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EUCISE-OWL: An ontology-based representation of the Common Information Sharing Environment (CISE) for the maritime domain
Abstract
The timely and efficient cooperation across sectors and borders during maritime crises is paramount for the safety of human lives. Maritime monitoring authorities are now realizing the grave importance of cross-sector and cross-border information sharing. However, this cooperation is compromised by the diversity of existing systems and the vast volumes of heterogeneous data generated and exchanged during maritime operations. In order to address these challenges, the EU has been driving several initiatives, including several EU-funded projects, for facilitating information exchange across sectors and borders. A key outcome from these efforts is the Common Information Sharing Environment (CISE), which constitutes a collaborative initiative for promoting automated information sharing between maritime monitoring authorities. However, the adoption of CISE is substantially limited by its existing serialization as an XML Schema only, which facilitates information sharing and exchange to some extent, but fails to deliver the fundamental additional benefits provided by ontologies, like the richer semantics, enhanced semantic interoperability and semantic reasoning capabilities. Thus, this paper presents EUCISE-OWL, an ontology representation of the CISE data model that capitalizes on the benefits provided by ontologies and aims to encourage the adoption of CISE. EUCISE-OWL is an outcome from close collaboration in an EU-funded project with domain experts with extensive experience in deploying CISE in practice. The paper also presents a representative example for handling information exchange during a maritime crisis as well as performance results for specific querying tasks that can demonstrate and evaluate the use of the proposed ontology in practice.
... To assess RSWO, we adhered to the guidelines in [48]. The RSWO is assessed with the OntoMetrics [49], to reflect some notions of ontology richness with five metrics (details follow in next paragraph). There are no public available (to our best knowledge) resistance spot welding ontology that we can directly compare with. ...
The ongoing industrial revolution termed Industry 4.0 (I4.0) has borne witness to a series of profound changes towards increasing smart automation, particularly in the industrial sectors of automotive, aerospace, manufacturing, etc. Automatic welding, a widely applied manufacturing process in these domains, is not an exception of these changes. One type of automatic welding, Resistance Spot Welding (RSW), lies in the center of this work. Large volumes and varieties of RSW data are being generated, thanks to the technologies behind I4.0. To address the associated data challenges, ontologies are essential in various aspects: integrating data sources, enhancing interoperability, and unifying knowledge etc. However, there have been limited studies around the semantic modelling of Resistance Spot Welding: Existing ontologies have overlooked some crucial concepts, such as an operation-centric view, welding software, and welding electrodes, which are essential for the monitoring of sensor measurements as well as the status of machine components (e.g., electrode wear). Additionally, current ontologies are not publicly available (to the best of our knowledge), and therefore cannot be accessed by other users. Such a lack of availability often requires that users build their own ontologies from scratch. In this paper, we propose our RSW ontology (RSWO)
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to formalize knowledge in the RSW domain. It combines three sources of knowledge: extensive discussions with Bosch welding experts; reusing terminologies following ISO-14327 and ISO-14373 standards; and existing established ontologies. We have evaluated RSWO on real-world data from monitoring welding quality at Bosch in Germany, using Competency Questions, FAIR principles, OOPS!, and OntoMetrics.
... It has defined specific vocabulary that involves seven core entities and relationships among them, such as: Agent, Object, Location, Document, Event, Risk and Period. Moreover, the model comprehends eleven auxiliary entities: Vessel, Cargo, Operational Asset, Person, Organization, Movement, Incident, Anomaly, Action, Unique Identifier and Metadata [10]. ...
