A Novel Encoder-Decoder Knowledge Graph Completion Model for Robot Brain

Song, Yichen; Li, Aiping; Tu, Hongkui; Chen, Kai; Li, Chenchen

doi:10.3389/fnbot.2021.674428

Cited by 7 publications

(4 citation statements)

References 10 publications

(30 reference statements)

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“…The “brain” of the system is a comprehensive knowledge graph [ 17 ] that contains knowledge about the user and the photos. All the knowledge is represented as a graph in which data is modeled as nodes (vertices) and links (edges) between them.…”

Section: Methodsmentioning

confidence: 99%

Promoting Personalized Reminiscence Among Cognitively Intact Older Adults Through an AI-Driven Interactive Multimodal Photo Album: Development and Usability Study

Wang,

Li,

Liang

et al. 2024

JMIR Aging

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Background Reminiscence, a therapy that uses stimulating materials such as old photos and videos to stimulate long-term memory, can improve the emotional well-being and life satisfaction of older adults, including those who are cognitively intact. However, providing personalized reminiscence therapy can be challenging for caregivers and family members. Objective This study aimed to achieve three objectives: (1) design and develop the GoodTimes app, an interactive multimodal photo album that uses artificial intelligence (AI) to engage users in personalized conversations and storytelling about their pictures, encompassing family, friends, and special moments; (2) examine the app’s functionalities in various scenarios using use-case studies and assess the app’s usability and user experience through the user study; and (3) investigate the app’s potential as a supplementary tool for reminiscence therapy among cognitively intact older adults, aiming to enhance their psychological well-being by facilitating the recollection of past experiences. Methods We used state-of-the-art AI technologies, including image recognition, natural language processing, knowledge graph, logic, and machine learning, to develop GoodTimes. First, we constructed a comprehensive knowledge graph that models the information required for effective communication, including photos, people, locations, time, and stories related to the photos. Next, we developed a voice assistant that interacts with users by leveraging the knowledge graph and machine learning techniques. Then, we created various use cases to examine the functions of the system in different scenarios. Finally, to evaluate GoodTimes’ usability, we conducted a study with older adults (N=13; age range 58-84, mean 65.8 years). The study period started from January to March 2023. Results The use-case tests demonstrated the performance of GoodTimes in handling a variety of scenarios, highlighting its versatility and adaptability. For the user study, the feedback from our participants was highly positive, with 92% (12/13) reporting a positive experience conversing with GoodTimes. All participants mentioned that the app invoked pleasant memories and aided in recollecting loved ones, resulting in a sense of happiness for the majority (11/13, 85%). Additionally, a significant majority found GoodTimes to be helpful (11/13, 85%) and user-friendly (12/13, 92%). Most participants (9/13, 69%) expressed a desire to use the app frequently, although some (4/13, 31%) indicated a need for technical support to navigate the system effectively. Conclusions Our AI-based interactive photo album, GoodTimes, was able to engage users in browsing their photos and conversing about them. Preliminary evidence supports GoodTimes’ usability and benefits cognitively intact older adults. Future work is needed to explore its potential positive effects among older adults with cognitive impairment.

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Section: Methodsmentioning

confidence: 99%

Promoting Personalized Reminiscence Among Cognitively Intact Older Adults Through an AI-Driven Interactive Multimodal Photo Album: Development and Usability Study

Wang,

Li,

Liang

et al. 2024

JMIR Aging

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“…It also incorporates the static attributes of entities and relationships obtained from the static graph into the temporal knowledge graph, significantly improving the evolutionary representation performance. The entity decoder Conv-TransD [26] and the relationship decoder Conv-TransR [27] transform entities and relationships and establish associations between them while preserving the translational features of convolutions to the maximum extent. They work collaboratively to enhance the prediction performance of entities and relationships in events.…”

Section: Overviewmentioning

confidence: 99%

IndRT-GCNets: Knowledge Reasoning with Independent Recurrent Temporal Graph Convolutional Representations

