Complex-InversE: Improving bilinear knowledge graph embeddings by mapping into complex space

2021 ◽  
pp. 1-11
Author(s):  
Yukun Cao ◽  
Zeyu Miao
Keyword(s):  
Link Prediction ◽  
Complex Space ◽  
Prediction Method ◽  
Knowledge Graph ◽  
The Public ◽  
Low Dimensional ◽  
Link Information ◽  
Prediction Problems ◽  
Benchmark Evaluation ◽  
Graph Link

Knowledge graph link prediction uses known fact links to infer the missing link information in the knowledge graph, which is of great significance to the completion of the knowledge graph. Generating low-dimensional embeddings of entities and relations which are used to make inferences is a popular way for such link prediction problems. This paper proposes a knowledge graph link prediction method called Complex-InversE in the complex space, which maps entities and relations into the complex space. The composition of complex embeddings can handle a large variety of binary relations, among them symmetric and antisymmetric relations. The Complex-InversE effectively captures the antisymmetric relations and introduces Dropout and Early-Stopping technologies into deal with the problem of small numbers of relationships and entities, thus effectively alleviates the model’s overfitting. The results of comparison experiment on the public knowledge graph datasets show that the Complex-InversE achieves good results on multiple benchmark evaluation indicators and outperforms previous methods. Complex-InversE’s code is available on GitHub at https://github.com/ZeyuMiao97/Complex-InversE.

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 Understanding Negative Sampling in Knowledge Graph Embedding

2021 ◽  
Vol 12 (1) ◽  
pp. 71-81
Author(s):  
Jing Qian ◽  
Gangmin Li ◽  
Katie Atkinson ◽  
Yong Yue
Keyword(s):  
Link Prediction ◽  
Graph Embedding ◽  
Knowledge Graph ◽  
Direct Impact ◽  
Dimensional Vector Space ◽  
Dynamic Distribution ◽  
Space Efficiency ◽  
Node Classification ◽  
Low Dimensional

Knowledge graph embedding (KGE) is to project entities and relations of a knowledge graph (KG) into a low-dimensional vector space, which has made steady progress in recent years. Conventional KGE methods, especially translational distance-based models, are trained through discriminating positive samples from negative ones. Most KGs store only positive samples for space efficiency. Negative sampling thus plays a crucial role in encoding triples of a KG. The quality of generated negative samples has a direct impact on the performance of learnt knowledge representation in a myriad of downstream tasks, such as recommendation, link prediction and node classification. We summarize current negative sampling approaches in KGE into three categories, static distribution-based, dynamic distribution-based and custom cluster-based respectively. Based on this categorization we discuss the most prevalent existing approaches and their characteristics. It is a hope that this review can provide some guidelines for new thoughts about negative sampling in KGE.

scholarly journals Neighborhood Intervention Consistency: Measuring Confidence for Knowledge Graph Link Prediction

2021 ◽  
Author(s):  
Kai Wang ◽  
Yu Liu ◽  
Quan Z. Sheng
Keyword(s):  
Link Prediction ◽  
State Of The Art ◽  
Confidence Score ◽  
Measurement Methods ◽  
Knowledge Graph ◽  
Critical Gap ◽  
Optimal Score ◽  
Causal Intervention ◽  
Graph Link ◽  
Confidence Measurement

Link prediction based on knowledge graph embeddings (KGE) has recently drawn a considerable momentum. However, existing KGE models suffer from insufficient accuracy and hardly evaluate the confidence probability of each predicted triple. To fill this critical gap, we propose a novel confidence measurement method based on causal intervention, called Neighborhood Intervention Consistency (NIC). Unlike previous confidence measurement methods that focus on the optimal score in a prediction, NIC actively intervenes in the input entity vector to measure the robustness of the prediction result. The experimental results on ten popular KGE models show that our NIC method can effectively estimate the confidence score of each predicted triple. The top 10% triples with high NIC confidence can achieve 30% higher accuracy in the state-of-the-art KGE models.

scholarly journals Contextual Parameter Generation for Knowledge Graph Link Prediction

2020 ◽  
Vol 34 (03) ◽  
pp. 3000-3008
Author(s):  
George Stoica ◽  
Otilia Stretcu ◽  
Emmanouil Antonios Platanios ◽  
Tom Mitchell ◽  
Barnabás Póczos
Keyword(s):  
Link Prediction ◽  
State Of The Art ◽  
Linear Functions ◽  
Convergence Time ◽  
Knowledge Graph ◽  
Complex Interactions ◽  
Significant Performance ◽  
Representational Power ◽  
Graph Link ◽  
Contextual Parameter

