Open AccessPosted Content
Logistic Tensor Factorization for Multi-Relational Data.
Maximilian Nickel,Volker Tresp +1 more
TLDR
This work extends the RESCAL tensor factorization, which has shown state-of-the-art results for multi-relational learning, to account for the binary nature of adjacency tensors and shows that the logistic extension can improve the prediction results significantly.Abstract:
Tensor factorizations have become increasingly popular approaches for various learning tasks on structured data. In this work, we extend the RESCAL tensor factorization, which has shown state-of-the-art results for multi-relational learning, to account for the binary nature of adjacency tensors. We study the improvements that can be gained via this approach on various benchmark datasets and show that the logistic extension can improve the prediction results significantly.read more
Citations
More filters
Journal ArticleDOI
Knowledge Graph Embedding: A Survey of Approaches and Applications
TL;DR: This article provides a systematic review of existing techniques of Knowledge graph embedding, including not only the state-of-the-arts but also those with latest trends, based on the type of information used in the embedding task.
Journal ArticleDOI
A Review of Relational Machine Learning for Knowledge Graphs
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.
Journal ArticleDOI
A review: Knowledge reasoning over knowledge graph
TL;DR: The basic concept and definitions of knowledge reasoning and the methods for reasoning over knowledge graphs are reviewed, and the reasoning methods are dissected into three categories: rule- based reasoning, distributed representation-based reasoning and neural network-based Reasoning.
Proceedings Article
Scalable Bayesian Low-Rank Decomposition of Incomplete Multiway Tensors
TL;DR: A scalable Bayesian framework for low-rank decomposition of multiway tensor data with missing observations, which outperforms several state-of-the-art tensor decomposition methods on various synthetic and benchmark real-world datasets.
Posted Content
A Review of Relational Machine Learning for Knowledge Graphs From Multi-Relational Link Prediction to Automated Knowledge Graph Construction
TL;DR: In this article, the authors 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).
References
More filters
Proceedings Article
A Three-Way Model for Collective Learning on Multi-Relational Data
TL;DR: This work presents a novel approach to relational learning based on the factorization of a three-way tensor that is able to perform collective learning via the latent components of the model and provide an efficient algorithm to compute the factorizations.
Proceedings ArticleDOI
Factorizing personalized Markov chains for next-basket recommendation
TL;DR: This paper introduces an adaption of the Bayesian Personalized Ranking (BPR) framework for sequential basket data and shows that the FPMC model outperforms both the common matrix factorization and the unpersonalized MC model both learned with and without factorization.
Proceedings Article
Learning structured embeddings of knowledge bases
TL;DR: A learning process based on an innovative neural network architecture designed to embed any of these symbolic representations into a more flexible continuous vector space in which the original knowledge is kept and enhanced would allow data from any KB to be easily used in recent machine learning methods for prediction and information retrieval.
Proceedings ArticleDOI
Factorizing YAGO: scalable machine learning for linked data
TL;DR: This work presents an efficient approach to relational learning on LOD data, based on the factorization of a sparse tensor that scales to data consisting of millions of entities, hundreds of relations and billions of known facts, and shows how ontological knowledge can be incorporated in the factorizations to improve learning results and how computation can be distributed across multiple nodes.
Proceedings ArticleDOI
Temporal Analysis of Semantic Graphs Using ASALSAN
TL;DR: The mixture of roles assigned to individuals by ASALSAN showed strong correspondence with known job classifications and revealed the patterns of communication between these roles, e.g., between top executives and the legal department, were also apparent in the solutions.