Designing ontologies for higher level fusion

Little, Eric; Rogova, Galina L.

doi:10.1016/j.inffus.2008.05.006

Cited by 49 publications

(23 citation statements)

References 24 publications

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“…The fusion functions and processes devoted to this goal are pertinent to JDL level 3. Little and Rogova [111] claim that a formal structure of domain-specific types of entities, attributes, situations, and their relations are needed for reasoning about situations, intent and threats. To this end, they postulate the use of formal ontologies in order to capture the complexity of domain-specific knowledge so to be able to understand issues related to change over time, CI, and identity.…”

Section: Intent Assessmentmentioning

confidence: 99%

Context-based Information Fusion: A survey and discussion

2015

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This survey aims to provide a comprehensive status of recent and current research on context-based Information Fusion (IF) systems, tracing back the roots of the original thinking behind the development of the concept of ''context''. It shows how its fortune in the distributed computing world eventually per-meated in the world of IF, discussing the current strategies and techniques, and hinting possible future trends. IF processes can represent context at different levels (structural and physical constraints of the scenario, a priori known operational rules between entities and environment, dynamic relationships modelled to interpret the system output, etc.). In addition to the survey, several novel context exploita-tion dynamics and architectural aspects peculiar to the fusion domain are presented and discussed.

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Section: Intent Assessmentmentioning

confidence: 99%

Context-based Information Fusion: A survey and discussion

2015

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“…These models may suffice for use in Level 1 fusion to work with data constraints [31], but they lack capabilities for complex context representation required by higher-level fusion. Ontology-based models provide a formal way for specifying core concepts, sub-concepts, facts and their inter-relationships to enable realistic representation of contextual knowledge [6][7][8]. Current approaches to ontology-based context modeling can be classified into three main areas: contextualization of ontologies, ontology design patterns, and context-aware systems [32].…”

Section: Context Definition and Representationmentioning

confidence: 99%

“…It has been built from available descriptions of regular operations in real harbors and the associated traffic regulations of daily activities. 7 This frame entails a simplification of the complete procedure explained in the previous section, because the number of hypothesis is reduced and we do not consider the hypothesis selection procedure. Hence, in the reminder of this section we will not refer to it as abduction, but just as threat detection.…”

Section: Description Of the Scenariomentioning

confidence: 99%

“…Ontologies are an appropriate formalism to represent contextual and factual knowledge in higher-level fusion [6][7][8]. However, ontology languages based on Description Logics, and in particular the standard ontology web language (OWL 2) [9], present several unsolved challenges when applied to Situation Assessment because: (i) they do not allow for reasoning with uncertain knowledge; and (ii) they do not directly support abductive reasoning to create and validate situational hypotheses that change in time.…”

Section: Introductionmentioning

confidence: 99%

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Context-based multi-level information fusion for harbor surveillance

et al. 2015

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Abstract:Harbor surveillance is a critical and challenging part of maritime security procedures. Building a surveil-lance picture to support decision makers in detection of potential threats requires the integration of data and information coming from heterogeneous sources. Context plays a key role in achieving this task by providing expectations, constraints and additional information for inference about the items of interest. This paper proposes a fusion system for context-based situation and threat assessment with application to harbor surveillance. The architecture of the system is organized in two levels. The lowest level uses an ontological model to formally represent input data and to classify harbor objects and basic situations by deductive reasoning according to the harbor regulations. The higher level applies Belief-based Argumen-tation to evaluate the threat posed by suspicious vessels. The functioning of the system is illustrated with several examples that reproduce common harbor scenarios. IntroductionMaritime security is an area of strategic importance for the international community. As stated in [1], ''a terrorist incident against a marine transportation system would have a disaster impact on global shipping, international trade, and the world economy in addition to the strategic military value of many ports and waterways''. For that reason, one of the principal goals of strengthening maritime security is to ''increase maritime domain awareness'' by building a ''surveillance picture as complete as possible to assess the threats and vulnerabilities in the maritime realm''. In particular, harbor surveillance is a critical part of maritime security procedures because of its multiple objectives: recognition of terrorist threats, prevention of maritime and ecological accidents, detection of illegal immigration, fishing and drug trafficking, and so forth. However, it is nowadays mostly developed by human operators [2], who have to evaluate an overwhelming amount of information. This makes it very difficult to keep track of the event stream with the required level of attention due to distraction, fatigue and oversight. In addition, their decisions may be strongly affected by sensor data imprecision and subjective judgment. make informed decisions. According to the JDL data fusion model, the latter task belongs to the domain of Situation Assessment, defined as the estimation of ''sets of relationships among entities and their implications for the states of the related entities '' [4]. In this domain, it requires understanding the intrinsic information provided by coastal sensors in the context determined by extrinsic factors, like harbor environment, operational regulations, traffic data and intelligence reports.Recently, the increasing interest in higher-level information fusion has led to several proposals for context management -see for example the special sessions on context-based information fusion celebrated in the International Conferences on Information Fusion since 2007. Detection and characteri...

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“…Little and Rogova address the symbolic representation of relationships among entities in the disaster management domain [26], focusing on the trade-off between the generality of the representation and the possibility to include domain-specific characteristic. These two aspects are critical when ontologies are used to reason over relationships among entities in a complex scenario.…”

Section: Centralized Situation Assessmentmentioning

confidence: 99%

Cooperative situation assessment in a maritime scenario

Farinelli

Nardi

Pigliacampo³

et al. 2012

Int. J. Intell. Syst.

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In large-scale, complex domains such as space defense and security systems, situation assessment and decision making are evolving from centralized models to high-level, net-centric models. In this context, collaboration among the many actors involved in the situation assessment process is critical in order to achieve a prompt reaction as needed in the operational scenario.In this paper, we propose a multi-agent based approach to situation assessment, where agents cooperate by sharing local information to reach a common and coherent assessment of situations. Specifically, we characterize situation assessment as a classification process based on OWL ontology reasoning, and we provide a protocol for cooperative multi-agent situation assessment, which allows the agents to achieve coherent high level conclusions. We validate our approach in a real maritime surveillance scenario, where our prototype system effectively supports the user in detecting and classifying potential threats; moreover, our distributed solution performs comparably to a centralized method, while preserving independence of decision makers and dramatically reducing the amount of communication required.

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Designing ontologies for higher level fusion

Cited by 49 publications

References 24 publications

Context-based Information Fusion: A survey and discussion

Context-based Information Fusion: A survey and discussion

Context-based multi-level information fusion for harbor surveillance

Cooperative situation assessment in a maritime scenario

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