Graph Embedding Using Quantum Commute Times

A Transition-Based Knowledge Graph Embedding with Adapting New Entities

Contemporary Research Trend of IT Convergence Technology ◽

10.21742/asehl.2016.4.03 ◽

2016 ◽

Author(s):

A-Yeong Kim ◽

◽

Hee-Guen Yoon ◽

Seong-Bae Park ◽

Se-Young Park ◽

...

Keyword(s):

Graph Embedding ◽

Knowledge Graph

Download Full-text

LineaRE: Simple but Powerful Knowledge Graph Embedding for Link Prediction

2020 IEEE International Conference on Data Mining (ICDM) ◽

10.1109/icdm50108.2020.00051 ◽

2020 ◽

Author(s):

Yanhui Peng ◽

Jing Zhang

Keyword(s):

Link Prediction ◽

Graph Embedding ◽

Knowledge Graph ◽

Powerful Knowledge

Download Full-text

Graph Learning for Combinatorial Optimization: A Survey of State-of-the-Art

Data Science and Engineering ◽

10.1007/s41019-021-00155-3 ◽

2021 ◽

Author(s):

Yun Peng ◽

Byron Choi ◽

Jianliang Xu

Keyword(s):

Machine Learning ◽

Combinatorial Optimization ◽

Graph Embedding ◽

Partial Solution ◽

Complex Data ◽

Learning Methods ◽

Graph Learning ◽

Second Stage ◽

End To End ◽

Embedding Methods

AbstractGraphs have been widely used to represent complex data in many applications, such as e-commerce, social networks, and bioinformatics. Efficient and effective analysis of graph data is important for graph-based applications. However, most graph analysis tasks are combinatorial optimization (CO) problems, which are NP-hard. Recent studies have focused a lot on the potential of using machine learning (ML) to solve graph-based CO problems. Most recent methods follow the two-stage framework. The first stage is graph representation learning, which embeds the graphs into low-dimension vectors. The second stage uses machine learning to solve the CO problems using the embeddings of the graphs learned in the first stage. The works for the first stage can be classified into two categories, graph embedding methods and end-to-end learning methods. For graph embedding methods, the learning of the the embeddings of the graphs has its own objective, which may not rely on the CO problems to be solved. The CO problems are solved by independent downstream tasks. For end-to-end learning methods, the learning of the embeddings of the graphs does not have its own objective and is an intermediate step of the learning procedure of solving the CO problems. The works for the second stage can also be classified into two categories, non-autoregressive methods and autoregressive methods. Non-autoregressive methods predict a solution for a CO problem in one shot. A non-autoregressive method predicts a matrix that denotes the probability of each node/edge being a part of a solution of the CO problem. The solution can be computed from the matrix using search heuristics such as beam search. Autoregressive methods iteratively extend a partial solution step by step. At each step, an autoregressive method predicts a node/edge conditioned to current partial solution, which is used to its extension. In this survey, we provide a thorough overview of recent studies of the graph learning-based CO methods. The survey ends with several remarks on future research directions.

Download Full-text

Making Sense of Search: Using Graph Embedding and Visualization to Transform Query Understanding

Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems ◽

10.1145/3334480.3375233 ◽

2020 ◽

Author(s):

Jonathan Larson ◽

Darren Edge ◽

Nathan Evans ◽

Christopher White

Keyword(s):

Graph Embedding ◽

Making Sense ◽

Query Understanding

Download Full-text

TransET: Knowledge Graph Embedding with Entity Types

Electronics ◽

10.3390/electronics10121407 ◽

2021 ◽

Vol 10 (12) ◽

pp. 1407

Author(s):

Peng Wang ◽

Jing Zhou ◽

Yuzhang Liu ◽

Xingchen Zhou

Keyword(s):

Link Prediction ◽

State Of The Art ◽

Score Function ◽

Graph Embedding ◽

Vector Spaces ◽

Knowledge Graph ◽

Semantic Features ◽

Knowledge Graphs ◽

Real World Datasets ◽

Low Dimensional

Knowledge graph embedding aims to embed entities and relations into low-dimensional vector spaces. Most existing methods only focus on triple facts in knowledge graphs. In addition, models based on translation or distance measurement cannot fully represent complex relations. As well-constructed prior knowledge, entity types can be employed to learn the representations of entities and relations. In this paper, we propose a novel knowledge graph embedding model named TransET, which takes advantage of entity types to learn more semantic features. More specifically, circle convolution based on the embeddings of entity and entity types is utilized to map head entity and tail entity to type-specific representations, then translation-based score function is used to learn the presentation triples. We evaluated our model on real-world datasets with two benchmark tasks of link prediction and triple classification. Experimental results demonstrate that it outperforms state-of-the-art models in most cases.

Download Full-text