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
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TL;DR: KGvec2go, a Web API for accessing and consuming graph embeddings in a light-weight fashion in downstream applications, is presented and it is shown that the trained models have semantic value by evaluating them on multiple semantic benchmarks.
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Learning Region Similarity over Spatial Knowledge Graphs with Hierarchical Types and Semantic Relations
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TL;DR: Experimental results on two real-world datasets show that the proposed SKRL4RS outperforms the state-of-the-art by a significant margin in terms of the accuracy of measuring region similarity.
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Improving Risk Assessment of Miscarriage During Pregnancy with Knowledge Graph Embeddings
Hegler Tissot,Hegler Tissot,Hegler Tissot,Lucas Alexandre Pedebôs +3 more
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TL;DR: The experiments show that simple knowledge embedding approaches that utilize domain-specific metadata perform better than complex embedding strategies, although both can improve results comparatively to a population probabilistic baseline in both AUPRC, F1-score and a proposed normalized version of these evaluation metrics that better reflects accuracy for unbalanced datasets.
Multi-label Learning with a Cone-Based Geometric Model
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•Proceedings Article
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Zhanqiu Zhang,Jianyu Cai,Jie Wang +2 more
- 01 Jan 2020
TL;DR: In this article, the DUality-induced RegulArizer (DURA) regularizer is proposed to solve the overfitting problem in tensor factorization based KGC models.
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