Design of scene knowledge base system based on domain ontology

Park, Wonjoo; Han, Moonsik; Jeong-woo, Son; Kim, Sun-Joong

doi:10.23919/icact.2017.7890152

Cited by 9 publications

(3 citation statements)

References 6 publications

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“…Ontology theory has become a research hotspot because of its structured knowledge representation and reasoning ability. Park W et al [14] proposed a scene knowledge network system design based on domain ontology. Hao Q et al [15] used the ontology knowledge network model to reorganize the original dataset, making the logical structure of the new dataset more suitable for upper-layer applications and improving the utilization of open data.…”

Section: Environmental Information Expressionmentioning

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: Environmental Information Expressionmentioning

confidence: 99%

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

Menglin

Tian

et al. 2020

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“…Ontology's structured knowledge representation has a wide range of representation, strong representation ability, and reasoning ability, which has quickly become a research hotspot. Literature studies [12][13][14][15], respectively, use ontology technology to construct corresponding knowledge frameworks to realize environmental representation. Park et al [12] proposed a scene knowledge base system design based on domain knowledge ontology.…”

Section: Related Workmentioning

confidence: 99%

“…Literature studies [12][13][14][15], respectively, use ontology technology to construct corresponding knowledge frameworks to realize environmental representation. Park et al [12] proposed a scene knowledge base system design based on domain knowledge ontology. Hao et al [13] used the ontology knowledge base model to reorganize the original dataset, making the logical structure of the new dataset more suitable for upper application and improving the utilization rate of open data.…”

Section: Related Workmentioning

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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Algorithm Design of Dynamic Question Answering System Based on Knowledge Base

Jiang

Wang

et al. 2020

Advances in Intelligent Systems and Computing

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Design of scene knowledge base system based on domain ontology

Cited by 9 publications

References 6 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

Algorithm Design of Dynamic Question Answering System Based on Knowledge Base

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