Graph embedding
Knowledge graph embedding by dynamic translation
... Knowledge graph embedding aims at representing entities and relations in a knowledge graph as dense, low-dimensional and real-valued ...various embedding models appeared in recent years, the ...
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A Translation Based Knowledge Graph Embedding Preserving Logical Property of Relations
... Knowledge graph embedding is another promi- nent method for link prediction (Bordes et ...knowledge graph into a continuous low dimensional space as vectors, and embeds relations as vectors or ...
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A Grassmann graph embedding framework for gait analysis
... by embedding the manifold into reproducing kernel Hilbert space and applying the mechanics of graph embedding on such manifold, significant performance improvement can be ...
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Knowledge Graph Embedding for Ecotoxicological Effect Prediction
... knowledge graph embedding approach for ecotoxicological effect ...knowledge graph has been constructed from publicly available data sets, including a species taxonomy and chemical classifi- cation ...
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TransGate: Knowledge Graph Embedding with Shared Gate Structure
... A number of works attempt to improve knowledge graph embedding in different ways. Some models explore different loss function to improve embeddings. Zhou et al. (Zhou et al. 2017) propose a limit-based ...
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TransG : A Generative Model for Knowledge Graph Embedding
... knowledge graph embedding, which projects symbolic entities and rela- tions into continuous vector space, has be- come a new, hot topic in artificial intelli- ...edge graph embedding, and at ...
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Weak Greedy Routing over Graph Embedding for Wireless Sensor Networks
... In our work, we propose a new weak greedy routing method. It does not need the geographic location. It establishes a tree-based graph embedding rather than a greedy embedding. In the tree-based ...
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ATP: Directed Graph Embedding with Asymmetric Transitivity Preservation
... Some graph embedding methods (Fang et ...of graph embedding seeks to represent vertices of a graph in a low-dimensional vector space in which meaningful semantic, relational and struc- ...
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Integration of Knowledge Graph Embedding Into Topic Modeling with Hierarchical Dirichlet Process
... Knowledge graph (KG) embedding (Bordes et ...edge graph embedding (TMKGE), a hierarchical Dirichlet process (HDP) based model to extract more coherent topics by taking advantage of the KG ...
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Graph Embedding Using Frequency Filtering
... of graph embedding is to embed graphs in vector space such that the embedded feature vectors follow the differences and similarities of the source ...Filtering Embedding (FFE) is proposed which uses ...
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Entropic graph embedding via multivariate degree distributions
... for embedding, clustering and classification problems, there is relatively little literature aimed at dealing with such problems for directed ...characterizing graph structure that can be used to embed ...
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Local Graph Embedding Based on Maximum Margin Criterion (LGE/MMC) for Face Recognition
... In pattern recognition, feature extraction techniques are widely employed to reduce the dimensionality of data and enhance the discriminatory information. In this paper, we proposed a new method for feature extraction ...
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Context Dependent Knowledge Graph Embedding
... Li Guo. 2015. Semantically smooth knowledge graph embedding. In Proceedings of the 53rd An- nual Meeting of the Association for Computational Linguistics and the 7th International Joint Confer- ence on ...
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Logic Attention Based Neighborhood Aggregation for Inductive Knowledge Graph Embedding
... Knowledge graph embedding aims at modeling entities and relations with low-dimensional ...knowledge graph completion tasks, we experimentally validate LAN’s superiority in terms of the desired ...
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Knowledge Graph Embedding via Dynamic Mapping Matrix
... Triplets classification and link prediction are im- plemented on two popular knowledge graphs: WordNet (Miller 1995) and Freebase (Bollacker et al. 2008). WordNet is a large lexical knowledge graph. Entities in ...
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Transition-based Knowledge Graph Embedding with Relational Mapping Properties
... fore, Bordes et al. propose to embed relationship r as a transitional vector into the same continuous space with the entities, i.e. h and t. They be- lieve that if a triplet ( h, r, t ) does stand for a rela- tion ...
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Improving Knowledge Graph Embedding Using Simple Constraints
... Implementation Details: We compare our ap- proach against all the three groups of baselines on the benchmarks of WN18 and FB15K. We direct- ly report their original results on these two datasets to avoid ...
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Generic Object Recognition Using Graph Embedding into A Vector Space
... To deal with this problem, we propose a method in this paper to connect keypoints with lines, as shown in Fig. 4, and to express the sets of the local features as a graph. Moreover, we propose a technique with ...
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Improved Knowledge Graph Embedding Using Background Taxonomic Information
... the embedding of entities, we simply apply an element-wise non-linearity φ ∶ R → R ≥ 0 before evaluation – that is we replace µ ( h, r, t ) with µ ( φ ( h ) , r, φ ( t )) ...
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Semantically Smooth Knowledge Graph Embedding
... KG embedding objective function. As such, SSE obtains an embedding space which is semantically smooth and at the same time com- patible with observed ...
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