scholarly journals Knowledge graph embedding for data mining vs. knowledge graph embedding for link prediction – two sides of the same coin?

Semantic Web ◽  
2022 ◽  
pp. 1-24
Author(s):  
Jan Portisch ◽  
Nicolas Heist ◽  
Heiko Paulheim
Keyword(s):  
Data Mining ◽  
Link Prediction ◽  
Graph Embedding ◽  
Knowledge Graph ◽  
Graph Embeddings ◽  
Similarity Functions ◽  
Evaluation Methodologies ◽  
Series Of Experiments ◽  
Two Sides ◽  
Lower Dimensional

Knowledge Graph Embeddings, i.e., projections of entities and relations to lower dimensional spaces, have been proposed for two purposes: (1) providing an encoding for data mining tasks, and (2) predicting links in a knowledge graph. Both lines of research have been pursued rather in isolation from each other so far, each with their own benchmarks and evaluation methodologies. In this paper, we argue that both tasks are actually related, and we show that the first family of approaches can also be used for the second task and vice versa. In two series of experiments, we provide a comparison of both families of approaches on both tasks, which, to the best of our knowledge, has not been done so far. Furthermore, we discuss the differences in the similarity functions evoked by the different embedding approaches.

scholarly journals TransET: Knowledge Graph Embedding with Entity Types

Electronics ◽  
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.

Hierarchical-Aware Relation Rotational Knowledge Graph Embedding For Link Prediction

2021 ◽  
Author(s):  
Shensi Wang ◽  
Kun Fu ◽  
Xian Sun ◽  
Zequn Zhang ◽  
Shuchao Li ◽  
...  
Keyword(s):  
Link Prediction ◽  
Graph Embedding ◽  
Knowledge Graph

scholarly journals Candidate gene prioritization using graph embedding

2020 ◽  
Author(s):  
Quan Do ◽  
Pierre Larmande
Keyword(s):  
Link Prediction ◽  
Graph Embedding ◽  
Prediction Performance ◽  
Knowledge Graph ◽  
Learning Techniques ◽  
Number Of Genes ◽  
Important Amount ◽  
Candidate Gene Prioritization ◽  
Gene Information ◽  
Embedding Methods

AbstractCandidate genes prioritization allows to rank among a large number of genes, those that are strongly associated with a phenotype or a disease. Due to the important amount of data that needs to be integrate and analyse, gene-to-phenotype association is still a challenging task. In this paper, we evaluated a knowledge graph approach combined with embedding methods to overcome these challenges. We first introduced a dataset of rice genes created from several open-access databases. Then, we used the Translating Embedding model and Convolution Knowledge Base model, to vectorize gene information. Finally, we evaluated the results using link prediction performance and vectors representation using some unsupervised learning techniques.

scholarly journals Unsupervised Embedding Enhancements of Knowledge Graphs using Textual Associations

2019 ◽  
Author(s):  
Neil Veira ◽  
Brian Keng ◽  
Kanchana Padmanabhan ◽  
Andreas Veneris
Keyword(s):  
Link Prediction ◽  
Graph Embedding ◽  
Structured Data ◽  
Knowledge Graph ◽  
Sources Of Information ◽  
Textual Information ◽  
Text Document ◽  
First Case ◽  
Textual Data ◽  
Unsupervised Approach

Knowledge graph embeddings are instrumental for representing and learning from multi-relational data, with recent embedding models showing high effectiveness for inferring new facts from existing databases. However, such precisely structured data is usually limited in quantity and in scope. Therefore, to fully optimize the embeddings it is important to also consider more widely available sources of information such as text. This paper describes an unsupervised approach to incorporate textual information by augmenting entity embeddings with embeddings of associated words. The approach does not modify the optimization objective for the knowledge graph embedding, which allows it to be integrated with existing embedding models. Two distinct forms of textual data are considered, with different embedding enhancements proposed for each case. In the first case, each entity has an associated text document that describes it. In the second case, a text document is not available, and instead entities occur as words or phrases in an unstructured corpus of text fragments. Experiments show that both methods can offer improvement on the link prediction task when applied to many different knowledge graph embedding models.

scholarly journals Knowledge Graph Embedding-Based Domain Adaptation for Musical Instrument Recognition

