A Survey on Approaches and Applications of Knowledge Representation Learning

Li, Chenchen; Li, Aiping; Wang, Ye; Tu, Hongkui; Song, Yichen

doi:10.1109/dsc50466.2020.00055

Cited by 9 publications

(3 citation statements)

References 34 publications

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“…Knowledge RL aims to embed entities and relationships into low-dimensional continuous value vector space, so that the semantic association between them can be calculated in this space [38]. Knowledge graph represented by fact triple is composed of entity and relation.…”

Section: Knowledge Rl Methodsmentioning

confidence: 99%

Recent Advances in Representation Learning for Electronic Health Records: A Systematic Review

Liu

Wang

et al. 2022

J. Phys.: Conf. Ser.

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Representation Learning (RL) aims to convert data into low-dimensional and dense real-valued vectors, so as to realize reasoning in vector space. RL is one of the important research contents in the analysis of health data. This paper systematically reviews the latest research on Electronic Health Records (EHR) RL. We searched the Web of Science, Google Scholar, and Association for Computing Machinery Digital Library for papers involving EHR RL. On the basis of literature review, we propose a new taxonomy to categorize the state-of-the-art EHR RL methods into three categories: statistics learning-based RL methods, knowledge RL methods and graph RL methods. We analyze and summarize their characteristics according to the input data form and underlying learning mechanisms. In addition, we provide evaluation strategies to verify the quality of EHR representations from both intrinsic and extrinsic perspectives. Finally, we put forward three promising research directions to promote future research. Overall, this survey aims to provide a profound overview of state-of-the-art developments in the field of EHR RL and to help researchers find the most appropriate methods.

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Section: Knowledge Rl Methodsmentioning

confidence: 99%

Recent Advances in Representation Learning for Electronic Health Records: A Systematic Review

Liu

Wang

et al. 2022

J. Phys.: Conf. Ser.

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“…Typical examples of symbolic knowledge are textual descriptions, graph-based definitions or propositional logical rules. The research area of Knowledge Representation Learning (KRL) aims to represent prior knowledge, e.g., entities, relations or rules into embeddings that can be used to improve or solve inference or reasoning tasks ( [439], [424]). Most existing literature narrows down the problem by defining KRL as converting prior knowledge from KGs only [439].…”

Section: Author: Stefan Zwicklbauermentioning

confidence: 99%

Knowledge Augmented Machine Learning with Applications in Autonomous Driving: A Survey

Wörmann¹,

Bogdoll²,

Bührle³

et al. 2022

Preprint

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The existence of representative datasets is a prerequisite of many successful artificial intelligence and machine learning models. However, the subsequent application of these models often involves scenarios that are inadequately represented in the data used for training. The reasons for this are manifold and range from time and cost constraints to ethical considerations. As a consequence, the reliable use of these models, especially in safety-critical applications, is a huge challenge. Leveraging additional, already existing sources of knowledge is key to overcome the limitations of purely data-driven approaches, and eventually to increase the generalization capability of these models. Furthermore, predictions that conform with knowledge are crucial for making trustworthy and safe decisions even in underrepresented scenarios. This work provides an overview of existing techniques and methods in the literature that combine data-based models with existing knowledge. The identified approaches are structured according to the categories integration, extraction and conformity. Special attention is given to applications in the field of autonomous driving.Acknowledgement: The research leading to these results is funded by the German Federal Ministry for Economic Affairs and Climate Action within the project "KI Wissen -Entwicklung von Methoden für die Einbindung von Wissen in maschinelles Lernen". The authors would like to thank the consortium for the successful cooperation.

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“…Aiming at completing missing entries, Knowledge Base Completion (KBC), also known as Link Prediction, plays a crucial role in constructing large-scale knowledge graphs [Nickel et al, 2016, Ji et al, 2020, Li et al, 2020. Over the past years, most of the research on KBC has been focusing on Knowledge Graph Embedding models, which learn representations for all entities and relations in a Knowledge Graph, and use them for scoring whether an edge exists or not [Nickel et al, 2016].…”

Section: Introductionmentioning

confidence: 99%

Relation Prediction as an Auxiliary Training Objective for Improving Multi-Relational Graph Representations

Chen¹,

Minervini²,

Riedel³

et al. 2021

Preprint

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Learning good representations on multi-relational graphs is essential to knowledge base completion (KBC). In this paper, we propose a new self-supervised training objective for multirelational graph representation learning, via simply incorporating relation prediction into the commonly used 1vsAll objective. The new training objective contains not only terms for predicting the subject and object of a given triple, but also a term for predicting the relation type. We analyse how this new objective impacts multi-relational learning in KBC: experiments on a variety of datasets and models show that relation prediction can significantly improve entity ranking, the most widely used evaluation task for KBC, yielding a 6.1% increase in MRR and 9.9% increase in Hits@1 on FB15k-237 as well as a 3.1% increase in MRR and 3.4% in Hits@1 on Aristo-v4. Moreover, we observe that the proposed objective is especially effective on highly multi-relational datasets, i.e. datasets with a large number of predicates, and generates better representations when larger embedding sizes are used. Code will be available at https://github.com/facebookresearch/ssl-relation-prediction.

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A Survey on Approaches and Applications of Knowledge Representation Learning

Cited by 9 publications

References 34 publications

Recent Advances in Representation Learning for Electronic Health Records: A Systematic Review

Recent Advances in Representation Learning for Electronic Health Records: A Systematic Review

Knowledge Augmented Machine Learning with Applications in Autonomous Driving: A Survey

Relation Prediction as an Auxiliary Training Objective for Improving Multi-Relational Graph Representations

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