On Training Knowledge Graph Embedding Models

Knowledge graph embedding (KGE) models have become popular means for making discoveries in knowledge graphs (e.g., RDF graphs) in an efficient and scalable manner. The key to success of these models is their ability to learn low-rank vector representations for knowledge graph entities and relations. Despite the rapid development of KGE models, state-of-the-art approaches have mostly focused on new ways to represent embeddings interaction functions (i.e., scoring functions). In this paper, we argue that the choice of other training components such as the loss function, hyperparameters and negative sampling strategies can also have substantial impact on the model efficiency. This area has been rather neglected by previous works so far and our contribution is towards closing this gap by a thorough analysis of possible choices of training loss functions, hyperparameters and negative sampling techniques. We finally investigate the effects of specific choices on the scalability and accuracy of knowledge graph embedding models.

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Biological applications of knowledge graph embedding models

Briefings in Bioinformatics ◽

10.1093/bib/bbaa012 ◽

2020 ◽

Cited By ~ 3

Author(s):

Sameh K Mohamed ◽

Aayah Nounu ◽

Vít Nováček

Keyword(s):

Predictive Accuracy ◽

Biological Systems ◽

Graph Embedding ◽

Low Rank ◽

Superior Performance ◽

Knowledge Graph ◽

Biological Knowledge ◽

Knowledge Graphs ◽

Vector Representations ◽

Biological Entities

Abstract Complex biological systems are traditionally modelled as graphs of interconnected biological entities. These graphs, i.e. biological knowledge graphs, are then processed using graph exploratory approaches to perform different types of analytical and predictive tasks. Despite the high predictive accuracy of these approaches, they have limited scalability due to their dependency on time-consuming path exploratory procedures. In recent years, owing to the rapid advances of computational technologies, new approaches for modelling graphs and mining them with high accuracy and scalability have emerged. These approaches, i.e. knowledge graph embedding (KGE) models, operate by learning low-rank vector representations of graph nodes and edges that preserve the graph’s inherent structure. These approaches were used to analyse knowledge graphs from different domains where they showed superior performance and accuracy compared to previous graph exploratory approaches. In this work, we study this class of models in the context of biological knowledge graphs and their different applications. We then show how KGE models can be a natural fit for representing complex biological knowledge modelled as graphs. We also discuss their predictive and analytical capabilities in different biology applications. In this regard, we present two example case studies that demonstrate the capabilities of KGE models: prediction of drug–target interactions and polypharmacy side effects. Finally, we analyse different practical considerations for KGEs, and we discuss possible opportunities and challenges related to adopting them for modelling biological systems.

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AutoSF: Searching Scoring Functions for Knowledge Graph Embedding

2020 IEEE 36th International Conference on Data Engineering (ICDE) ◽

10.1109/icde48307.2020.00044 ◽

2020 ◽

Cited By ~ 4

Author(s):

Yongqi Zhang ◽

Quanming Yao ◽

Wenyuan Dai ◽

Lei Chen

Keyword(s):

Graph Embedding ◽

Knowledge Graph ◽

Scoring Functions

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GREG: A Global Level Relation Extraction with Knowledge Graph Embedding

Applied Sciences ◽

10.3390/app10031181 ◽

2020 ◽

Vol 10 (3) ◽

pp. 1181 ◽

Cited By ~ 1

Author(s):

Kuekyeng Kim ◽

Yuna Hur ◽

Gyeongmin Kim ◽

Heuiseok Lim

Keyword(s):

Local Level ◽

Relation Extraction ◽

Graph Embedding ◽

Structured Data ◽

Unstructured Data ◽

Knowledge Graph ◽

Global Level ◽

Proposed Model ◽

Vector Representations ◽

Document Level

In an age overflowing with information, the task of converting unstructured data into structured data are a vital task of great need. Currently, most relation extraction modules are more focused on the extraction of local mention-level relations—usually from short volumes of text. However, in most cases, the most vital and important relations are those that are described in length and detail. In this research, we propose GREG: A Global level Relation Extractor model using knowledge graph embeddings for document-level inputs. The model uses vector representations of mention-level ‘local’ relation’s to construct knowledge graphs that can represent the input document. The knowledge graph is then used to predict global level relations from documents or large bodies of text. The proposed model is largely divided into two modules which are synchronized during their training. Thus, each of the model’s modules is designed to deal with local relations and global relations separately. This allows the model to avoid the problem of struggling against loss of information due to too much information crunched into smaller sized representations when attempting global level relation extraction. Through evaluation, we have shown that the proposed model yields high performances in both predicting global level relations and local level relations consistently.

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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

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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

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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.

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