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

Wu, Hao; Menglin, Jiao; Tian, Guohui; Ma, Qing; Liu, Guoliang

doi:10.3390/ai1010006

Cited by 3 publications

(2 citation statements)

References 22 publications

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“…ese achievements show that the knowledge graph has a very broad development prospect, but most of the achievements and applications exist in search engine improvement, text information processing, and intelligent search. Hao et al [35] proposed an environmental information representation mechanism based on knowledge graph in the field of intelligent service of home robots. In this paper, we further into the professional database in the Internet industry for the construction of knowledge graph based on the above research, at the same time in the process of knowledge extraction added the description information of work environment, the function information, and realtime status information of the mechanical arm into knowledge extraction triples, and make the mechanical arm cognitive external working environment and working object.…”

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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Section: Complexitymentioning

confidence: 99%

A Cognition Knowledge Representation Model Based on Multidimensional Heterogeneous Data

et al. 2020

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“…Various related work, conducted on image summarization on pure images, have applied numerical metrics to perform the summarization [2,3]. Semantic information, ontology, and knowledge-based platforms are applied in a different scope of multimedia, like computing the affective-aesthetic potential of literary texts [37] and robot intelligent service [38]. In this study, we proposed a semantic technique for image collection summarization.…”

Section: Multi-modal Image Summarization Systemmentioning

confidence: 99%

Image Collection Summarization Method Based on Semantic Hierarchies

Samani

Moghaddam

2020

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The size of internet image collections is increasing drastically. As a result, new techniques are required to facilitate users in browsing, navigation, and summarization of these large volume collections. Image collection summarization methods present users with a set of exemplar images as the most representative ones from the initial image collection. In this study, an image collection summarization technique was introduced according to semantic hierarchies among them. In the proposed approach, images were mapped to the nodes of a pre-defined domain ontology. In this way, a semantic hierarchical classifier was used, which finally mapped images to different nodes of the ontology. We made a compromise between the degree of freedom of the classifier and the goodness of the summarization method. The summarization was done using a group of high-level features that provided a semantic measurement of information in images. Experimental outcomes indicated that the introduced image collection summarization method outperformed the recent techniques for the summarization of image collections.

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Numerical Markov Logic Network: A Scalable Probabilistic Framework for Hybrid Knowledge Inference

Zhong

Chen

et al. 2021

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In recent years, the Markov Logic Network (MLN) has emerged as a powerful tool for knowledge-based inference due to its ability to combine first-order logic inference and probabilistic reasoning. Unfortunately, current MLN solutions cannot efficiently support knowledge inference involving arithmetic expressions, which is required to model the interaction between logic relations and numerical values in many real applications. In this paper, we propose a probabilistic inference framework, called the Numerical Markov Logic Network (NMLN), to enable efficient inference of hybrid knowledge involving both logic and arithmetic expressions. We first introduce the hybrid knowledge rules, then define an inference model, and finally, present a technique based on convex optimization for efficient inference. Built on decomposable exp-loss function, the proposed inference model can process hybrid knowledge rules more effectively and efficiently than the existing MLN approaches. Finally, we empirically evaluate the performance of the proposed approach on real data. Our experiments show that compared to the state-of-the-art MLN solution, it can achieve better prediction accuracy while significantly reducing inference time.

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

Cited by 3 publications

References 22 publications

A Cognition Knowledge Representation Model Based on Multidimensional Heterogeneous Data

A Cognition Knowledge Representation Model Based on Multidimensional Heterogeneous Data

Image Collection Summarization Method Based on Semantic Hierarchies

Numerical Markov Logic Network: A Scalable Probabilistic Framework for Hybrid Knowledge Inference

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