Ensuring a high level of vessel traffic surveillance and maritime safety is determined by exploiting innovative ICT technologies and international cooperation among maritime authorities. Therefore, initiatives for maritime surveillance, global and regional integrations are realised through a collaborative, cost-effective and interoperable Common Information Sharing Environment (CISE). Consisting of the institutional network of maritime authorities that cooperate on various domains like safety, border control, environmental and rescue missions at sea, CISE enables the efficient transfer and economic exchange of maritime data and information via different interoperable systems using modern digital technologies. The ever-increasing amount of data received from heterogeneous data sources requires specific processing through the adoption of a Big Data framework which hosts, manages and distributes data to maritime users, contributing with great overall benefits to the CISE network core functionality. Specifically, this paper analyses the advantages of the Data Lake infrastructure, including its processes, techniques, tools and applications used to enhance maritime surveillance and safety across the CISE network. This part contains the deployment and interoperability achieved through the components of the participating command and control (C2) systems. Last, as a case study, an overview of the EU project EFFECTOR is provided which aims to demonstrate an end-to-end interoperability framework of data-driven solutions for maritime situational awareness at strategic and tactical operations.
... Originally, the CISE was formulated with a data model (it continually gets enhanced [9]) using XSD (XML Schema Definition) or UML (Unified Modelling Language) and comprising seven core data entities [10] [11]: Location, Period, Event, Document, Agent, Risk and Object; and associated eleven auxiliary data entities: Action, Movement, Incident, Anomaly, Metadata, UID (Unique Identifier), Cargo, Vessel, Operational asset, Meteo-oceanographic condition, Person and Organisation. The data is defined through relationships and association rules alongside attributes, subclasses, values, and enumerations. ...
This paper outlines an example method and trial scenario phases for extending the existing tools for maritime surveillance across the maritime regions of Europe. There are continuous dedicated efforts throughout European collaborations for advancing the features of Common Information Sharing Environment (CISE), which is a European Union (EU) initiative for ubiquitous information sharing across the maritime and land borders, as well as between the national sectors. The EFFECTOR EU project introduces a novel concept in the information exchange: the data lake, intended for providing a common smart data repository for diverse and comprehensive data analysis and storage that emanates from diverse sources within the local, regional, national, and international scope. Setting up of the data lake facilities and broadened information exchange is established and validated through early trials planned to be conducted in the project. Administration for Maritime Safety and Port Management (AMSPM) is integrating maritime surveillance data from Montenegro in the EU information exchanges and vice versa. The EFFECTOR project’s French maritime trial offers an implementation of the broadened scope of uses of advanced data exchanges, analysis storage, and retrieval. A case of retroactive data analysis and deductions is to be demonstrated through collaboration of AMSPM in the French maritime trial with other project partners. The trial scenario planning includes two vessels coming into the French waters near Marseille from distant ports, one via a stop-over in Montenegro and the other from Portugal.
... The original CISE Data Model composition of data entities is depicted in Figure 2 (UID stands for Unique Identifier) initially defined in FP7 EU project CoopP (Cooperation Project Maritime Surveillance) [8]. It is constantly being perfected [9] and extended as conducted in the ANDROMEDA project [5] with new data entities that describe further surveillance situations and details being observed. ...
This paper outlines an extensive analysis of the case of Montenegro’s maritime surveillance system becoming integrated within the European Common Information Sharing Environment (CISE). Threats to secure maritime borders across Europe are ever-present and regularly demand coordinated efforts between the member states to tackle and prevent them, e.g. illegal immigration across the Mediterranean. Administration for Maritime Safety and Port Management (AMSPM) in Montenegro is a member of the ANDROMEDA EU project that seeks to facilitate deployments and demonstrations of CISE trials across the European regions, towards their endorsement readiness. AMSPM is now at the forefront of assessing and deploying the CISE components in Montenegro. It thus appropriately evaluates the operational aspects, observes the CISE implementations in some European states, formulates the impact for other national stakeholders, as well as the very prospect of the resulting augmented maritime surveillance in the country. This substantiates the content of this paper as the feasibility of the CISE deployment in Montenegro, supported by a snapshot of the cost-benefit analysis. We aspire to offer novel perspectives and insights that could be a universally useful experience to different CISE implementation initiatives, especially for countries or regions of similar smaller sizes and coastal area.
