[PDF] Top 20 Knowledge Graph Embedding via Dynamic Mapping Matrix
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Knowledge Graph Embedding via Dynamic Mapping Matrix
... Link prediction is to predict the missing h or t for a golden triplet (h, r, t). In this task, we remove the head or tail entity and then replace it with all the entities in dictionary in turn for each triplet in test ... See full document
10
A Capsule Network based Embedding Model for Knowledge Graph Completion and Search Personalization
... an embedding model, named CapsE, exploring a capsule net- work to model relationship triples (subject, re- lation, ...3-column matrix where each col- umn vector represents the embedding of an element ... See full document
10
Knowledge Graph Completion via Complex Tensor Factorization
... learning, knowledge graph completion deals with automati- cally understanding the structure of large knowledge graphs—labeled directed graphs— and predicting missing relationships—labeled ... See full document
38
Graph Representation for Accurate Mapping and Reconstruction: Node Listed Adjacency Matrix
... Jose Cadena et al. published a research work detecting hotspots and anomalies are a recurring problem with a wide range of applications, such as social network analysis, epidemiology, finance, and bio-surveillance, among ... See full document
5
Knowledge Graph Embedding for Ecotoxicological Effect Prediction
... Since there does not exist a complete and public alignment between ECO- TOX species and the NCBI Taxonomy, we have used the LogMap [11, 12] ontol- ogy alignment systems to index and align the ECOTOX and NCBI ... See full document
18
TransGate: Knowledge Graph Embedding with Shared Gate Structure
... on embedding knowledge graphs into continuous vector spaces for knowledge graph ...optimize embedding and reduce parameters ...avoid matrix-vector multiplication oper- ations, we ... See full document
8
Semi supervised Entity Alignment via Joint Knowledge Embedding Model and Cross graph Model
... joint Knowledge Embedding model and Cross-Graph model (KECG), which combines the above two types of ...the knowledge embedding model learns KG representations to implicitly complete ... See full document
10
Detect Camouflaged Spam Content via StoneSkipping: Graph and Text Joint Embedding for Chinese Character Variation Representation
... cognition knowledge of Chinese and the contextual information, users are able to consume the spam information, even when some characters in the content are intentionally mutated into their similar variations ... See full document
10
A Translation Based Knowledge Graph Embedding Preserving Logical Property of Relations
... able. Since Hits@10 of TransD in “bern” is the best performance ever reported, that of lppTransD in “bern” becomes a new state-of-the-art performance. Table 5 exhibits Hits@10s according to mapping property of the ... See full document
10
Relation Embedding with Dihedral Group in Knowledge Graph
... In RESCAL (Nickel et al., 2011) each relation is represented by a full-rank matrix. As a downside, there is a huge number of parameters in RESCAL making the model prone to overfitting. A totally symmetric DistMult ... See full document
10
Knowledge graph embedding by dynamic translation
... In this paper, we only combine our DT principle with classical translation-based models. One of our future works is to incorporate more information such as the relation paths [31], [32] and the textual descriptions on ... See full document
10
Transition-based Knowledge Graph Embedding with Relational Mapping Properties
... is not flexible enough to tackle well with the various relational mapping properties of triplet- s, even though Bordes et al. (2013b; 2013a) realize the harm on performance through split- ting the dataset into ... See full document
10
Megaman: Scalable Manifold Learning in Python
... megaman puts in the hands of scientists and methodologists alike tools that enable them to apply state of the art manifold learning methods to data sets of realistic size. The package is extensible, modular, with an API ... See full document
5
Distantly Supervised Biomedical Knowledge Acquisition via Knowledge Graph Based Attention
... biomedical knowledge graph and biomedi- cal textual data, which will be detailed as ...Knowledge Graph. We choose the UMLS as the KG. UMLS is a large biomedical knowledge base developed ... See full document
10
Logic Attention Based Neighborhood Aggregation for Inductive Knowledge Graph Embedding
... served knowledge graph becomes sparser), all models’ per- formance would ...sparse knowledge graphs, we conduct link prediction on datasets with different sample rates R as described in Step 1 of the ... See full document
8
Integration of Knowledge Graph Embedding Into Topic Modeling with Hierarchical Dirichlet Process
... word embedding and topic ...the knowledge graph structure in- cluded into the entity embedding conveys more in- formation than pure word ... See full document
11
Cross lingual Knowledge Graph Alignment via Graph Matching Neural Network
... entity graph to repre- sent the contextual information of an entity within the KG and view this task as a graph matching ...a graph matching model which induces a graph matching vector by ... See full document
6
Kernelization via sampling with applications to dynamic graph streams
... • Vertex Cover and Hitting Set: There exists an O(k ˜ d ) space algorithm that solves the minimum hitting set problem where d is the cardinality of the input sets and k is an upper bound on the size of the minimum ... See full document
20
Long tail Relation Extraction via Knowledge Graph Embeddings and Graph Convolution Networks
... the knowledge from data-rich classes at the head of the distribution to boost the performance of the data-poor classes at the ...relational knowledge among class labels from knowledge graph ... See full document
10
Coupling between Vias and the PCB Power-Bus
... lumped via model from the physics of the geometry ...the via model elements are presented in ...a via are derived using a matrix de-embedding algorithm and afterwards two-port theory is ... See full document
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