (VIIRS), ocean color observed in the day time by visible bands of VIIRS were the tools to monitor the surface activities, and the Automated Information System (AIS) was used to verify the types and number of vessels associated with the reclamation activities. The lights as the radiance from the surface were monitored by the object based analysis, where the object was defined as a radius of 5 km from the center of the Mischief Reef in the South China Sea (SCS). The time history of surface lights exhibited the increase of the radiance from January to May 2015 and the radiance was kept in the certain level to December 2016 with some variations. The ocean color, chlorophyll-a concentration as a proxy of sediments, showed an increase from February to June 2015 and returned to a low concentration in August 2015. According to the historical data of AIS, the number of dredgers has increased from February to August 2015 and the maximum number of dredgers was recorded in June 2015. The timing of increase of lights from surface, increase of chlorophyll-a concentration, and increase of number of vessels are consistent.

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Ontology-Based Data Access for Maritime Security

Cited by 34 publications

References 4 publications

Semantic Modelling of Ship Behavior in Harbor Based on Ontology and Dynamic Bayesian Network

Semantic Modelling of Ship Behavior in Harbor Based on Ontology and Dynamic Bayesian Network

GeoTriples: Transforming geospatial data into RDF graphs using R2RML and RML mappings

Island Activities Detected by VIIRS and Validation with AIS

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