... The original CISE Data Model composition of data entities is depicted in Figure 2 (UID stands for Unique Identifier) initially defined in FP7 EU project CoopP (Cooperation Project Maritime Surveillance) [8]. It is constantly being perfected [9] and extended as conducted in the ANDROMEDA project [5] with new data entities that describe further surveillance situations and details being observed. ...
This paper outlines an extensive analysis of the case of Montenegro’s maritime surveillance system becoming integrated within the European Common Information Sharing Environment (CISE). Threats to secure maritime borders across Europe are ever-present and regularly demand coordinated efforts between the member states to tackle and prevent them, e.g. illegal immigration across the Mediterranean. Administration for Maritime Safety and Port Management (AMSPM) in Montenegro is a member of the ANDROMEDA EU project that seeks to facilitate deployments and demonstrations of CISE trials across the European regions, towards their endorsement readiness. AMSPM is now at the forefront of assessing and deploying the CISE components in Montenegro. It thus appropriately evaluates the operational aspects, observes the CISE implementations in some European states, formulates the impact for other national stakeholders, as well as the very prospect of the resulting augmented maritime surveillance in the country. This substantiates the content of this paper as the feasibility of the CISE deployment in Montenegro, supported by a snapshot of the cost-benefit analysis. We aspire to offer novel perspectives and insights that could be a universally useful experience to different CISE implementation initiatives, especially for countries or regions of similar smaller sizes and coastal area.
This paper presents the design of an ontology for the representation of enriched semantic trajectories in the maritime domain. The ontology supports vessel trajectories, at different levels of detail, enriched with vessel characteristics, weather information and events, as well as topological and proximity relations to geographical areas of interest (such as ports and protected areas). The paper describes how raw data from diverse data sources are integrated in order to produce enriched semantic trajectories, represented as linked data in RDF, in compliance with the proposed ontology. Moreover, the added-value of the ontology is demonstrated by means of semantic queries that retrieve information about moving vessels and their trajectories using complex criteria. The linked data and our ontology are publicly available as five star linked open data, offering a valuable resource to the research community.KeywordsOntologydata integrationsemantic trajectoriesmaritime domain
Maritime heterogeneous sensor networks (HSN), which are widely used in support of marine monitoring, maritime security and safety applications, combine diversified sensing systems and platforms observing the state of environmental features of interest or the status of anthropogenic objects to achieve awareness on the situation. To model the information characteristics of HSN observations for maritime situational awareness (MSA), we developed the MSA-HSN ontology, an integrated semantic model for maritime linked data, specifically observations suiting the requirements of maritime information fusion systems. MSA-HSN integrates established ontologies and models for sensors, observations, measurements, quantities, and occurrents, tailored to MSA applications and requirements. To support the interoperability with existing maritime data models, MSA-HSN is aligned with the relevant aspects of the EUCISE/e-CISE (Common Information Sharing Environment) data models. As a validation use case, in this paper the MSA-HSN ontology is applied to model the information elements of the maritime surveillance system developed within the Interactive Extreme-Scale Analytics and Forecasting (INFORE) project, where different situational views offered by a variegate suite of sensors and platforms are fused using big data analytics to achieve maritime situational awareness for maritime security. The paper describes the design of the MSA-HSN ontology, illustrating its application through examples taken from the INFORE use case and from relevant datasets developed.
