Learning Entity and Relation Embeddings with Entity Description for Knowledge Graph Completion

Dai, Shaozhi; Liang, Yanchun; Liu, Shuyan; Wang, Ying; Shao, Wenle; Lin, Xi-Jie; Feng, Xiaoyue

doi:10.2991/icaita-18.2018.49

Cited by 20 publications

(5 citation statements)

References 11 publications

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“…The KG embedding model embeds all entities and relationships into low-dimensional vectors and captures their semantics [ 27 ]. For each e ∈ ℰ and l ∈ ℒ, KG embeddings models generate e e ∈ ℝ d e and e r ∈ ℝ d r , where e e and e r are d e and d r dimensional vectors, respectively.…”

Section: Methodsmentioning

confidence: 99%

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Decision-Making System for the Diagnosis of Syndrome Based on Traditional Chinese Medicine Knowledge Graph

Yang

Cheng

et al. 2022

Evidence-Based Complementary and Alternative Medicine

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The clinical informatization of traditional Chinese medicine (TCM) focuses on serving users and assisting in diagnosis. The rules of clinical knowledge play an important role in improving the TCM informatization service. However, many rules are difficult to find because of the complexity of the data in the current TCM syndrome prediction. Therefore, we proposed an end-to-end model, called Decision-making System for the Diagnosis of Syndrome (DSDS), which is based on the knowledge graph (KG) of TCM. This paper introduces the link prediction for the diagnosis of syndrome by dismantling medical records into multiple symptoms. In addition, based on the symptoms and predicted syndromes, the most relevant syndrome could be determined by the scoring and voting method in this paper. The results show that the accuracy of DSDS is 80.6%.

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Section: Methodsmentioning

confidence: 99%

“…The tail entities of the missing triples are complemented by the score function of the ComplEx model. There was a corresponding score for each completed triple [ 25 , 27 ].…”

Section: Methodsmentioning

confidence: 99%

Decision-Making System for the Diagnosis of Syndrome Based on Traditional Chinese Medicine Knowledge Graph

Yang

Cheng

et al. 2022

Evidence-Based Complementary and Alternative Medicine

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“…• CKE adopt a heterogeneous network embedding method [18], termed as TransR [19], to extract items' structural representations by considering the heterogeneity of both nodes and relationships. • CFKG propose a knowledge-base representation learning framework to embed heterogeneous entities for recommendation [20].…”

Section: Comparison Methodsmentioning

confidence: 99%

LighterKGCN: A Recommender System Model based on Bi-layer Graph Convolutional Networks

陳鵬¹,

Chen²,

Zhao³

2022

Journal of Internet Technology

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<p>Recommender systems have been extensively utilized to meet users’ personalized needs. Collaborative filtering is one of the most classic algorithms in the recommendation field. However, it has problems such as cold start and data sparsity. In that case, knowledge graphs and graph convolutional networks have been introduced by scholars into recommender systems to solve the above problems. However, the current graph convolutional networks fail to give full play to the advantages of graph convolution since they are employed either in the embedding representations of users and commodity entities, or in the embedding representations between entities of the knowledge graphs. Therefore, LighterKGCN, a recommender system model based on bi-layer graph convolutional networks was proposed in accordance with the KGCN model and the LightGCN model. In the first layer of GCN, the model first learned the embedding representations of users and commodity entities on the user-commodity entity interaction graph. Then, the attained user embedding and commodity embedding were used as the data source for the second layer of GCN. In the second layer, the entity v and its neighborhoods were calculated using the hybrid aggregation function proposed in this paper. The result was taken as the new entity v. According to tests on three public datasets and comparison results with the KGCN, LighterKGCN improved by 0.52% and 51.16% in terms of AUC and F1 performances, respectively on the dataset of MovieLens-20M; LighterKGCN improved by 0.67% and 45.0% in terms of AUC and F1 performances, respectively on the dataset of Yelp2018; and the number was 0.67% and 36.35% in AUC and F1 performances, respectively on the dataset of Last.FM.</p> <p> </p>

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“…TransD [10] projects the head entity and tail entity into different vector spaces, respectively, and constructs the projection matrix dynamically through the projection vector, which greatly saves the amounts of parameters and calculations. TransR [11] believes that entities in different relationships have different semantic spaces, so entities and relationships are embedded in two different spaces; that is, entity space and multiple relationship-specific entity spaces, and the entities are projected into a shared hyperplane through the relationship projection matrix in the TransR model. TransM [12] and TransF [13] optimized the model by changing the evaluation function of TransE, improving its ability to represent complex relationships.…”

Section: Related Workmentioning

confidence: 99%

KTAT: A Complex Embedding Model of Knowledge Graph Integrating Type Information and Attention Mechanism

2023

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Knowledge graph embedding learning aims to represent the entities and relationships of real-world knowledge as low-dimensional dense vectors. Existing knowledge representation learning methods mostly aggregate only the internal information of triplets and graph structure information. Recent research has proved that multi-source information of entities is conducive to more accurate knowledge embedding tasks. In this paper, we propose a model based on an attention mechanism and integrating the type information of entities, named KTAT. This model is based on the graph attention mechanism, to distribute corresponding attention mechanisms according to different weights between nodes. We introduce a type-specific hyperplane, which enables entities to have different embedding representations according to their type in the current triplet. Simultaneously, we also used textual description information of entities to improve the performance of the model. We conducted link prediction experiments on the FB15k and FB15k-237 datasets. The experimental results show that our model outperforms previous advanced methods compared to some baseline models, and demonstrate that combining type information can effectively improve the performance of link prediction.

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Learning Entity and Relation Embeddings with Entity Description for Knowledge Graph Completion

Cited by 20 publications

References 11 publications

Decision-Making System for the Diagnosis of Syndrome Based on Traditional Chinese Medicine Knowledge Graph

Decision-Making System for the Diagnosis of Syndrome Based on Traditional Chinese Medicine Knowledge Graph

LighterKGCN: A Recommender System Model based on Bi-layer Graph Convolutional Networks

KTAT: A Complex Embedding Model of Knowledge Graph Integrating Type Information and Attention Mechanism

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