Filters








110,426 Hits in 5.9 sec

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

A Review on Semantic web based E-learning Applications

Vinay M, Dr. Deepaanand
2017 IOSR Journal of Computer Engineering  
Additionally, the integration of the information in the knowledge graphs, so that, they can be used in machine learning techniques are also reviewed.  ...  Moreover, the review shows that the existing integration approaches limit the performance of the machine learning techniques, as the knowledge extracted from the knowledge graph is trimmed to match the  ...  Machine Learning for Knowledge Graphs Machine learning techniques have been widely employed for creating and improving the knowledge graphs, as these graphs cannot present a complete model of the real-life  ... 
doi:10.9790/0661-1904023438 fatcat:4shewzqwxrfh5jl2ievctbapju

Machine learning with biomedical ontologies [article]

Maxat Kulmanov, Fatima Zohra Smaili, Xin Gao, Robert Hoehndorf
2020 biorxiv/medrxiv   pre-print
method for utilizing ontologies in machine learning.  ...  The methods and experiments we describe are available as a set of executable notebooks, and we also provide a set of slides and additional resources at https://github.com/bio-ontology-research-group/machine-learning-with-ontologies.Key  ...  Translational embeddings methods are a family of representation learning methods on knowledge graphs which model relations in the knowledge graph as translation operations between graph node embeddings  ... 
doi:10.1101/2020.05.07.082164 fatcat:wpy4r3v7cjen7ehlqdrjssuj64

Drug Repurposing for Parkinson's Disease by Integrating Knowledge Graph Completion Model and Knowledge Fusion of Medical Literature

Xiaolin Zhang, Chao Che
2021 Future Internet  
Finally, we employ classic machine learning methods to repurpose the drug for Parkinson's disease and compare the results with the method only using the literature-based knowledge graph in order to confirm  ...  DRKF first extracts the relations that are related to Parkinson's disease from medical literature and builds a medical literature knowledge graph.  ...  Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable.  ... 
doi:10.3390/fi13010014 fatcat:ap4zz7skwracrhkc6m3mtjl4ia

Fair Graph Mining

Jian Kang, Hanghang Tong
2021 Proceedings of the 30th ACM International Conference on Information & Knowledge Management  
fair machine learning, and (2) algorithmic challenge on the dilemma of balancing model accuracy and fairness.  ...  This tutorial aims to (1) present a comprehensive review of state-of-the-art techniques in fairness on graph mining and ( 2 ) identify the open challenges and future trends.  ...  on graphs -Connections between group fairness and individual fairness on graphs The related tutorial reviews intrinsic limitations of existing fairness notions in machine learning and sheds light on  ... 
doi:10.1145/3459637.3482030 fatcat:fzg6nb56cjcird7vfkcviqxtni

Knowledge Graph Semantic Enhancement of Input Data for Improving AI

Shreyansh Bhatt, Amit Sheth, Valerie Shalin, Jinjin Zhao, Amit Sheth
2020 IEEE Internet Computing  
The term Knowledge Graph (KG) is in vogue as for many practical applications, it is convenient and useful to organize this background knowledge in the form of a graph.  ...  Recent academic research and implemented industrial intelligent systems have shown promising performance for machine learning algorithms that combine training data with a knowledge graph.  ...  We review these approaches as Iterative optimization for enhanced machine learning using a KG.  ... 
doi:10.1109/mic.2020.2979620 fatcat:q4xrsmddnfbvzjhev4xqknfo64

Multi-modal Network Representation Learning

Chuxu Zhang, Meng Jiang, Xiangliang Zhang, Yanfang Ye, Nitesh V. Chawla
2020 Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining  
In this tutorial, we systematically review the area of multi-modal network representation learning, including a series of recent methods and applications.  ...  Therefore, automating the feature discovery through representation learning in multi-modal networks has become essential for many applications.  ...  Aligned with machine learning strategies, representation learning for multi-modal networks can be categorized as supervised, semi-supervised, and unsupervised learning methods, according to the usage of  ... 
doi:10.1145/3394486.3406475 fatcat:vbnikhs53ndczblj2nepa5nq2y

Word Sense Disambiguation: Hybrid Approach with Annotation Up To Certain Level – A Review
English

Roshan R . Karwa, M.B Chandak
2014 International Journal of Engineering Trends and Technoloy  
This paper presents a review on methods for WSD.  ...  Disambiguation of a word is required in Machine Translation for lexical choice for words that have different translations for different senses and that are potentially ambiguous within a given domain,  ...  Supervised Learning Methods Supervised WSD uses machine-learning techniques rely on corpus evidence for inducing a classifier from manually sense-annotated corpus, includes training and testing module  ... 
doi:10.14445/22315381/ijett-v18p267 fatcat:ts2eguqyi5c3hec2nkmey54itm