Current frameworks to assess resilience are often based on low-dimensional models that describe a small number of inter-related components. However, for many infrastructures their resilience is a property that emerges from myriads of individual interactions. Here, we develop for the first time fully quantitative and data-driven indicators for several aspects that relate to resilient organizational behavior during emergency situations. We consider dynamic communication networks of emergency responders collected during the annual public emergency drill of the Hungarian National University of Public Service. Our key hypothesis is that the time-dependent network structure derived from these communication flows conveys information as to how redundant, vulnerable, and efficient individual organizations acted during the drill. The resulting indicators can be applied on two different levels—the level of the entire network and the level of individual participants. To validate our framework, we show that when the personnel is prepared for a given event (e.g., critical weather conditions), effective communication within the organization is dominated by vertical flows of information. However, under surprise the affected units concentrated on horizontal communication, thereby creating bottlenecks and losses of efficiency in the network. Our results indicate a generic trade-off between efficiency, vulnerability and redundancy of communication networks in emergency response. Moreover, we can relate the origin of this trade-off to the specific way of how individual organizations adapted their communication behavior in times of crisis. We do so in a fully quantitative and data-driven way that is directly linked to real-world networks, infrastructures, and the people acting within them.
Ontology evaluation refers to the activity of checking the technical quality of an ontology against a frame of reference. As such, it is of core importance for ontology engineering supporting scenarios such as ontology validation, knowledge selection, or the evaluation of knowledge extraction algorithms. In this chapter, we provide methodological guidelines for evaluating stand-alone ontologies as well as ontology networks. Our goal is not only to present the NeOn perspective on this issue but to also provide a practical outlook to the vast area of work in the area of ontology evaluation. Without performing an extensive state-of-the-art analysis of this research field, we aim to illustrate how various evaluation methods developed by the NeOn project, and not only, can be used at different stages of the evaluation process. We conclude the chapter with some concrete examples of performing ontology evaluation.
Although originally conceived as a conceptual object for modelling knowledge, current ontologies do not make it possible to manipulate spatial knowledge. However, spatial knowledge is an essential component of any modelling specification. This problem provided the motivation for the creation of an expert system driven by an ontology. The system enables experts in the maritime domain to characterise abnormal ship behaviour based on formal semantic properties. Users are able to specify and execute spatial rules that are directly integrated into the ontology and a map interface linked to the ontology displays the results of the inferences obtained.
Full implementation of the Semantic Web requires widespread availability of OWL ontologies. Manual ontology development using current OWL editors remains a tedious and cumbersome task that requires significant understanding of the new ontology language and can easily result in a knowledge acquisition bottleneck. On the other hand, abundant domain knowledge has been specified by existing database schemata such as UML class diagrams. Thus developing an automatic tool for extracting OWL ontologies from existing UML class diagrams is helpful to Web ontology development. In this paper we propose an automatic, semantics-preserving approach for extracting OWL ontologies from existing UML class diagrams. This approach establishes a precise conceptual correspondence between UML and OWL through a semantics-preserving schema translation algorithm. The experiments with our implemented prototype tool, UML2OWL, show that the proposed approach is effective and a fully automatic ontology extraction is achievable. The proposed approach and tool will facilitate the development of Web ontologies and the realization of semantic interoperations between existing Web database applications and the Semantic Web.
This is a system description of the Description Logic reasoner FaCT++. The reasoner implements a tableaux decision procedure for the well known SHOIQ\mathcal{SHOIQ} description logic, with additional support for datatypes, including strings and integers. The system employs a wide range
of performance enhancing optimisations, including both standard techniques (such as absorption and model merging) and newly
developed ones (such as ordering heuristics and taxonomic classification). FaCT++ can, via the standard DIG interface, be used to provide reasoning services for ontology engineering tools supporting the
OWL DL ontology language.
