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
WG4Rec: Modeling Textual Content with Word Graph for News Recommendation
Shaoyun Shi,Weizhi Ma,Zhen Wang,Min Zhang,Kun Fang,Jingfang Xu,Yiqun Liu,Shaoping Ma +7 more
- 26 Oct 2021
TL;DR: Wang et al. as discussed by the authors proposed a new textual content representation method by building a word graph for recommendation, which is named WG4Rec, which adopts three types of word associations for content representation and user preference modeling.
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Link Prediction with Attention Applied on Multiple Knowledge Graph Embedding Models
Cosimo Gregucci,Mojtaba Nayyeri,D. Hern'andez,Steffen Staab +3 more
- 13 Feb 2023
TL;DR: In this paper , the authors combine the query representations from several models in a unified one to incorporate patterns that are independently captured by each model, and the combined model can learn relational and structural patterns.
Hierarchical Metadata-Aware Document Categorization under Weak Supervision
Yu Zhang,Xiusi Chen,Yu Meng,Jiawei Han +3 more
- 08 Mar 2021
TL;DR: In this paper, a joint representation learning and data augmentation module is proposed for document categorization under weak supervision, which allows simultaneous modeling of category dependencies, metadata information and textual semantics, and introduces a hierarchical synthesizing training documents to complement the original, small-scale training set.
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Categorization of knowledge graph based recommendation methods and benchmark datasets from the perspectives of application scenarios: A comprehensive survey
TL;DR: In this article , the authors explore well-known recommendation systems, popular knowledge repositories, benchmark datasets, recommendation methods, and future research dimensions about the current research, and investigate recommendation methods and associated datasets with respect to the corresponding application scenarios in a categorical way.
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A Knowledge Graph Embedding Approach for Metaphor Processing
TL;DR: Wang et al. as mentioned in this paper presented a method for metaphor processing based on knowledge graph (KG) embedding, where each specific metaphor can be represented as a metaphor triple $(target, attribute, source)$.
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