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A Review of Relational Machine Learning for Knowledge Graphs

Maximilian Nickel, Kevin Murphy, Volker Tresp, Evgeniy Gabrilovich
2016 Proceedings of the IEEE  
Relational machine learning studies methods for the statistical analysis of relational, or graph-structured, data.  ...  In this paper, we provide a review of how such statistical models can be "trained" on large knowledge graphs, and then used to predict new facts about the world (which is equivalent to predicting new edges  ...  We provided a review of state-of-the-art statistical relational learning (SRL) methods applied to very large knowledge graphs.  ... 
doi:10.1109/jproc.2015.2483592 fatcat:uk6xvh5xljgf7aytfadzwzncsi

Constructing Knowledge Graphs and Their Biomedical Applications

David Nicholson, Casey S. Greene
2020 Computational and Structural Biotechnology Journal  
A number of techniques are used to represent knowledge graphs, but often machine learning methods are used to construct a low-dimensional representation that can support many different applications.  ...  In this review, we discuss how these graphs are constructed and applied with a particular focus on how machine learning approaches are changing these processes.  ...  In practice, is usually used to represent nodes in a knowledge graph and can be used as input for machine learning classi ers to perform tasks such as link prediction or node classi cation [141] ; however  ... 
doi:10.1016/j.csbj.2020.05.017 pmid:32637040 pmcid:PMC7327409 fatcat:eontflxz3fggdnw3jzajzr2bdu

A Survey on State-of-the-art Techniques for Knowledge Graphs Construction and Challenges ahead [article]

Ali Hur, Naeem Janjua, Mohiuddin Ahmed
2021 arXiv   pre-print
Automated schemes can reduce the cost of building knowledge graphs up to 15-250 times.  ...  This paper critiques state-of-the-art automated techniques to produce knowledge graphs of near-human quality autonomously.  ...  Smart Cloud, relational machine learning for knowledge graphs," Proc. IEEE, vol.  ... 
arXiv:2110.08012v2 fatcat:q6utzgjahfehpftol3dttgolui

Mobile Link Prediction: Automated Creation and Crowd-sourced Validation of Knowledge Graphs [article]

Mark Christopher Ballandies, Evangelos Pournaras
2020 arXiv   pre-print
The knowledge graph grows via automated link predictions using genetic programming that are validated by humans for improving transparency and calibrating accuracy.  ...  Building trustworthy knowledge graphs for cyber-physical social systems (CPSS) is a challenge.  ...  Automated knowledge graph building via link prediction In this work, link prediction is utilized to automate the completion of a knowledge graph by predicting links between existing instances.  ... 
arXiv:2006.16858v1 fatcat:w53nldzryrb4jngyczyenktr3a

SoftNER: Mining Knowledge Graphs From Cloud Incidents [article]

Manish Shetty, Chetan Bansal, Sumit Kumar, Nikitha Rao, Nachiappan Nagappan
2021 arXiv   pre-print
Next, we present an approach to mine relations between the named entities for automatically constructing knowledge graphs.  ...  In this work, we address the fundamental problem of structured knowledge extraction from service incidents. We have built SoftNER, a framework for mining Knowledge Graphs from incident reports.  ...  Figure 7 shows a sub-graph of related entity types in the entity knowledge graph constructed from our incident data set.  ... 
arXiv:2101.05961v2 fatcat:envypijvyvej3lieo3yqudf6ri

Domain-specific Knowledge Graphs: A survey [article]

Bilal Abu-Salih
2021 arXiv   pre-print
Also, the paper presents a thorough review of the state-of-the-art approaches drawn from academic works relevant to seven domains of knowledge.  ...  This is leveraged by the underlying structure of the KG which underpins a better comprehension, reasoning and interpretation of knowledge for both human and machine.  ...  Findings from the Survey The review of KG construction approaches which are drawn from academic works in seven domain reveals a correlated array of limitations and deficiencies related to the following  ... 
arXiv:2011.00235v3 fatcat:oc2loewqdjfgvlapy4kmult5li

Low-resource Learning with Knowledge Graphs: A Comprehensive Survey [article]

Jiaoyan Chen and Yuxia Geng and Zhuo Chen and Jeff Z. Pan and Yuan He and Wen Zhang and Ian Horrocks and Huajun Chen
2021 arXiv   pre-print
Machine learning methods especially deep neural networks have achieved great success but many of them often rely on a number of labeled samples for training.  ...  Among all the low-resource learning studies, many prefer to utilize some auxiliary information in the form of Knowledge Graph (KG), which is becoming more and more popular for knowledge representation,  ...  Meta Relational Learning for Few-Shot Link Prediction in Knowledge Graphs.  ... 
arXiv:2112.10006v3 fatcat:wkz6gjx4r5gvlhh673p3rqsmgi

Survey on graph embeddings and their applications to machine learning problems on graphs

Ilya Makarov, Dmitrii Kiselev, Nikita Nikitinsky, Lovro Subelj
2021 PeerJ Computer Science  
Going further, we perform a thorough evaluation of graph embedding applications to machine learning problems on graphs, among which are node classification, link prediction, clustering, visualization,  ...  in graph embeddings quality in the four classic machine learning problems on graphs, such as node classification, link prediction, clustering and graph visualization.  ...  Knowledge graph completion Knowledge graph embedding aims to learn vectors for entities and multi-dimensional vectors for entity relations.  ... 
doi:10.7717/peerj-cs.357 pmid:33817007 pmcid:PMC7959646 fatcat:ntronyrbgfbedez5dks6h4hoq4

