KGSynNet: A Novel Entity Synonyms Discovery Framework with Knowledge Graph

Yang, Yiying; Yin, Xi; Yang, Haiqin; Fei, Xingjian; Peng, Hao; Zhou, Kaijie; Lai, K. Robert; Shen, Jianping

doi:10.1007/978-3-030-73194-6_13

Cited by 13 publications

(6 citation statements)

References 20 publications

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“…There are two evaluation indicators adopted by TransE and TransH: first, the average ranking of correct entities is recorded as MeanRank [43]; Second, the probability that the correct entity ranks in the top 10 is recorded as Hits@10 [44]. The lower the MeanRank is, the better the experimental results are.…”

Section: ) Evaluation Indexmentioning

confidence: 99%

TransH-RA: A Learning Model of Knowledge Representation by Hyperplane Projection and Relational Attributes

et al. 2023

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The TransE model plays a key role in dealing with data sparsity and promotes the development of knowledge graphs completion. However, TransE has some difficulties in dealing with oneto-many, many-to-one, many-to-many and transmission relationships. In order to solve this problem, this paper proposes a knowledge representation learning model based on hyperplane projection and relational attributes, namely TransH-RA. First of all, we introduce the idea of hyperplane projection based on the TransE model, this idea is inspired by TransH, which makes different entities have different roles in a specific relationship, thus reducing the constraints of TransE translation rules, and map the head entity h and tail entity t to the plane of special relation r; Secondly, considering that it is not easy to identify different similar entities, the neighborhood information of entities is added to learn the neighborhood of entities around different entities; Then, in order to further strengthen the ability to deal with complex relationships, attribute features of relationships are added and attribute knowledge is embedded; Eventually, during the training of the model, the probability method is chosen to replace the head and tail entities. Link prediction experiments are conducted on the public datasets FB15K and WN18, and the triple classification experiments on the datasets WN11, FB13 and FB15K are carried out to analyze and verify the effectiveness of the proposed method. The evaluation results show that our method achieves the better results on MeanRank, Hits@10 and ACC indicators compared with TransE and TransH.

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Section: ) Evaluation Indexmentioning

confidence: 99%

TransH-RA: A Learning Model of Knowledge Representation by Hyperplane Projection and Relational Attributes

et al. 2023

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“…[ 22 ] constructed Parkinson's disease KG and KG completion methods that were leveraged to predict drug candidates. Yang et al [ 23 ] pretrained the embeddings of entities by large-scale domain-specific corpus while learning the knowledge embeddings of entities via a joint TransC-TransE model. Lin et al [ 24 ] combined the context provided by medical entity descriptions with the embeddings of medical entities and relations and user embeddings to learn patient similarities through a convolutional neural network.…”

Section: Related Workmentioning

confidence: 99%

Knowledge-Based Recurrent Neural Network for TCM Cerebral Palsy Diagnosis

Tian

et al. 2022

Evidence-Based Complementary and Alternative Medicine

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Cerebral palsy is one of the most prevalent neurological disorders and the most frequent cause of disability. Identifying the syndrome by patients’ symptoms is the key to traditional Chinese medicine (TCM) cerebral palsy treatment. Artificial intelligence (AI) is advancing quickly in several sectors, including TCM. AI will considerably enhance the dependability and precision of diagnoses, expanding effective treatment methods’ usage. Thus, for cerebral palsy, it is necessary to build a decision-making model to aid in the syndrome diagnosis process. While the recurrent neural network (RNN) model has the potential to capture the correlation between symptoms and syndromes from electronic medical records (EMRs), it lacks TCM knowledge. To make the model benefit from both TCM knowledge and EMRs, unlike the ordinary training routine, we begin by constructing a knowledge-based RNN (KBRNN) based on the cerebral palsy knowledge graph for domain knowledge. More specifically, we design an evolution algorithm for extracting knowledge in the cerebral palsy knowledge graph. Then, we embed the knowledge into tensors and inject them into the RNN. In addition, the KBRNN can benefit from the labeled EMRs. We use EMRs to fine-tune the KBRNN, which improves prediction accuracy. Our study shows that knowledge injection can effectively improve the model effect. The KBRNN can achieve 79.31% diagnostic accuracy with only knowledge injection. Moreover, the KBRNN can be further trained by the EMRs. The results show that the accuracy of fully trained KBRNN is 83.12%.

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“…KG-BART [22] presented aKG-augmented approach KG-BART based on pre-trained BART for generative commonsense reasoning. KGSynNet [23] tackle the task of entity synonyms discovery and exploit external knowledge graph and domain-specific corpus. resolved the OOV issue and semantic discrepancy in mention-entity pairs.…”

Section: Combination Of Pre-training Model and Knowledge Graphmentioning

confidence: 99%

Let Knowledge Make Recommendations for You

2021

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The knowledge graph can make more accurate personalized recommendations for the recommendation system, but it is also interpretative and has traces to follow. The purpose of the recommendation system is to recommend a series of unobserved items for users. At present, recommendation systems based on knowledge graphs are mainly implemented in two ways: Embedding-based and pathbased. Embedding methods usually directly use information from the knowledge graph to enrich the representation of an item or user. Still, it failed to introduce multi-hop relations, and it is challenging to use semantic network information. A path-based recommendation algorithm utilizes the knowledge graph to gain multi-hop knowledge and compare the similarity between users or items to improve the recommendation effect. This paper (1) Aiming at the problem of how the recommendation algorithm effectively utilizes the semantically related information of knowledge, a self-attention-based knowledge representation learning model is designed to learn the semantic information of the entity-relationship by using the overall triplet of the entity-relationship to achieve high-quality knowledge features, Which brings more and more helpful information to the recommendation. (2) Constructing a content recommendation model with unified, embedded behavior and knowledge features, using historical user preferences combined with knowledge graphs to dynamically learn knowledge features to bring users more accurate and diverse recommendations. (3) Aiming at the problem of knowledge feature representation learning, a self-attentionbased knowledge representation learning model is proposed. Focusing on the difference in the importance of triples for determining entity semantics, the self-attention mechanism is used to learn semantics from triples to improve knowledge features. The quality of the representation provides high-quality auxiliary information for the recommendation system. The model's performance is demonstrated through link prediction and triple classification experiments to prove the feasibility of the method proposed in this article.

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KGSynNet: A Novel Entity Synonyms Discovery Framework with Knowledge Graph

Cited by 13 publications

References 20 publications

TransH-RA: A Learning Model of Knowledge Representation by Hyperplane Projection and Relational Attributes

TransH-RA: A Learning Model of Knowledge Representation by Hyperplane Projection and Relational Attributes

Knowledge-Based Recurrent Neural Network for TCM Cerebral Palsy Diagnosis

Let Knowledge Make Recommendations for You

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