Multi-goal Pathfinding in Cyber-Physical-Social Environments: Multi-layer Search over a Semantic Knowledge Graph

Kem, Oudom; Balbo, Flavien; Zimmermann, Antoine; Nagellen, Pierre

doi:10.1016/j.procs.2017.08.162

Cited by 6 publications

(2 citation statements)

References 5 publications

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“…Marino [26], using a graphical search method combined with a neural network to merge large knowledge graphs in visual classification pipelines, improved the accuracy of image classification. Kem [27] used a knowledge graph model to describe the spatial structure of an environment, including physics and social entities, and used the relationship between them to form a network space map (CSG). Jaya [28] used a knowledge graph combined with speech recognition and language understanding to solve automatic voice recognition issues.…”

Section: Knowledge Graphmentioning

confidence: 99%

R-KG: A Novel Method for Implementing a Robot Intelligent Service

Menglin

Tian

et al. 2020

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Aiming to solve the problem of environmental information being difficult to characterize when an intelligent service is used, knowledge graphs are used to express environmental information when performing intelligent services. Here, we specially design a kind of knowledge graph for environment expression referred to as a robot knowledge graph (R-KG). The main work of a R-KG is to integrate the diverse semantic information in the environment and pay attention to the relationship at the instance level. Also, through the efficient knowledge organization of a R-KG, robots can fully understand the environment. The R-KG firstly integrates knowledge from different sources to form a unified and standardized representation of a knowledge graph. Then, the deep logical relationship hidden in the knowledge graph is explored. To this end, a knowledge reasoning model based on a Markov logic network is proposed to realize the self-developmental ability of the knowledge graph and to further enrich it. Finally, as the strength of environment expression directly affects the efficiency of robots performing services, in order to verify the efficiency of the R-KG, it is used here as the semantic map that can be directly used by a robot for performing intelligent services. The final results prove that the R-KG can effectively express environmental information.

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Section: Knowledge Graphmentioning

confidence: 99%

R-KG: A Novel Method for Implementing a Robot Intelligent Service

Menglin

Tian

et al. 2020

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“…Using machine learning, entities are extracted, ontologies are constructed, and network security knowledge base is obtained. Kem et al [33] proposed a knowledge model to describe the spatial structure of an environment. e network physic society entities it contains, and the relationship between them, are regarded as a graph, called cyberspatial graph (CSG).…”

Section: Complexitymentioning

confidence: 99%

A Cognition Knowledge Representation Model Based on Multidimensional Heterogeneous Data

et al. 2020

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The information in the working environment of industrial Internet is characterized by diversity, semantics, hierarchy, and relevance. However, the existing representation methods of environmental information mostly emphasize the concepts and relationships in the environment and have an insufficient understanding of the items and relationships at the instance level. There are also some problems such as low visualization of knowledge representation, poor human-machine interaction ability, insufficient knowledge reasoning ability, and slow knowledge search speed, which cannot meet the needs of intelligent and personalized service. Based on this, this paper designs a cognitive information representation model based on a knowledge graph, which combines the perceptual information of industrial robot ontology with semantic description information such as functional attributes obtained from the Internet to form a structured and logically reasoned cognitive knowledge graph including perception layer and cognition layer. Aiming at the problem that the data sources of the knowledge base for constructing the cognitive knowledge graph are wide and heterogeneous, and there are entity semantic differences and knowledge system differences among different data sources, a multimodal entity semantic fusion model based on vector features and a system fusion framework based on HowNet are designed, and the environment description information such as object semantics, attributes, relations, spatial location, and context acquired by industrial robots and their own state information are unified and standardized. The automatic representation of robot perceived information is realized, and the universality, systematicness, and intuition of robot cognitive information representation are enhanced, so that the cognition reasoning ability and knowledge retrieval efficiency of robots in the industrial Internet environment can be effectively improved.

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Contextualized Knowledge Graphs in Communication Network and Cyber-Physical System Modeling

Sikos

2020

Provenance in Data Science

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Multi-goal Pathfinding in Cyber-Physical-Social Environments: Multi-layer Search over a Semantic Knowledge Graph

Cited by 6 publications

References 5 publications

R-KG: A Novel Method for Implementing a Robot Intelligent Service

R-KG: A Novel Method for Implementing a Robot Intelligent Service

A Cognition Knowledge Representation Model Based on Multidimensional Heterogeneous Data

Contextualized Knowledge Graphs in Communication Network and Cyber-Physical System Modeling

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