Automated Machine Learning on Graphs: A Survey [article]

Ziwei Zhang, Xin Wang, Wenwu Zhu
2021 arXiv   pre-print
This paper is the first systematic and comprehensive review of automated machine learning on graphs to the best of our knowledge.  ...  To solve this critical challenge, automated machine learning (AutoML) on graphs which combines the strength of graph machine learning and AutoML together, is gaining attention from the research community  ...  of China No.62050110.  ... 
arXiv:2103.00742v3 fatcat:d7h4vjeksvh5lbg5qns7lvh2su

Knowledge Graphs in Manufacturing and Production: A Systematic Literature Review

Georg Buchgeher, David Gabauer, Jorge Martinez-Gil, Lisa Ehrlinger
2021 IEEE Access  
A machine learning component analyzes the KG and event data (log files) to complete the KG with additional links.  ...  In 2 studies a system for the automated construction of a knowledge graph was proposed, 1 study proposed a KG management system.  ...  fundamental research projects related to knowledge-based technologies.  ... 
doi:10.1109/access.2021.3070395 fatcat:gxawnftgkbbt3fm52ybpiqplyi

SeMi: A SEmantic Modeling machIne to build Knowledge Graphs with graph neural networks

Giuseppe Futia, Antonio Vetrò, Juan Carlos De Martin
2020 SoftwareX  
SeMi (SEmantic Modeling machIne) is a tool to semi-automatically build large-scale Knowledge Graphs from structured sources such as CSV, JSON, and XML files.  ...  This contribution can be considered as a step ahead towards automatic and scalable approaches for building Knowledge Graphs.  ...  Acknowledgments Computational resources provided by hpc@polito, which is a project of Academic Computing within the Department of Control and Computer Engineering at the Politecnico di Torino (http://  ... 
doi:10.1016/j.softx.2020.100516 fatcat:pasnkbc73rbblo4deocva32h2i

Knowledge Graphs [article]

Aidan Hogan, Eva Blomqvist, Michael Cochez, Claudia d'Amato, Gerard de Melo, Claudio Gutierrez, José Emilio Labra Gayo, Sabrina Kirrane, Sebastian Neumaier, Axel Polleres, Roberto Navigli, Axel-Cyrille Ngonga Ngomo (+6 others)
2021 arXiv   pre-print
In this paper we provide a comprehensive introduction to knowledge graphs, which have recently garnered significant attention from both industry and academia in scenarios that require exploiting diverse  ...  We discuss the roles of schema, identity, and context in knowledge graphs. We explain how knowledge can be represented and extracted using a combination of deductive and inductive techniques.  ...  Acknowledgements: We thank the attendees of the Dagstuhl Seminar on "Knowledge Graphs" for discussions that inspired and influenced this paper, and all those that make such seminars possible.  ... 
arXiv:2003.02320v5 fatcat:ab4hmm2f2bbpvobwkjw4xbrz4u

Open Graph Benchmark: Datasets for Machine Learning on Graphs [article]

Weihua Hu, Matthias Fey, Marinka Zitnik, Yuxiao Dong, Hongyu Ren, Bowen Liu, Michele Catasta, Jure Leskovec
2021 arXiv   pre-print
We present the Open Graph Benchmark (OGB), a diverse set of challenging and realistic benchmark datasets to facilitate scalable, robust, and reproducible graph machine learning (ML) research.  ...  OGB datasets are large-scale, encompass multiple important graph ML tasks, and cover a diverse range of domains, ranging from social and information networks to biological networks, molecular graphs, source  ...  Finally, we thank the entire community of graph ML for providing valuable feedback to improve OGB.  ... 
arXiv:2005.00687v7 fatcat:wwqiss2naratnfmwmqdoko272m

Neural Graph Embedding Methods for Natural Language Processing [article]

Shikhar Vashishth
2020 arXiv   pre-print
Existing approaches to handle such graphs suffer from over-parameterization and are restricted to learning representation of nodes only.  ...  In the second part of the thesis, we utilize GCNs for Document Timestamping problem and for learning word embeddings using dependency context of a word instead of sequential context.  ...  We propose CompGCN, a novel Graph Convolutional based framework for multi-relational graphs which leverages a variety of composition operators from Knowledge Graph embedding techniques to embed nodes and  ... 
arXiv:1911.03042v3 fatcat:fruw547yxnev5pmnlij76wovcy

Knowledge Graphs Representation for Event-Related E-News Articles

M.V.P.T. Lakshika, H.A. Caldera
2021 Machine Learning and Knowledge Extraction  
Thus, there is a striking concentration on constructing knowledge graphs by combining the background information related to the subjects in text documents.  ...  We propose an enhanced representation of a scalable knowledge graph by automatically extracting the information from the corpus of e-news articles and determine whether a knowledge graph can be used as  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/make3040040 fatcat:hwspn2ma4vh5fn3o4oh3zgwj6m
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