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.

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.

scholarly journals A Survey on Knowledge Graph Embeddings for Link Prediction

Symmetry ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 485
Author(s):  
Meihong Wang ◽  
Linling Qiu ◽  
Xiaoli Wang
Keyword(s):  
Language Processing ◽  
Link Prediction ◽  
Knowledge Graph ◽  
Practical Utilization ◽  
Complete Knowledge ◽  
Benchmark Datasets ◽  
Comprehensive Survey ◽  
Knowledge Graphs ◽  
Representation Techniques ◽  
Open Nature

Knowledge graphs (KGs) have been widely used in the field of artificial intelligence, such as in information retrieval, natural language processing, recommendation systems, etc. However, the open nature of KGs often implies that they are incomplete, having self-defects. This creates the need to build a more complete knowledge graph for enhancing the practical utilization of KGs. Link prediction is a fundamental task in knowledge graph completion that utilizes existing relations to infer new relations so as to build a more complete knowledge graph. Numerous methods have been proposed to perform the link-prediction task based on various representation techniques. Among them, KG-embedding models have significantly advanced the state of the art in the past few years. In this paper, we provide a comprehensive survey on KG-embedding models for link prediction in knowledge graphs. We first provide a theoretical analysis and comparison of existing methods proposed to date for generating KG embedding. Then, we investigate several representative models that are classified into five categories. Finally, we conducted experiments on two benchmark datasets to report comprehensive findings and provide some new insights into the strengths and weaknesses of existing models.

A quaternion-group knowledge graph embedding model

2021 ◽  
pp. 1-10
Author(s):  
Heng Chen ◽  
Guanyu Li ◽  
Yunhao Sun ◽  
Wei Jiang
Keyword(s):  
Link Prediction ◽  
State Of The Art ◽  
Graph Embedding ◽  
Knowledge Graph ◽  
Complex Vector ◽  
Quaternion Group ◽  
Rotation Matrices ◽  
Group Knowledge ◽  
Benchmark Datasets ◽  
Complete Relation

Capturing the composite embedding representation of a multi-hop relation path is an extremely vital task in knowledge graph completion. Recently, rotation-based relation embedding models have been widely studied to embed composite relations into complex vector space. However, these models make some over-simplified assumptions on the composite relations, resulting the relations to be commutative. To tackle this problem, this paper proposes a novel knowledge graph embedding model, named QuatGE, which can provide sufficient modeling capabilities for complex composite relations. In particular, our method models each relation as a rotation operator in quaternion group-based space. The advantages of our model are twofold: (1) Since the quaternion group is a non-commutative group (i.e., non-Abelian group), the corresponding rotation matrices of composite relations can be non-commutative; (2) The model has a more expressive setting with stronger modeling capabilities, which is flexible to model and infer the complete relation patterns, including: symmetry/anti-symmetry, inversion and commutative/non-commutative composition. Experimental results on four benchmark datasets show that the proposed method outperforms the existing state-of-the-art models for link prediction, especially on composite relations.

scholarly journals Link Prediction between Group Entities in Knowledge Graphs (Student Abstract)

2020 ◽  
Vol 34 (10) ◽  
pp. 13925-13926
Author(s):  
Jialin Su ◽  
Yuanzhuo Wang ◽  
Xiaolong Jin ◽  
Yantao Jia ◽  
Xueqi Cheng
Keyword(s):  
Real World ◽  
Heterogeneous Network ◽  
Link Prediction ◽  
Graph Embedding ◽  
Knowledge Graph ◽  
Low Dimension ◽  
Individual Entity ◽  
Knowledge Graphs ◽  
Dimension Space

Link prediction in knowledge graphs (KGs) aims at predicting potential links between entities in KGs. Existing knowledge graph embedding (KGE) based methods represent individual entities and links in KGs as vectors in low-dimension space. However, these methods focus mainly on the link prediction of individual entities, yet neglect that between group entities, which exist widely in real-world KGs. In this paper, we propose a KGE based method, called GTransA, for link prediction between group entities in a heterogeneous network by integrating individual entity links into group entity links during prediction. Experiments show that GTransA decreases mean rank by 5.4%, compared to TransA.

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

Natural Frequencies and Mode Shapes of Elliptic Plates With Boundary Characteristic Orthogonal Polynomials As Assumed Shape Functions

1991 ◽  
Author(s):  
C. Rajalingham ◽  
R. B. Bhat ◽  
G. D. Xistris
Keyword(s):  
Coordinate System ◽  
Orthogonal Polynomials ◽  
Natural Frequencies ◽  
Ritz Method ◽  
Mode Shapes ◽  
Polar Coordinate System ◽  
Free Boundaries ◽  
Natural Modes ◽  
Polar Coordinate ◽  
Boundary Characteristic

Abstract The natural frequencies and natural modes of vibration of uniform elliptic plates with clamped, simply supported and free boundaries are investigated using Rayleigh-Ritz method. A modified polar coordinate system is used to investigate the problem. Energy expressions in Cartesian coordinate system are transformed into the modified polar coordinate system. Boundary characteristic orthogonal polynomials in the radial direction, and trigonometric functions in the angular direction are used to express the deflection of the plate. These deflection shapes are classified into four basic categories, depending on its symmetrical or antisymmetrical property about the major and minor axes of the ellipse. The first six natural modes in each of the above categories are presented in the form of contour plots.

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