Journal Article10.1109/TKDE.2017.2754499
Knowledge Graph Embedding: A Survey of Approaches and Applications
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TL;DR: This article provides a systematic review of existing techniques of Knowledge graph embedding, including not only the state-of-the-arts but also those with latest trends, based on the type of information used in the embedding task.
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Abstract: Knowledge graph (KG) embedding is to embed components of a KG including entities and relations into continuous vector spaces, so as to simplify the manipulation while preserving the inherent structure of the KG. It can benefit a variety of downstream tasks such as KG completion and relation extraction, and hence has quickly gained massive attention. In this article, we provide a systematic review of existing techniques, including not only the state-of-the-arts but also those with latest trends. Particularly, we make the review based on the type of information used in the embedding task. Techniques that conduct embedding using only facts observed in the KG are first introduced. We describe the overall framework, specific model design, typical training procedures, as well as pros and cons of such techniques. After that, we discuss techniques that further incorporate additional information besides facts. We focus specifically on the use of entity types, relation paths, textual descriptions, and logical rules. Finally, we briefly introduce how KG embedding can be applied to and benefit a wide variety of downstream tasks such as KG completion, relation extraction, question answering, and so forth.
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Citations
TwHIN-BERT: A Socially-Enriched Pre-trained Language Model for Multilingual Tweet Representations at Twitter
Xinyang Zhang,Yury Malkov,Omar U. Florez,Serim Park,Brian McWilliams,Jiawei Han,Ahmed El-Kishky +6 more
- 04 Aug 2023
TL;DR: TwHIN-BERT is a pre-trained language model tailored for social media text, trained on a massive dataset of tweets and incorporating social engagement logs.
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Efficient Non-Sampling Knowledge Graph Embedding
TL;DR: The Efficient Non-Sampling Knowledge Graph Embedding (NS-KGE) as mentioned in this paper is proposed to reduce the complexity of non-sampling loss function by considering all negative instances in the KG for model learning.
Active Ensemble Learning for Knowledge Graph Error Detection
Junnan Dong,Qinggang Zhang,Xiao Huang,Qiaoyu Tan,Daochen Zha,Zhao Zihao +5 more
- 27 Feb 2023
TL;DR: In this article , an ensemble learning framework for KG error detection is proposed, which adaptively updates the ensemble learning policy in each iteration based on active queries, i.e., the answers from experts.
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Drug repurposing against Parkinson's disease by text mining the scientific literature
TL;DR: The proposed strategy was used to mine information pertaining to the mechanisms of disease treatment from known treatment relationships and predict drugs for repurposing against Parkinson's disease, indicating the effectiveness of the proposed approach.
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InGram: Inductive Knowledge Graph Embedding via Relation Graphs
TL;DR: In this article , a relation graph is defined as a weighted graph consisting of relations and the affinity weights between them, and an attention mechanism is used to aggregate neighboring embeddings to generate relation and entity embedding.
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