Patterns for context-based knowledge fusion in decision support systems

Smirnov, Alexander; Levashova, Tatiana; Shilov, Nikolay

doi:10.1016/j.inffus.2013.10.010

Cited by 46 publications

(21 citation statements)

References 25 publications

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“…The work by Smirnov et al [109] describes from a general perspective context-based knowledge fusion processes and proposes a classification related to their use in Decision Support Systems (DSS). Some general patterns are identified, analyzing the effects that knowledge fusion process produces in the system for the preservation of internal structures representing the knowledge and their autonomies.…”

Section: Decision Makingmentioning

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: Decision Makingmentioning

confidence: 99%

Context-based Information Fusion: A survey and discussion

2015

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“…These relationships are the result of knowledge fusion. Particularly, fusion of the ontology-based representations for Fire Emergency response center [50] independent entities and the non-ontological result of problem solving produce new contextual knowledge about the entities.…”

Section: Problem Solvingmentioning

confidence: 99%

“…The set of constraints C comprises six types of constraints. Namely, constraints used to specify pairs <class, attribute> (C 1 ); constraints on 1 The detailed description of the framework underlying the CADSS can be found in [50,51].6 Context-Aware Knowledge Fusion for Decision Support 131 domains of the attribute values (C 2 ); class compatibility constraints (C 3 ); constraints used to represent taxonomical (C 4 , type 1), hierarchical (C 4 , type 2), and associative relationships (C 5 ) between classes; and functional constraints for class attributes (C 6 ). The application ontology specified as it is referred here corresponds to a (non-instantiated) object-oriented constraint network.…”

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confidence: 99%

Context-Aware Knowledge Fusion for Decision Support

Smirnov

Levashova

Shilov

2016

Context-Enhanced Information Fusion

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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...

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“…Like in other branches of computer science and artificial intelligence [7,24], crowd computing pattern represents a reusable solution to a commonly occurring problem. In crowd computing context this term was first used (to the best of authors' knowledge) by [27] to name various techniques for dealing with unreliability of human responses.…”

Section: Human Factors and Crowd Workflow Patternsmentioning

confidence: 99%

Crowd Computing Framework for Geoinformation Tasks

Smirnov

Ponomarev

2015

Lecture Notes in Geoinformation and Cartography

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In the paper a general purpose crowd computing framework architecture is discussed. The proposed framework can be used to compose crowd computing workflows of different complexity. Its prominent features include ontological description of crowd members' competencies profiles; automatic assignment of tasks to crowd members; the support of both human and non-human computing units (hybrid crowd); and spatial features of crowd members which make way for employing the proposed framework for a variety of crowdsourced geoinformation tasks.

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Patterns for context-based knowledge fusion in decision support systems

Cited by 46 publications

References 25 publications

Context-based Information Fusion: A survey and discussion

Context-based Information Fusion: A survey and discussion

Context-Aware Knowledge Fusion for Decision Support

Crowd Computing Framework for Geoinformation Tasks

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