The goal of the ontology requirements specification activity is to state why the ontology is being built, what its intended uses are, who the end-users are, and which requirements the ontology should fulfill. The novelty of this paper lies in the systematization of the ontology requirements specification activity since the paper proposes detailed methodological guidelines for specifying ontology requirements efficiently. These guidelines will help ontology engineers to capture ontology requirements and produce the ontology requirements specification document (ORSD). The ORSD will play a key role during the ontology development process because it facilitates, among other activities, (1) the search and reuse of existing knowledge-aware resources with the aim of re-engineering them into ontologies, (2) the search and reuse of existing ontological resources (ontologies, ontology modules, ontology statements as well as ontology design patterns), and (3) the verification of the ontology along the ontology development. In parallel to the guidelines, we present the ORSD that resulted from the ontology requirements specification activity within the SEEMP project, and how this document facilitated not only the reuse of existing knowledge-aware resources but also the verification of the SEEMP ontologies. Moreover, we present some use cases in which the methodological guidelines proposed here were applied
The perspective role of UML as a conceptual modelling language for the semantic web has become an important research topic. We argue that UML could be a key technology for overcoming the ontology development bottleneck thanks to its wide acceptance and sophisticated tool support. Transformational approaches are a promising way of establishing a connection between UML and web-based ontology languages. We compare some proposals for defining transformations between UML and web ontology languages and discuss the different ways they handle the conceptual differences between these languages. We identify commonalities and differences of the approaches and point out to open questions that have not or not satisfyingly addressed by existing approaches.
Motivated by real-life emerging needs in critical domains, this paper proposes a coherent and generic ontology for the representation of semantic trajectories, in association with related events and contextual information. The main contribution of the proposed ontology is the representation of semantic trajectories at different levels of spatio-temporal analysis.
The maritime security domain is challenged by a number of data analysis needs focusing on increasing the maritime situation awareness, i.e., detection and analysis of abnormal vessel behaviors and suspicious vessel movements. The need for efficient processing of dynamic and/or static vessel data that come from different heterogeneous sources is emerged. In this paper we describe how we address the challenge of combining and processing real-time and static data from different sources using ontology-based data access techniques, and we explain how the application of semantic web technologies increases the value of data and improves the processing workflow in the maritime domain.
In this paper we introduce Graffoo, i.e., a graphical notation to develop OWL ontologies by means of yEd, a free editor for diagrams.
Ontologies tend to be found everywhere. They are viewed as the silver bullet for many applications, such as database integration, peer-to-peer systems, e-commerce, semantic web services, or social networks. However, in open or evolving systems, such as the semantic web, different parties would, in general, adopt different ontologies. Thus, merely using ontologies, like using XML, does not reduce heterogeneity: it just raises heterogeneity problems to a higher level. Euzenat and Shvaiko's book is devoted to ontology matching as a solution to the semantic heterogeneity problem faced by computer systems. Ontology matching aims at finding correspondences between semantically related entities of different ontologies. These correspondences may stand for equivalence as well as other relations, such as consequence, subsumption, or disjointness, between ontology entities. Many different matching solutions have been proposed so far from various viewpoints, e.g., databases, information systems, and artificial intelligence. The second edition of Ontology Matching has been thoroughly revised and updated to reflect the most recent advances in this quickly developing area, which resulted in more than 150 pages of new content. In particular, the book includes a new chapter dedicated to the methodology for performing ontology matching. It also covers emerging topics, such as data interlinking, ontology partitioning and pruning, context-based matching, matcher tuning, alignment debugging, and user involvement in matching, to mention a few. More than 100 state-of-the-art matching systems and frameworks were reviewed. With Ontology Matching, researchers and practitioners will find a reference book that presents currently available work in a uniform framework. In particular, the work and the techniques presented in this book can be equally applied to database schema matching, catalog integration, XML schema matching and other related problems. The objectives of the book include presenting (i) the state of the art and (ii) the latest research results in ontology matching by providing a systematic and detailed account of matching techniques and matching systems from theoretical, practical and application perspectives. © Springer-Verlag Berlin Heidelberg 2007, 2013. All rights are reserved.
This paper presents two contributions to the field of Ontology Evaluation. First, a live catalogue of pitfalls that extends previous works on modeling errors with new pitfalls resulting from an empirical analysis of over 693 ontologies. Such a catalogue classifies pitfalls according to the Structural, Functional and Usability- Profiling dimensions. For each pitfall, we incorporate the value of its importance level (critical, important and minor) and the number of ontologies where each pitfall has been detected. Second, OOPS! (OntOlogy Pitfall Scanner!), a tool for detecting pitfalls in ontologies and targeted at newcomers and domain experts unfamiliar with description logics and ontology implementation languages. The tool operates independently of any ontology development platform and is available online. The evaluation of the system is provided both through a survey of users' satisfaction and worldwide usage statistics. In addition, the system is also compared with existing ontology evaluation tools in terms of coverage of pitfalls detected.