We consider the task of knowledge graph link prediction. Given a question consisting of a source entity and a relation (e.g., Shakespeare and BornIn), the objective is to predict the most likely answer entity (e.g., England). Recent approaches tackle this problem by learning entity and relation embeddings. However, they often constrain the relationship between these embeddings to be additive (i.e., the embeddings are concatenated and then processed by a sequence of linear functions and element-wise non-linearities). We show that this type of interaction significantly limits representational power. For example, such models cannot handle cases where a different projection of the source entity is used for each relation. We propose to use contextual parameter generation to address this limitation. More specifically, we treat relations as the context in which source entities are processed to produce predictions, by using relation embeddings to generate the parameters of a model operating over source entity embeddings. This allows models to represent more complex interactions between entities and relations. We apply our method on two existing link prediction methods, including the current state-of-the-art, resulting in significant performance gains and establishing a new state-of-the-art for this task. These gains are achieved while also reducing convergence time by up to 28 times.

The research of clinical temporal knowledge graph based on depth learning

2021 ◽  
pp. 1-10
Author(s):  
Lijuan Diao ◽  
Shoujun Song ◽  
Gaofang Cao ◽  
Yang Kong
Keyword(s):  
Deep Learning ◽  
Knowledge Base ◽  
Link Prediction ◽  
Prediction Models ◽  
Temporal Characteristic ◽  
Diabetic Complication ◽  
Transformation Model ◽  
Sequential Learning ◽  
Knowledge Graph ◽  
Graph Link

Temporal knowledge base exists on various fields. Take medical medicine field as example, diabetes is a typical chronic disease which evolves slowly. This paper starts from actual EMR data of hospitals by combination of experience and knowledge of clinical doctors. Link prediction on clinical knowledge base such as diabetic complication requires the analysis on temporal characteristic of temporal knowledge base, which is a great challenge for traditional link prediction models. This paper proposes temporal knowledge graph link prediction model based on deep learning. This model selects the TransR transformation model suitable for big data and makes entity projection in relation space containing different semantic meanings, so as to vector the entities and complex semantic relations in graph. Then it adopts LSTM recursive neural network and adds the top-bottom relational information of the graph for sequential learning. Finally it constantly carries out deep learning through incremental calculation and LSTM recursive network to improve the accuracy of prediction. The incremental LSTM model highlights the hidden semantic and clinical temporal information and effectively utilizes sequential learning to mining forward-backward dependent information. It compensates the deficiency of lower prediction accuracy on timely knowledge graph caused by the traditional link prediction models. Finally, it is proved that the new model has better performance over temporal knowledge graph link prediction.

scholarly journals A Novel Negative Sampling Based on Frequency of Relational Association Entities for Knowledge Graph Embedding

2021 ◽  
Author(s):  
Wanhua Cao ◽  
Yi Zhang ◽  
Juntao Liu ◽  
Ziyun Rao
Keyword(s):  
Link Prediction ◽  
State Of The Art ◽  
Evaluation Criteria ◽  
Relation Extraction ◽  
Graph Embedding ◽  
Semantic Space ◽  
Knowledge Graph ◽  
Knowledge Reasoning ◽  
Relation Prediction ◽  
Low Dimensional

Knowledge graph embedding improves the performance of relation extraction and knowledge reasoning by encoding entities and relationships in low-dimensional semantic space. During training, negative samples are usually constructed by replacing the head/tail entity. And the different replacing relationships lead to different accuracy of the prediction results. This paper develops a negative triplets construction framework according to the frequency of relational association entities. The proposed construction framework can fully consider the quantitative of relations and entities in the dataset to assign the proportion of relation and entity replacement and the frequency of the entities associated with each relationship to set reasonable proportions for different relations. To verify the validity of the proposed construction framework, it is integrated into the state-of-the-art knowledge graph embedding models, such as TransE, TransH, DistMult, ComplEx, and Analogy. And both the evaluation criteria of relation prediction and entity prediction are used to evaluate the performance of link prediction more comprehensively. The experimental results on two commonly used datasets, WN18 and FB15K, show that the proposed method improves entity link and triplet classification accuracy, especially the accuracy of relational link prediction.

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