[PDF] Top 20 Embedding Uncertain Knowledge Graphs
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Embedding Uncertain Knowledge Graphs
... deterministic Knowledge Graphs (KG) have been extensively studied, with the purpose of cap- turing latent semantic relations between entities and incor- porating the structured knowledge they contain ... See full document
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Traversing Knowledge Graphs in Vector Space
... Path modeling. Numerous methods have been proposed to leverage path information for knowl- edge base completion and question answering. Nickel et al. (2014) proposed combining low-rank models with sparse path features. ... See full document
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OpenKE: An Open Toolkit for Knowledge Embedding
... for knowledge em- bedding (OpenKE), which provides a unified framework and various fundamental models to embed knowledge graphs into a continu- ous low-dimensional ...large-scale knowledge ... See full document
6
Context Dependent Knowledge Graph Embedding
... WordNet knowledge base, and then em- ploy word embedding techniques on these random ...heterogeneous graphs with differen- t types of ...during knowledge path ex- ... See full document
6
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
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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 ... See full document
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Knowledge Graph Embedding for Ecotoxicological Effect Prediction
... or knowledge graph ...ecotox:taxon/Carya). Embedding models. Knowledge graph embedding [22] plays a key role in link prediction problems where the goal is to learn a scoring function S : E × R ... See full document
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Knowledge Graph Embedding with Numeric Attributes of Entities
... Recently, a number of Knowledge Graphs (KGs) have been created, such as DBpe- dia (Lehmann, 2015), YAGO (Mahdisoltani et al., 2015), and Freebase (Bollacker et al., 2008). KGs encode structured informa- ... See full document
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Incorporating Domain Knowledge into Medical NLI using Knowledge Graphs
... Tay et al., 2018). With these attempts, even though NLI in domains like fiction, travel etc. has pro- gressed a lot, NLI in medical domain is yet to be explored extensively. With the introduction of MedNLI (Romanov and ... See full document
6
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 the ... See full document
10
TransGate: Knowledge Graph Embedding with Shared Gate Structure
... Embedding knowledge graphs (KGs) into continuous vec- tor space is an essential problem in knowledge ...improve embedding by focus- ing on discriminating relation-specific information ... See full document
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Improving Knowledge Graph Embedding Using Simple Constraints
... Embedding knowledge graphs (KGs) into continuous vector spaces is a focus of cur- rent ...the embedding space, without negative impacts on efficiency or scalabil- ... See full document
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Knowledge Graph Embedding via Dynamic Mapping Matrix
... Knowledge graphs are useful resources for numerous AI applications, but they are far from completeness. Previous work such as TransE, TransH and TransR/CTransR re- gard a relation as translation from head ... See full document
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Learning to Update Knowledge Graphs by Reading News
... The dimensions of all embeddings (words, enti- ties, and relations) are all set to 128, and the hid- den dimension is 256. We use a single layer en- coder, as we find that more layers do not bring any benefit. The basis ... See full document
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Uncertainty Theory Based Novel Multi Objective Optimization Technique Using Embedding Theorem with Application to R & D Project Portfolio Selection
... proposes uncertain process and uncertain differential equation to deal with dynamic uncertain ...addition, uncertain calculus is introduced by Liu [26] to describe the function of uncer- tain ... See full document
11
Probabilistic Embedding of Knowledge Graphs with Box Lattice Measures
... Embedding applications to relational learning constitute a huge field to which it is impossible to do justice, but one general difference between our approaches is that e.g. a matrix factorization model treats the ... See full document
10
Jointly Embedding Knowledge Graphs and Logical Rules
... by embedding models, via integer linear pro- gramming or Markov logic ...modeling knowledge and logic. Since each entity has its own embedding, our approach can successfully make predictions be- ... See full document
11
Knowledge Based Semantic Embedding for Machine Translation
... Our proposed KBSE relies on the knowledge base. To get the semantic vector of source sentence, our semantic space should be able to represent any necessary information in the sentence. For ex- ample, since our ... See full document
10
TransG : A Generative Model for Knowledge Graph Embedding
... on embedding vectors obtained from TransE (Bordes et ...in knowledge bases for two reasons: artificial simplification and nature of ...hand, knowledge base curators could not involve too many similar ... See full document
10
Estimating node connectedness in spatial network under stochastic link disconnection based on efficient sampling
... (Fushimi et al. 2018). Although our method can be applied to general networks in prin- ciple, we target mainly spatial networks because urban road structures can be naturally regarded as uncertain graphs ... See full document
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