The amount of research papers published nowadays related to ontology matching is remarkable and we believe that reflects the growing interest of the research community. However, for new practitioners that approach the field, this amount of information might seem overwhelming. Therefore, the purpose of this work is to help in guiding new practitioners get a general idea on the state of the field and to determine possible research lines.
To do so, we first perform a literature review of the field in the last decade by means of an online search. The articles retrieved are sorted using a classification framework that we propose, and the different categories are revised and analyzed. The information in this review is extended and supported by the results obtained by a survey that we have designed and conducted among the practitioners.
One of the tasks of a maritime safety and security (MSS) system is to map incoming observations in the form of sensor data onto existing maritime domain knowledge. This domain knowledge is modeled in an ontology. The sensor data contains information on ship trajectories, labeled with ship types from this ontology. These ship types are broad and within one type there can be several distinctive behavior patterns. As a consequence we cannot make a good mapping from these trajectories to the ship types. To make this possible we should change the ontology by adding relevant subtypes. This paper presents a semi-automatic method to extend the ontology of ship types on the basis of trajectory data. The first part involves the use of hidden Markov models to model the data of each ship within one ship type and the clustering of these models. The clusters are input to the second part where we use internet querying and natural language processing based ontology extension techniques to extend the maritime domain ontology. We present the promising results of a preliminary experiment that shows an interesting possibility in terms of semi-automatic ontology extension, which would enable an optimal coverage of a given domain: not providing too many concepts, and not leaving essential ones out.
In this paper, we present a brief overview of Pellet: a complete OWL-DL reasoner with acceptable to very good performance, extensive middleware, and a number of unique features. Pellet is the first sound and complete OWL-DL reasoner with extensive support for reasoning with individuals (including nominal support and conjunctive query), user-defined datatypes, and debugging support for ontologies. It implements several extensions to OWL-DL including a combination formalism for OWL-DL ontologies, a non-monotonic operator, and preliminary support for OWL/Rule hybrid reasoning. Pellet is written in Java and is open source.
Ontologies are used in the integration of information resources by describing the semantics of the information sources with machine understandable terms and definitions. But, creating an ontology is a difficult and time-consuming process, especially in the early stage of extracting key concepts and relations. This paper proposes a method for domain ontology building by extracting ontological knowledge from UML models of existing systems. We compare the UML model elements with the OWL ones and derive transformation rules between the corresponding model elements. Based on these rules, we define an XSLT document which implements the transformation processes. We expect that the proposed method reduce the cost and time for building domain ontologies with the reuse of existing UML models
We present a novel reasoning calculus for Description Logics (DLs)—knowledge representation formalisms with applications in areas such as the Semantic Web. In order to reduce the nondeterminism due to general inclusion axioms, we base our calculus on hypertableau and hyperresolution calculi, which we extend with a blocking condition to en- sure termination. To prevent the calculus from generating large models, we introduce "anywhere" pairwise blocking. Our preliminary implementa- tion shows significant performance improvements on several well-known ontologies. To the best of our knowledge, our reasoner is currently the only one that can classify the original version of the GALEN terminology.
EUCISE2020: European test bed for the maritime common information sharing environment
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- C Medaglia
- J Tyni
- I Mattila
- C Matarazzi
- G Codispoti
Approaches, methods, metrics, measures, and subjectivity in ontology evaluation: A survey
- Hlomani
Application of robotics for enhanced security: European research on security robots
- Z Székely
An ontology for digital maritime regulations
- Hagaseth
Increasing cooperation between the European maritime domain authorities
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