[PDF] Top 20 Knowledge Graph Embedding for Ecotoxicological Effect Prediction
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Knowledge Graph Embedding for Ecotoxicological Effect Prediction
... a knowledge graph called TERA that aims at covering the knowledge and data relevant to the ecotoxicological ...for ecotoxicological effect prediction based on ... See full document
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Knowledge Driven Event Embedding for Stock Prediction
... Stock Prediction We compare our knowledge-driven event embeddings with the baseline methods on individual stock prediction, using the 15 companies selected by Ding et ...that knowledge-driven ... See full document
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Knowledge Graph Embedding via Dynamic Mapping Matrix
... the embedding space. For a golden triplet (h, r, t), the embedding h is close to the embedding t by adding the embedding r, that is h + r ≈ ... See full document
10
TransGate: Knowledge Graph Embedding with Shared Gate Structure
... Link prediction and triplets classification are implemented on two large-scale knowledge graphs: WordNet (Miller 1995) and Freebase (Bollacker et ...semantic knowledge of ... See full document
8
Knowledge Graph Embedding with Numeric Attributes of Entities
... Because our approach is built based on TransE, we compare our approach with TransE to see whether adding attribute embedding in the model improves the performance of link prediction. For TransE and TransEA, ... See full document
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Transition-based Knowledge Graph Embedding with Relational Mapping Properties
... We carry out extensive experiments in two dif- ferent application scenarios, i.e. link prediction and triplet classification. For each task, we compare the proposed TransM with the state-of-the-art method TransE ... See full document
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Improved Knowledge Graph Embedding Using Background Taxonomic Information
... Knowledge Graphs (KGs) are graph structured knowl- edge bases that store facts about the world. A large num- ber of KGs have been created such as NELL (Carlson et al. 2010), F REEBASE (Bollacker et al. ... See full document
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Relation Embedding with Dihedral Group in Knowledge Graph
... link prediction task, fol- lowed by an introduction to group theory and di- hedral ...relation embedding space by showing that the desired properties of (skew-) symmetry, inversion and relation composition ... See full document
10
Investigating the logical inference capabilities of Knowledge Graph Embedding Models
... link prediction methods are the Mean Reciprocal Rank (MRR) and ...link prediction is to predict tail entity given subject entity and relation, or subject entity given tail entity and ...the graph ... See full document
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Logic Attention Based Neighborhood Aggregation for Inductive Knowledge Graph Embedding
... Link prediction in the inductive setting aims at reasoning the missing part “?” in a triplet when given (s, r, ?) or (?, r, o) with emerging entities s or o ... See full document
8
A Translation Based Knowledge Graph Embedding Preserving Logical Property of Relations
... Knowledge graph embedding is another promi- nent method for link prediction (Bordes et ...a knowledge graph into a continuous low dimensional space as vectors, and embeds ... See full document
10
A Capsule Network based Embedding Model for Knowledge Graph Completion and Search Personalization
... Triple modeling is applied not only to the KG completion, but also for other tasks which can be formulated as a triple-based prediction prob- lem. An example is in search personalization, one would aim to tailor ... See full document
10
Relation prediction in knowledge graph by Multi-Label Deep Neural Network
... accurate prediction of entity relations even when entity descriptions are not ...lower prediction accuracy in ...the embedding layer of head entity and tail ... See full document
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Candidate gene prioritization using graph embedding
... a knowledge graph approach combined with embedding methods to overcome these ...Translating Embedding model and Convolution Knowledge Base model, to vectorize gene ...link ... See full document
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Knowledge graph embeddings
... Web, knowledge graphs have become an important data source em- powering many ...edge graph embedding techniques were proposed to project entities and relations from a KG into a low-dimensional ... See full document
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FRS: A Simple Knowledge Graph Embedding Model for Entity Prediction
... entity prediction model, this paper proposes a new neural network model where the entities and relationships are modeled by a three-layer structure with a feature processing layer, a refactoring layer, and a ... See full document
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Knowledge Graph and Text Jointly Embedding
... the embedding approach to reason new relational facts from a large- scale knowledge graph and a text ...The embedding process attempts to preserve the relations between entities in the ... See full document
11
Knowledge graph embedding by dynamic translation
... ABSTRACT Knowledge graph embedding aims at representing entities and relations in a knowledge graph as dense, low-dimensional and real-valued ...in knowledge graphs, and improve ... See full document
10
Semantically Smooth Knowledge Graph Embedding
... bedding Knowledge Graphs (KGs) con- sisting of entities and relations into low- dimensional vector ...the embedding space, we propose Semantically Smooth Embedding ...the embedding space to be ... See full document
11
Graph-embedding Enhanced Attention Adversarial Autoencoder
... robust embedding representations against uncertainty. In our experiments, we test those two prior data distributions and show almost have the same impact on results. However, here we only proposed a generalized ... See full document
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