Technologies for Army Knowledge Fusion

2014 International Conference on Connected Vehicles and Expo (ICCVE)

Kashevnik

Ponomarev

et al. 2014

Nowadays location-based services become more and more popular. People travel from one location to another and usually would like to know information about places of interests around. Integration of location-based services with vehicles onboard systems opens new possibilities for delivering useful information about places of interests around using the vehicle multimedia system and voice control possibilities. The paper presents location-based on-board system for e-tourism and its implementation for Ford SYNC. The system consists of several services that find, extract and process potentially useful for the tourist information about places of interests around and provide it through user-friendly vehicle interface.

Section: # Service Fusion Process Service Fusion Resultsmentioning

confidence: 99%

Location-based on-board system for e-tourism: Implementation for Ford SYNC

2014 International Conference on Connected Vehicles and Expo (ICCVE)

Kashevnik

Ponomarev

et al. 2014

“…• Intelligent fusion of massive amounts of heterogeneous data/information from a wide range of distributed sources into a form which may be used by systems and humans as the foundation for problem solving and decision making [1,2]. Intelligent fusion assumes taking into account the semantic content of the sources being fused.…”

Section: Knowledge Fusionmentioning

confidence: 99%

“…Figure 4 illustrates the case when AO specifies that a value for an input parameter of the routing method serves as a value for the attribute representing the current location of a transportation device (1). In this ontology the class ''mobile'' representing a mobile acting resource and the class ''transportation device'' are related by a functional relationship (2) assigning that the location of a mobile acting resource is the same as the location of the transportation device this resource goes by.…”

Section: Knowledge Fusion: Abstract Contextmentioning

confidence: 99%

“…Whereas several years ago the main technology used to integrate data and information from multiple sources within DSS was data fusion, today the focus of data fusion has naturally changed to knowledge fusion. The goal of knowledge fusion (KF) is to integrate information and knowledge from multiple sources into a common piece of knowledge that may be used for decision making and problem solving or may provide a better insight and understanding of the situation under consideration [1][2][3][4].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Knowledge Fusion in Context-Aware Decision Support: Ontology-Based Modeling and Patterns

Studies in Fuzziness and Soft Computing

Levashova

Shilov

et al. 2014

The purpose of this chapter is twofold: (1) introducing of a semantic modeling mechanism, which is applied to achieve context-based knowledge fusion in a decision support system and (2) discovery of context-based knowledge fusion patterns. An approach to ontology-based resource modeling is proposed. The set of resources comprises sources of data/information/knowledge, problem solving resources and various actors. The knowledge fusion patterns are generalized with regard to two aspects: (1) preserving internal structures of multiple sources from which information/knowledge is fused within the ontological structure of context and preserving internal structure of the context itself, and (2) preserving autonomies of the multiple sources and the context. Six knowledge fusion patterns have been discovered. They are simple fusion, inferred fusion, instantiated fusion, adapted fusion, flat fusion, and historical fusion.

“…Referring to various perspectives on information fusion (e.g., [2,[4][5][6][7][8]), this technology is aimed at facilitation of situation awareness and improvement of decision making. The main result of information fusion is a new meaningful piece of information that is beneficial to decision making in less uncertainty, more preciseness, and/or more comprehensibility then the contributing parts [9][10][11].The objective of knowledge fusion is to integrate information and knowledge from multiple sources into some common knowledge that may be used for decision making and problem solving or may provide a better insight and understanding of the situation under consideration [12][13][14][15]. Knowledge fusion is defined as a process that merges heterogeneous information from multiple sources to create knowledge that is more complete, less uncertain, and less conflicting than the input [16].…”

mentioning

confidence: 99%

Context-Aware Knowledge Fusion for Decision Support

Context-Enhanced Information Fusion

Levashova

Shilov

2016

The purpose of this chapter is to investigate knowledge fusion processes with reference to context-aware decision support. Various knowledge fusion processes and their possible outcomes are analyzed. A context-aware decision support system for emergency management serves as a possible application in which knowledge fusion processes go on. This system provides fused outputs from different knowledge sources. It relies upon context model, which is the key to fuse information/knowledge and to generate useful decisions. The discussion is complemented by examples from a fire response scenario.Keywords Context-aware decision support Á Constraint-based ontology Á Information fusion model Á Knowledge fusion Á Emergency management Á Fire response IntroductionInformation fusion deals with various forms of information integration (aggregation, union, merging, etc.) from multiple sources. Some time ago, sources of data and information (databases, sensors, etc.) [1] due to observed shortcomings. Currently, this model is the most popular among other information fusion models. Initially proposed for the military applications, now it is widely used in civil domains as well, such as business or medicine. The levels with the JDL/DFG model are: source pre-processing/subject assessment (level 0), object assessment (level 1), situation assessment (level 2), impact assessment/threat refinement (level 3), process refinement (level 4), and user refinement/cognitive refinement (level 5). Through its different levels, the model divides the fusion processes according to the different levels of abstraction of the data fused and the different problems the data fusion is applicable to (e.g., characteristic estimation vs. situation recognition and analysis). The model does not prescribe a strict ordering of the processes and the fusion levels, and the levels are not always discrete and may overlap. The JDL/DFG model is useful for visualizing the data fusion process, facilitating discussion and common understanding, and important for system-level information fusion design [2].Recently, research on information fusion extended the set of input sources with ontologies, text documents, the web, etc. That is, the focus of data and information fusion has changed to knowledge fusion.Information fusion and knowledge fusion are tightly related [3]. Referring to various perspectives on information fusion (e.g., [2,[4][5][6][7][8]), this technology is aimed at facilitation of situation awareness and improvement of decision making. The main result of information fusion is a new meaningful piece of information that is beneficial to decision making in less uncertainty, more preciseness, and/or more comprehensibility then the contributing parts [9][10][11].The objective of knowledge fusion is to integrate information and knowledge from multiple sources into some common knowledge that may be used for decision making and problem solving or may provide a better insight and understanding of the situation under consideration [12][13][14][15]. Knowledge f...