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Multi-Paragraph Reasoning with Knowledge-enhanced Graph Neural Network [article]

Deming Ye, Yankai Lin, Zhenghao Liu, Zhiyuan Liu, Maosong Sun
2019 arXiv   pre-print
In this work, we propose a knowledge-enhanced graph neural network (KGNN), which performs reasoning over multiple paragraphs with entities.  ...  To explicitly capture the entities' relatedness, KGNN utilizes relational facts in knowledge graph to build the entity graph.  ...  introduce entity graph neural network to reason over entities for Knowledge Base QA (KBQA).  ... 
arXiv:1911.02170v1 fatcat:2fzzx7vjxjhbviy2twmhj7l7xa

A Survey on Neural-symbolic Systems [article]

Dongran Yu, Bo Yang, Dayou Liu, Hui Wang
2021 arXiv   pre-print
In this case, an ideal intelligent system--a neural-symbolic system--with high perceptual and cognitive intelligence through powerful learning and reasoning capabilities gains a growing interest in the  ...  Combining the fast computation ability of neural systems and the powerful expression ability of symbolic systems, neural-symbolic systems can perform effective learning and reasoning in multi-domain tasks  ...  They utilized knowledge graphs to model the correlations between seen and unseen classes and combines with neural networks to propose a knowledge graph transfer network model (KGTN) for the problem of  ... 
arXiv:2111.08164v1 fatcat:bc33afiitnb73bmjtrfbdgkwpy

A Review of Inference Methods Based on Knowledge Graph [chapter]

Dexiang Zhang, Hairong Wang, Yudan Ding
2020 Frontiers in Artificial Intelligence and Applications  
According to the different methods adopted for each type, each type also includes reasoning based on distributed representation; reasoning based on neural network and mixed reasoning.  ...  According to the types of reasoning methods, knowledge reasoning methods based on knowledge graph can be divided into single-step reasoning and multi-step reasoning.  ...  Neural Network Reasoning In single-step reasoning, neural network-based reasoning uses the neural network to directly model the knowledge graph fact tuple, and obtain the vector representation of the fact  ... 
doi:10.3233/faia200727 fatcat:wnjuq5lkbbdh7gzcoukri5nciy

Unifying Neural Learning and Symbolic Reasoning for Spinal Medical Report Generation [article]

Zhongyi Han, Benzheng Wei, Yilong Yin, Shuo Li
2020 arXiv   pre-print
Concretely, we design an adversarial graph network that interpolates a symbolic graph reasoning module into a generative adversarial network through embedding prior domain knowledge, achieving semantic  ...  In this paper, we propose the neural-symbolic learning (NSL) framework that performs human-like learning by unifying deep neural learning and symbolic logical reasoning for the spinal medical report generation  ...  To endow the local convolution networks with the capability of global graph reasoning, Liang et al. (2018) introduced a new graph layer, symbolic graph reasoning layer, to embed the external human knowledge  ... 
arXiv:2004.13577v1 fatcat:oh5aka5zr5be3ipd7qqnyhikzy

Neuro-Symbolic Learning: Principles and Applications in Ophthalmology [article]

Muhammad Hassan, Haifei Guan, Aikaterini Melliou, Yuqi Wang, Qianhui Sun, Sen Zeng, Wen Liang, Yiwei Zhang, Ziheng Zhang, Qiuyue Hu, Yang Liu, Shunkai Shi (+15 others)
2022 arXiv   pre-print
Neural networks have been rapidly expanding in recent years, with novel strategies and applications.  ...  Attempts have been made to overcome the challenges in neural network computing by representing and embedding domain knowledge in terms of symbolic representations.  ...  graph neural network.  ... 
arXiv:2208.00374v1 fatcat:pktmnomj3bbwpjyj7lmu37rl7i

Brain-inspired Graph Spiking Neural Networks for Commonsense Knowledge Representation and Reasoning [article]

Hongjian Fang, Yi Zeng, Jianbo Tang, Yuwei Wang, Yao Liang, Xin Liu
2022 arXiv   pre-print
and reasoning spiking neural networks with solid biological plausibility.  ...  The neuron populations of different communities together constitute the entire commonsense knowledge graph, forming a giant graph spiking neural network.  ...  reasoning by combining GNN with knowledge graphs [25] .  ... 
arXiv:2207.05561v1 fatcat:ywnyiylgbje6fhyowtkj6x57km

Knowledge graph using resource description framework and connectionist theory

Ravi Lourdusamy, Xavierlal J Mattam
2020 Journal of Physics, Conference Series  
The weighted RDF in Graph Neural Network will represent the knowledge graph using RDF and connectionist theory.  ...  This article presents the use of weighted RDF as a vector embedding of RDF that could be used with Bayesian networks in Graph Neural Networks.  ...  Motivation The connectionist or sub-symbolic approaches employing artificial neural network fundamentally differs from the symbolic approaches that use logic and reasoning in a knowledge graph.  ... 
doi:10.1088/1742-6596/1427/1/012001 fatcat:uqnd3tliczdhtarlu256inzjtu