Ma,

Altenbek,

2024

CMC

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Due to the structural dependencies among concurrent events in the knowledge graph and the substantial amount of sequential correlation information carried by temporally adjacent events, we propose an Independent Recurrent Temporal Graph Convolution Networks (IndRT-GCNets) framework to efficiently and accurately capture event attribute information. The framework models the knowledge graph sequences to learn the evolutionary representations of entities and relations within each period. Firstly, by utilizing the temporal graph convolution module in the evolutionary representation unit, the framework captures the structural dependency relationships within the knowledge graph in each period. Meanwhile, to achieve better event representation and establish effective correlations, an independent recurrent neural network is employed to implement auto-regressive modeling. Furthermore, static attributes of entities in the entity-relation events are constrained and merged using a static graph constraint to obtain optimal entity representations. Finally, the evolution of entity and relation representations is utilized to predict events in the next subsequent step. On multiple real-world datasets such as Freebase13 (FB13), Freebase 15k (FB15K), WordNet11 (WN11), WordNet18 (WN18), FB15K-237, WN18RR, YAGO3-10, and Nell-995, the results of multiple evaluation indicators show that our proposed IndRT-GCNets framework outperforms most existing models on knowledge reasoning tasks, which validates the effectiveness and robustness.

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“…Due to the advantage of storing large ontologies and relationships in the knowledge graph [24,25], we can construct a knowledge repository simulating the human brain to better understand the world [26]. However, unlike the extraordinarily large amount of data from various fields stored in the human brain, the knowledge graph built in this study is smaller in size and specific to the field of HR remote sensing.…”

Section: Knowledge Graph Constructionmentioning

confidence: 99%

Section: Knowledge Graph Constructionmentioning

confidence: 99%

Novel Knowledge Graph- and Knowledge Reasoning-Based Classification Prototype for OBIA Using High Resolution Remote Sensing Imagery

Gun

Chen

2023

Remote Sensing

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Although many machine learning methods have been successfully applied for the object-based classification of high resolution (HR) remote sensing imagery, current methods are highly dependent on the spectral similarity between segmented objects and have disappointingly poor performance when dealing with different segmented objects that have similar spectra. To overcome this limitation, this study exploited a knowledge graph (KG) that preserved the spatial relationships between segmented objects and has a reasoning capability that can assist in improving the probability of correctly classifying different segmented objects with similar spectra. In addition, to assist the knowledge graph classifications, an image segmentation method generating segmented objects that closely resemble real ground objects in size was used, which improves the integrity of the object classification results. Therefore, a novel HR remote sensing image classification scheme is proposed that involves a knowledge graph and an optimal segmentation algorithm, which takes full advantage of object-based classification and knowledge inference. This method effectively addresses the problems of object classification integrity and misclassification of objects with the same spectrum. In the evaluation experiments, three QuickBird-2 images and over 15 different land cover classes were utilized. The results showed that the classification accuracy of the proposed method is high, with overall accuracies exceeding 0.85. These accuracies are higher than the K Nearest Neighbors (KNN), Support Vector Machine (SVM), Decision Tree (DT), and Random Forest (RF) methods. The evaluated results confirmed that the proposed method offers excellent performance in HR remote sensing image classification.

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A Novel Encoder-Decoder Knowledge Graph Completion Model for Robot Brain

Cited by 7 publications

References 10 publications

Promoting Personalized Reminiscence Among Cognitively Intact Older Adults Through an AI-Driven Interactive Multimodal Photo Album: Development and Usability Study

Promoting Personalized Reminiscence Among Cognitively Intact Older Adults Through an AI-Driven Interactive Multimodal Photo Album: Development and Usability Study

IndRT-GCNets: Knowledge Reasoning with Independent Recurrent Temporal Graph Convolutional Representations

Novel Knowledge Graph- and Knowledge Reasoning-Based Classification Prototype for OBIA Using High Resolution Remote Sensing Imagery

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