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Debiasing knowledge graph embeddings [PDF]
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2020 It has been shown that knowledge graph embeddings encode potentially harmful social biases, such as the information that women are more likely to be nurses, and men more likely to be bankers. As graph embeddings begin to be used more widely in NLP pipelines, there is a need to develop training methods which remove such biases.
Joseph Fisher +3 more
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SMR: Medical Knowledge Graph Embedding for Safe Medicine Recommendation [PDF]
, 2021 Most of the existing medicine recommendation systems that are mainly based on electronic medical records (EMRs) are significantly assisting doctors to make better clinical decisions benefiting both patients and caregivers.
Gong, F +4 more
core +1 more source
MöbiusE: Knowledge Graph Embedding on Möbius ring [PDF]
, 2021 In this work, we propose a novel Knowledge Graph Embedding (KGE) strategy, called MöbiusE, in which the entities and relations are embedded to the surface of a Möbius ring.
Xiong, W +4 more
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Knowledge Graph Embedding via Metagraph Learning [PDF]
, 2021 Knowledge graph embedding aims to represent entities and relations in a continuous feature space while preserving the structure of a knowledge graph.
Joyce Jiyoung Whang +3 more
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Knowledge Graph Embeddings [PDF]
, 2012 With the growing popularity of multi-relational data on the Web, knowledge graphs (KGs) have become a key data source in various application domains, such as Web search, question answering, and natural language understanding. In a typical KG such as Freebase (Bollacker et al.
Rosso, Paolo +2 more
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What Is Learned in Knowledge Graph Embeddings? [PDF]
, 2022 A knowledge graph (KG) is a data structure which represents entities and relations as the vertices and edges of a directed graph with edge types. KGs are an important primitive in modern machine learning and artificial intelligence. Embedding-based models, such as the seminal TransE [Bordes et al., 2013] and the recent PairRE [Chao et al., 2020] are ...
Michael R. Douglas +6 more
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Application and evaluation of knowledge graph embeddings in biomedical data [PDF]
PeerJ Computer Science, 2021 Linked data and bio-ontologies enabling knowledge representation, standardization, and dissemination are an integral part of developing biological and biomedical databases.
Mona Alshahrani +2 more
doaj +2 more sources
Hypernetwork Knowledge Graph Embeddings [PDF]
, 2019 Knowledge graphs are graphical representations of large databases of facts, which typically suffer from incompleteness. Inferring missing relations (links) between entities (nodes) is the task of link prediction. A recent state-of-the-art approach to link prediction, ConvE, implements a convolutional neural network to extract features from concatenated
Ivana Balazevic +2 more
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Embedding Knowledge Graph through Triple Base Neural Network and Positive Samples [PDF]
Computer and Knowledge Engineering, 2022 Representation learning on a knowledge graph aims to capture patterns in the knowledge graph as low-dimensional dense distributed representation vectors in the continuous semantic space, which is a powerful technique for predicting missing links in ...
Sogol Haghani, Mohammad Reza Keyvanpour
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Knowledge Graph Embedding Technology: A Review
Jisuanji kexue yu tansuo, 2021 Knowledge graph embedding (KGE) is a new research hotspot in the field of knowledge graphs, which aims to apply the translation invariance of word vectors to embedding entities and relationships of the knowledge graph into a low-dimensional vector space ...
SHU Shitai, LI Song+, HAO Xiaohong, ZHANG Liping
doaj +1 more source