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.  ...  However, as the literature on graph learning booms with a vast number of emerging methods and techniques, it becomes increasingly difficult to manually design the optimal machine learning algorithm for  ...  of China No.62050110.  ... 
arXiv:2103.00742v3 fatcat:d7h4vjeksvh5lbg5qns7lvh2su

Predictive article recommendation using natural language processing and machine learning to support evidence updates in domain-specific knowledge graphs

Bhuvan Sharma, Van C Willis, Claudia S Huettner, Kirk Beaty, Jane L Snowdon, Shang Xue, Brett R South, Gretchen P Jackson, Dilhan Weeraratne, Vanessa Michelini
2020 JAMIA Open  
Objectives Describe an augmented intelligence approach to facilitate the update of evidence for associations in knowledge graphs.  ...  Conclusion Integrated search and recommendation exploiting current evidence in a knowledge graph is useful for reducing human cognition load.  ...  to search for and recommend related literature for SME review.  ... 
doi:10.1093/jamiaopen/ooaa028 fatcat:l4fh7rdvtfapxgstppyuwupzty

Informed Machine Learning - A Taxonomy and Survey of Integrating Prior Knowledge into Learning Systems

Laura Vonrueden, Sebastian Mayer, Katharina Beckh, Bogdan Georgiev, Sven Giesselbach, Raoul Heese, Birgit Kirsch, Michal Walczak, Julius Pfrommer, Annika Pick, Rajkumar Ramamurthy, Jochen Garcke (+2 others)
2021 IEEE Transactions on Knowledge and Data Engineering  
We introduce a taxonomy that serves as a classification framework for informed machine learning approaches.  ...  A potential solution is the additional integration of prior knowledge into the training process which leads to the notion of informed machine learning.  ...  ACKNOWLEDGMENTS This work is a joint effort of the Fraunhofer Research Center for Machine Learning (RCML) within the Fraunhofer Cluster of Excellence Cognitive Internet Technologies (CCIT) and the Competence  ... 
doi:10.1109/tkde.2021.3079836 fatcat:jbuzbl6vlzagxcnr52vkuqsj5a

A Review of Some Techniques for Inclusion of Domain-Knowledge into Deep Neural Networks [article]

Tirtharaj Dash, Sharad Chitlangia, Aditya Ahuja, Ashwin Srinivasan
2021 arXiv   pre-print
The categorisation is for ease of exposition: in practice we expect a combination of such changes will be employed.  ...  In many such instances, machine-based model construction may benefit significantly from being provided with human-knowledge of the domain encoded in a sufficiently precise form.  ...  machine learning”, reviewed in [12].  ... 
arXiv:2107.10295v4 fatcat:ifkgq3cptbapfamr3dld57uruu

Semantic similarity and machine learning with ontologies

Maxat Kulmanov, Fatima Zohra Smaili, Xin Gao, Robert Hoehndorf
2020 Briefings in Bioinformatics  
The methods and experiments we describe are available as a set of executable notebooks, and we also provide a set of slides and additional resources at https://github.com/bio-ontology-research-group/machine-learning-with-ontologies  ...  Recently, ontologies are increasingly being used to provide background knowledge in similarity-based analysis and machine learning models.  ...  Conf licts of Interest statement The authors declare they have no conf licts of interest.  ... 
doi:10.1093/bib/bbaa199 pmid:33049044 pmcid:PMC8293838 fatcat:3mqrjqnggrhdrkvsl6w4odazeu

Analysing the Requirements for an Open Research Knowledge Graph: Use Cases, Quality Requirements and Construction Strategies [article]

Arthur Brack and Anett Hoppe and Markus Stocker and Sören Auer and Ralph Ewerth
2021 arXiv   pre-print
In this paper, we aim to transcend this limited perspective and present a comprehensive analysis of requirements for an Open Research Knowledge Graph (ORKG) by (a) collecting and reviewing daily core tasks  ...  Recently, several initiatives have proposed knowledge graphs (KG) for organising scientific information as a solution to many of the current issues.  ...  is updated and extended with the new sections Quality of knowledge graphs and Systematic literature reviews.  ... 
arXiv:2102.06021v1 fatcat:fpev37m5ivfy3ghuegwg7u3wq4

Machine Learning with World Knowledge: The Position and Survey [article]

Yangqiu Song, Dan Roth
2017 arXiv   pre-print
Two essential problems of machine learning are how to generate features and how to acquire labels for machines to learn.  ...  Machine learning has become pervasive in multiple domains, impacting a wide variety of applications, such as knowledge discovery and data mining, natural language processing, information retrieval, computer  ...  ACKNOWLEDGMENTS The authors wish to thank the anonymous reviewers of previous papers.  ... 
arXiv:1705.02908v1 fatcat:t4fypa6h3vampcp64eosvppsfe
« Previous Showing results 1 — 15 out of 110,426 results