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A Hybrid Model for Learning Embeddings and Logical Rules Simultaneously from Knowledge Graphs [article]

Susheel Suresh, Jennifer Neville
2020 arXiv   pre-print
We aim to leverage the complementary properties of both methods to develop a hybrid model that learns both high-quality rules and embeddings simultaneously.  ...  The problem of knowledge graph (KG) reasoning has been widely explored by traditional rule-based systems and more recently by knowledge graph embedding methods.  ...  ACKNOWLEDGMENT This research is supported by NSF under contract numbers CCF-1918483, IIS-1618690, and CCF-0939370.  ... 
arXiv:2009.10800v1 fatcat:jaipkweslvg6xaomiupgkmnjpu

Knowledge Graph Reasoning with Logics and Embeddings: Survey and Perspective [article]

Wen Zhang, Jiaoyan Chen, Juan Li, Zezhong Xu, Jeff Z. Pan, Huajun Chen
2022 arXiv   pre-print
Knowledge graph (KG) reasoning is becoming increasingly popular in both academia and industry.  ...  A promising direction is to integrate both logic-based and embedding-based methods, with the vision to have advantages of both.  ...  Embeddings for Logic Learning Logic learning is to learn patterns from KGs and discover potential (and probabilistic) logics such as schemas and logic rules.  ... 
arXiv:2202.07412v1 fatcat:ou6ioak6affevo4e6sz2mebbvm

Knowledge Graph Embeddings [chapter]

Paolo Rosso, Dingqi Yang, Philippe Cudré-Mauroux
2018 Encyclopedia of Big Data Technologies  
For each fact of the logical rule, a score is computed in order to indicate whether the ground rule is satisfied or not. The embedding model is then learned based on the unified facts and rules.  ...  Learning knowledge graph embeddings Learning KG embeddings consists in two key steps in general: 1.  ... 
doi:10.1007/978-3-319-63962-8_284-1 fatcat:xwmtv26vyrayvk3uwudy32xaz4

Neural, Symbolic and Neural-Symbolic Reasoning on Knowledge Graphs [article]

Jing Zhang, Bo Chen, Lingxi Zhang, Xirui Ke, Haipeng Ding
2021 arXiv   pre-print
In this survey, we take a thorough look at the development of the symbolic, neural and hybrid reasoning on knowledge graphs.  ...  We survey two specific reasoning tasks, knowledge graph completion and question answering on knowledge graphs, and explain them in a unified reasoning framework.  ...  Both MLN and ProbLog learn the probabilities for rules, where MLN builds a global probabilistic graph for all the rules and learns the probabilities for all the rules simultaneously, but ProbLog constructs  ... 
arXiv:2010.05446v5 fatcat:tc6fowebkzbv7df3cjyhkcu6uq

Incorporating Context Graph with Logical Reasoning for Inductive Relation Prediction

Qika Lin, Jun Liu, Fangzhi Xu, Yudai Pan, Yifan Zhu, Lingling Zhang, Tianzhe Zhao
2022 Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval  
To this end, we propose a novel model ConGLR to incorporate context graph with logical reasoning.  ...  Relation prediction on knowledge graphs (KGs) aims to infer missing valid triples from observed ones.  ...  In ConGLR, the context graph is introduced to simultaneously acquire relation embeddings in the modeling level and represent logic rules in the reasoning level.  ... 
doi:10.1145/3477495.3531996 fatcat:2hffwilib5cljpmnaososkqcs4

Best-first Enumeration Based on Bounding Conflicts, and its Application to Large-scale Hybrid Estimation (Extended Abstract)

Eric Timmons, Brian C. Williams
2020 Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence  
State estimation methods based on hybrid discrete and continuous state models have emerged as a method of precisely computing belief states for real world systems, however they have difficulty scaling  ...  While best-first methods have been developed for hybrid estimation, conflict-directed methods have thus far been elusive as conflicts summarize constraint violations, but probabilistic hybrid estimation  ...  Acknowledgments This work was supported by the UK EPSRC grants EP/J008-346/1, EP/R013667/1, EP/L012138/1, and EP/M025268/1, and the Alan Turing Institute under the EPSRC grant EP/ N510129/1.  ... 
doi:10.24963/ijcai.2020/707 dblp:conf/ijcai/HoheneckerL20 fatcat:b4fsi5w4fbgyjebqcnifojqb64

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
Attempts have been made to overcome the challenges in neural network computing by representing and embedding domain knowledge in terms of symbolic representations.  ...  This review presents a comprehensive survey on the state-of-the-art NeSyL approaches, their principles, advances in machine and deep learning algorithms, applications such as opthalmology, and most importantly  ...  Graphs enable AI to reason and learn based on common sense, prior knowledge, and statistics. For a graph-based AI, new knowledge will be integrated into the larger graph via logics.  ... 
arXiv:2208.00374v1 fatcat:pktmnomj3bbwpjyj7lmu37rl7i

A Survey of Knowledge Reasoning based on KG

Rui Lu, Zhiping Cai, Shan Zhao
2019 IOP Conference Series: Materials Science and Engineering  
It uses some inference models and algorithms to infer new unknown knowledge and targets at improving the completeness and accuracy of KG.  ...  This article presents a brief overview of KR based on KG, expounds the connotation and research scope of it, judges the two main research directions(Knowledge Graph Completion(KGC) and Question Answering  ...  [55] integrated logical-probabilistic KRR with model-based RL, enabling agents to simultaneously reason with declarative knowledge and learn from interaction experiences. Toutanova et al.  ... 
doi:10.1088/1757-899x/569/5/052058 fatcat:erpnnqzsy5hmhbgidjsb7dmplu

