A Review of Relational Machine Learning for Knowledge Graphs
Maximilian Nickel,Kevin Murphy,Volker Tresp,Evgeniy Gabrilovich +3 more
- 01 Jan 2016
- Vol. 104, Iss: 1, pp 11-33
TL;DR: This paper provides a review of how 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 in the graph) and how such statistical models of graphs can be combined with text-based information extraction methods for automatically constructing knowledge graphs from the Web.
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Abstract: 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 in the graph). In particular, we discuss two fundamentally different kinds of statistical relational models, both of which can scale to massive data sets. The first is based on latent feature models such as tensor factorization and multiway neural networks. The second is based on mining observable patterns in the graph. We also show how to combine these latent and observable models to get improved modeling power at decreased computational cost. Finally, we discuss how such statistical models of graphs can be combined with text-based information extraction methods for automatically constructing knowledge graphs from the Web. To this end, we also discuss Google's knowledge vault project as an example of such combination.
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Citations
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•Posted Content
Representation Learning for Dynamic Graphs: A Survey
Seyed Mehran Kazemi,Rishab Goel,Kshitij Jain,Ivan Kobyzev,Akshay Sethi,Peter Forsyth,Pascal Poupart +6 more
TL;DR: A survey of representation learning for dynamic knowledge graphs can be found in this article, where the authors describe existing models from an encoder-decoder perspective, categorize these encoders and decoders based on the techniques they employ, and analyze the approaches in each category.
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Context-aware Path Ranking for Knowledge Base Completion
Sahisnu Mazumder,Bing Liu +1 more
TL;DR: Zhang et al. as mentioned in this paper proposed a Context-Aware Path Ranking (C-PR) algorithm, which learns global semantics of entities in the knowledge base using word embedding and leverages the knowledge of entity semantics to enumerate contextually relevant paths using bidirectional random walk.
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Mehdi Ali,Charles Tapley Hoyt,Charles Tapley Hoyt,Daniel Domingo-Fernández,Daniel Domingo-Fernández,Jens Lehmann,Jens Lehmann,Hajira Jabeen +7 more
TL;DR: This work developed BioKEEN and PyKEEN to facilitate their easy use through an interactive command line interface and presents a case study in which a novel biological pathway mapping resource is used to predict links that represent pathway crosstalks and hierarchies.
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