Machine Learning Meets the Semantic Web

Konstantinos Ilias Kotis, Konstantina Zachila, Evaggelos Paparidis
2021 Artificial Intelligence Advances  
The emerging Graph Neural Networks (GNN) can extract both object characteristics and relations from KGs.  ...  A Knowledge Graph (KG) can illustrate high-order relations that connect two objects with one or multiple related attributes.  ...  Recurrent Graph Neural Networks aim to learn node representations with recurrent neural architectures.  ... 
doi:10.30564/aia.v3i1.3178 fatcat:vq47pxxmkja2blzz4gaterau7q

Visual Experience-Based Question Answering with Complex Multimodal Environments

Incheol Kim, Jiayi Ma
2020 Mathematical Problems in Engineering  
To address this VEQA problem, we propose a hybrid visual question answering system, VQAS, integrating a deep neural network-based scene graph generation model and a rule-based knowledge reasoning system  ...  Moreover, it can answer complex questions through knowledge reasoning with rich background knowledge.  ...  Different from the pure deep neural network-based models, the proposed knowledge reasoning system can use a rich knowledge source to answer questions by combining the shallow knowledge in 3D scene graphs  ... 
doi:10.1155/2020/8567271 fatcat:cgmzylh4ujadfisikt5hekvrga

Natural Language QA Approaches using Reasoning with External Knowledge [article]

Chitta Baral, Pratyay Banerjee, Kuntal Kumar Pal, Arindam Mitra
2020 arXiv   pre-print
use in various NLQA models have brought the issue of NLQA using "reasoning" with external knowledge to the forefront.  ...  We believe our survey will help establish a bridge between multiple fields of AI, especially between (a) the traditional fields of knowledge representation and reasoning and (b) the field of NL understanding  ...  While knowledge in the form of undirected graphs are mainly used by the graph-based reasoning systems like Graph Neural Networks (GNN) [66] , the directed graphs are better handled by convolutional neural  ... 
arXiv:2003.03446v1 fatcat:5ssmvcdzajc5flasg3s5hsfxsu

Special issue on semantic deep learning

Dagmar Gromann, Luis Espinosa Anke, Thierry Declerck, Pascal Hitzler, Krzysztof Janowicz
2019 Semantic Web Journal  
Approaches range from utilizing structured knowledge in the training process of neural networks to enriching such architectures with ontological reasoning mechanisms.  ...  Bridging the neural-symbolic gap by joining deep learning and Semantic Web not only holds the potential of improving performance but also of opening up new avenues of research.  ...  Responsibility for the content of this workshop is with the editor(s).  ... 
doi:10.3233/sw-190364 fatcat:hnmi2xowhvdvdheagxuzlnmqmu

Knowledge Graph Completion: A Review

Zhe Chen, Yuehan Wang, Bin Zhao, Jing Cheng, Xin Zhao, Zongtao Duan
2020 IEEE Access  
The former mainly includes rule-based reasoning method, probability graph model, such as Markov logic network, and graph computation based method.  ...  Knowledge graph completion (KGC) is a hot topic in knowledge graph construction and related applications, which aims to complete the structure of knowledge graph by predicting the missing entities or relationships  ...  graph completion method based on neural network applies strong learning and expression ability of neural network to model the knowledge graph, which can obtain good reasoning ability [27] .  ... 
doi:10.1109/access.2020.3030076 fatcat:jbimdngcmrbx3jhihsgrp62cxq

Making Neural Networks FAIR [article]

Anna Nguyen and Tobias Weller and Michael Färber and York Sure-Vetter
2020 arXiv   pre-print
Secondly, we have modeled over 18,400 neural networks from GitHub based on this ontology, which we provide to the public as a knowledge graph called FAIRnets, ready to be used for recommending suitable  ...  neural networks to data scientists.  ...  CNNs follows with 36% and RNN with 16% of total number of neural networks. Statistical Analysis of the FAIRnets knowledge graph.  ... 
arXiv:1907.11569v4 fatcat:3pbgjqst6bg2jkcpwfq6x6anjm

A Proposal for Common Dataset in Neural-Symbolic Reasoning Studies

Özgür Yilmaz, Artur S. d'Avila Garcez, Daniel L. Silver
2016 International Workshop on Neural-Symbolic Learning and Reasoning  
We promote and analyze the needs of a common publicly available benchmark dataset to be used for neural-symbolic studies of learning and reasoning.  ...  Along with the original tasks that were suggested by the Visual Genome creators, we propose neural-symbolic tasks that can be used as challenges to promote research in the field and competition between  ...  They contain three main components: (1) knowledge encoding and reasoning in neural networks, (2) knowledge evolution and network learning, and (3) knowledge extraction from trained networks.  ... 
dblp:conf/nesy/YilmazGS16 fatcat:qf3grff5nbbjdbszeihh5ugghy

Neural-Symbolic Reasoning over Knowledge Graph for Multi-stage Explainable Recommendation [article]

Yikun Xian, Zuohui Fu, Qiaoying Huang, S. Muthukrishnan, Yongfeng Zhang
2020 arXiv   pre-print
from knowledge graph.  ...  Moreover, direct reasoning over large-scale knowledge graph can be costly due to the huge search space of pathfinding.  ...  Figure 1: A coarse-to-fine process of neural-symbolic reasoning over knowledge graph for explainable recommendation.  ... 
arXiv:2007.13207v1 fatcat:we2und23rvbghoprpjnzbgsxeu
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