Sentic Computing

Erik Cambria, Amir Hussain
2015 Cognitive Computation  
Such a knowledge graph is then exploited by a graph-propagation algorithm that propagates sentiment information learned from labeled datasets.  ...  and irregular, syntactical and semantic rules proper of a language.  ... 
doi:10.1007/s12559-015-9325-0 fatcat:p2l2e5osbncbtplvvt6w25dcke

Ontology Reasoning with Deep Neural Networks

Patrick Hohenecker, Thomas Lukasiewicz
2020 The Journal of Artificial Intelligence Research  
In this paper, we employ state-of-the-art methods for training deep neural networks to devise a novel model that is able to learn how to effectively perform logical reasoning in the form of basic ontology  ...  Traditionally, logic-based symbolic methods from the field of knowledge representation and reasoning have been used to equip agents with capabilities that resemble human logical reasoning qualities.  ...  Acknowledgments This work was supported by the UK Engineering and Physical Sciences Research Council (EPSRC) under the grants EP/J008346/1, EP/R013667/1, EP/L012138/1, and EP/M0-25268/1, as well as the  ... 
doi:10.1613/jair.1.11661 fatcat:i6bpywvdtrhglhrsztw2q3npni

A Survey on Knowledge Graphs: Representation, Acquisition and Applications [article]

Shaoxiong Ji and Shirui Pan and Erik Cambria and Pekka Marttinen and Philip S. Yu
2021 IEEE Transactions on Neural Networks and Learning Systems   accepted
For knowledge acquisition, especially knowledge graph completion, embedding methods, path inference, and logical rule reasoning, are reviewed.  ...  Knowledge graph embedding is organized from four aspects of representation space, scoring function, encoding models, and auxiliary information.  ...  Research efforts have been made for learning temporal and relational embedding simultaneously. Relevant models for dynamic network embedding also inspire temporal knowledge graph embedding.  ... 
doi:10.1109/tnnls.2021.3070843 pmid:33900922 arXiv:2002.00388v4 fatcat:4l2yxnf3wbg4zpzdumduvyr4he

Boosting Intelligent Data Analysis in Smart Sensors by Integrating Knowledge and Machine Learning

Piotr Łuczak, Przemysław Kucharski, Tomasz Jaworski, Izabela Perenc, Krzysztof Ślot, Jacek Kucharski
2021 Sensors  
We show that the proposed hybrid structure successfully combines knowledge and learning, providing high recognition performance even for very limited training datasets, while also benefiting from an abundance  ...  The postulated concept integrates prior knowledge with learning from examples, thus allowing sensor devices to be used for the successful execution of machine learning even when the volume of training  ...  This asymmetry isolates knowledge from concepts learned by conventional neurons, which are not suitable for 'logical' interpretation, enabling rule explainability at any phase of training.  ... 
doi:10.3390/s21186168 pmid:34577375 pmcid:PMC8473127 fatcat:ltyovpqv5ngwfcaw2t7lc7pu7y

Deep Algorithmic Question Answering: Towards a Compositionally Hybrid AI for Algorithmic Reasoning [article]

Kwabena Nuamah
2021 arXiv   pre-print
system should possess, and conclude that they are best achieved with a combination of hybrid and compositional AI.  ...  We argue that the challenge of algorithmic reasoning in QA can be effectively tackled with a "systems" approach to AI which features a hybrid use of symbolic and sub-symbolic methods including deep neural  ...  Acknowledgment The author would like to thank Vaishak Belle, Alan Bundy and Thomas Fletcher for feedback on an earlier draft and Huawei for supporting the research on which this paper was based under grant  ... 
arXiv:2109.08006v3 fatcat:35ugb3qfdvg2djua7tvh4asll4

A Survey of Knowledge Enhanced Pre-trained Models [article]

Jian Yang, Gang Xiao, Yulong Shen, Wei Jiang, Xinyu Hu, Ying Zhang, Jinghui Peng
2022 arXiv   pre-print
In this survey, we provide a comprehensive overview of KEPTMs in NLP and CV. We first introduce the progress of pre-trained models and knowledge representation learning.  ...  Pre-trained models with knowledge injection, which we call knowledge enhanced pre-trained models (KEPTMs), possess deep understanding and logical reasoning and introduce interpretability.  ...  [20] designed a graph-based model that extracts relational triplets from retrieved sentences and constructs self-defined graphs for it.  ... 
arXiv:2110.00269v3 fatcat:b2g3ezuplvftfp7zlehvogd44m

VOGUE: Answer Verbalization through Multi-Task Learning [article]

Endri Kacupaj, Shyamnath Premnadh, Kuldeep Singh, Jens Lehmann, Maria Maleshkova
2021 arXiv   pre-print
The VOGUE framework attempts to generate a verbalized answer using a hybrid approach through a multi-task learning paradigm.  ...  .), users prefer verbalized answers instead of a generated response. This paper addresses the task of answer verbalization for (complex) question answering over knowledge graphs.  ...  Approach In question answering, the input data consists of question u and its answer a, extracted from the knowledge graph.  ... 
arXiv:2106.13316v2 fatcat:4nvt3gtje5dsfcqabhmms44vge
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