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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Adverse Drug Event Prediction Using Noisy Literature-Derived Knowledge Graphs: Algorithm Development and Validation.
TL;DR: This paper used knowledge graphs (KGs) to learn representations of clinical concepts from the KG, which are used in machine learning models to predict adverse drug events (ADEs) and achieved state-of-the-art performance.
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Soft Marginal TransE for Scholarly Knowledge Graph Completion.
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Knowledge graph embedding methods for entity alignment: experimental review
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Towards Building a Multilingual Sememe Knowledge Base: Predicting Sememes for BabelNet Synsets
TL;DR: This work builds a unified sememe KB for multiple languages based on BabelNet, a multilingual encyclopedic dictionary, and presents a novel task of automatic sememe prediction for synsets, aiming to expand the seed dataset into a usable KB.
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•Proceedings Article
Knowledge Graph Embeddings: Are Relation-Learning Models Learning Relations?
Andrea Rossi,Antonio Matinata +1 more
- 01 Jan 2020
TL;DR: This study provides a more robust evaluation direction for future research on relation learning models, stressing that understanding why LP models reach certain performances is a crucial step towards explaining predicted relations.
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