Computers ◽  
2021 ◽  
Vol 10 (8) ◽  
pp. 94
Author(s):  
Victoria Eyharabide ◽  
Imad Eddine Ibrahim Bekkouch ◽  
Nicolae Dragoș Constantin
Keyword(s):  
Cultural Heritage ◽  
Domain Adaptation ◽  
Graph Embedding ◽  
Musical Instrument ◽  
Vector Spaces ◽  
Knowledge Graph ◽  
Graph Embeddings ◽  
Linear Discriminant ◽  
Historical Collections ◽  
Instrument Recognition

Convolutional neural networks raised the bar for machine learning and artificial intelligence applications, mainly due to the abundance of data and computations. However, there is not always enough data for training, especially when it comes to historical collections of cultural heritage where the original artworks have been destroyed or damaged over time. Transfer Learning and domain adaptation techniques are possible solutions to tackle the issue of data scarcity. This article presents a new method for domain adaptation based on Knowledge graph embeddings. Knowledge Graph embedding forms a projection of a knowledge graph into a lower-dimensional where entities and relations are represented into continuous vector spaces. Our method incorporates these semantic vector spaces as a key ingredient to guide the domain adaptation process. We combined knowledge graph embeddings with visual embeddings from the images and trained a neural network with the combined embeddings as anchors using an extension of Fisher’s linear discriminant. We evaluated our approach on two cultural heritage datasets of images containing medieval and renaissance musical instruments. The experimental results showed a significant increase in the baselines and state-of-the-art performance compared with other domain adaptation methods.

scholarly journals Learning Translation-Based Knowledge Graph Embeddings by N-Pair Translation Loss

2020 ◽  
Vol 10 (11) ◽  
pp. 3964
Author(s):  
Hyun-Je Song ◽  
A-Yeong Kim ◽  
Seong-Bae Park
Keyword(s):  
Link Prediction ◽  
Early Stage ◽  
Score Function ◽  
Knowledge Graph ◽  
Graph Embeddings ◽  
Local Optima ◽  
Slow Convergence ◽  
Translation Operators ◽  
Vector Representations ◽  
Ranking Loss

Translation-based knowledge graph embeddings learn vector representations of entities and relations by treating relations as translation operators over the entities in an embedding space. Since the translation is represented through a score function, translation-based embeddings are trained in general by minimizing a margin-based ranking loss, which assigns a low score to positive triples and a high score to negative triples. However, this type of embedding suffers from slow convergence and poor local optima because the loss adopts only one pair of a positive and a negative triple at a single update of learning parameters. Therefore, this paper proposes the N-pair translation loss that considers multiple negative triples at one update. The N-pair translation loss employs multiple negative triples as well as one positive triple and allows the positive triple to be compared against the multiple negative triples at each parameter update. As a result, it becomes possible to obtain better vector representations rapidly. The experimental results on link prediction prove that the proposed loss helps to quickly converge toward good optima at the early stage of training.

scholarly journals Learning Hierarchy-Aware Knowledge Graph Embeddings for Link Prediction

2020 ◽  
Vol 34 (03) ◽  
pp. 3065-3072 ◽  
Author(s):  
Zhanqiu Zhang ◽  
Jianyu Cai ◽  
Yongdong Zhang ◽  
Jie Wang
Keyword(s):  
Coordinate System ◽  
Link Prediction ◽  
Graph Embedding ◽  
Knowledge Graph ◽  
Polar Coordinate System ◽  
Radial Coordinate ◽  
Polar Coordinate ◽  
Benchmark Datasets ◽  
Knowledge Graphs ◽  
Semantic Hierarchies

Knowledge graph embedding, which aims to represent entities and relations as low dimensional vectors (or matrices, tensors, etc.), has been shown to be a powerful technique for predicting missing links in knowledge graphs. Existing knowledge graph embedding models mainly focus on modeling relation patterns such as symmetry/antisymmetry, inversion, and composition. However, many existing approaches fail to model semantic hierarchies, which are common in real-world applications. To address this challenge, we propose a novel knowledge graph embedding model—namely, Hierarchy-Aware Knowledge Graph Embedding (HAKE)—which maps entities into the polar coordinate system. HAKE is inspired by the fact that concentric circles in the polar coordinate system can naturally reflect the hierarchy. Specifically, the radial coordinate aims to model entities at different levels of the hierarchy, and entities with smaller radii are expected to be at higher levels; the angular coordinate aims to distinguish entities at the same level of the hierarchy, and these entities are expected to have roughly the same radii but different angles. Experiments demonstrate that HAKE can effectively model the semantic hierarchies in knowledge graphs, and significantly outperforms existing state-of-the-art methods on benchmark datasets for the link prediction task.

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