[PDF] Top 20 Variational Knowledge Graph Reasoning
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Variational Knowledge Graph Reasoning
... a variational framework to understand the variability of human language about entity ...a variational framework to cope with the complex link connections in large ...to variational auto-encoder as ... See full document
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Modeling Event Background for If Then Commonsense Reasoning Using Context aware Variational Autoencoder
... In dialogue generation, Zhao et al. (2017) adapts VAE with encoder-decoder framework to model the latent semantic distribution of answers, which can increase the diversity of generations. For the task of machine ... See full document
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DIVINE: A Generative Adversarial Imitation Learning Framework for Knowledge Graph Reasoning
... Knowledge graphs (Suchanek et al., 2007; Auer et al., 2007; Bollacker et al., 2008; Carlson et al., 2010; Vrandeˇci´c and Kr¨otzsch, 2014), typically composed of massive relational triples, are useful resources ... See full document
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Adapting Meta Knowledge Graph Information for Multi Hop Reasoning over Few Shot Relations
... Multi-Hop Reasoning over KGs aims to learn symbolic inference rules from relational paths in G and has been formulated as sequential deci- sion problems in recent ...These reasoning methods are intuitive ... See full document
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Efficient learning of non-autoregressive graph variational autoencoders for molecular graph generation
... In recent years, machine learning has been actively adopted to accelerate the discovery of novel mole- cules with desired properties [1–4]. While traditional approaches, such as manual design and enumeration [5–8], ... See full document
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Automatic Extraction of Reasoning Chains from Textual Reports
... expert knowledge contained in these reports may greatly increase productiv- ity of the ...a graph-based text representation that makes the relations between textual units ex- ... See full document
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GEAR: Graph based Evidence Aggregating and Reasoning for Fact Verification
... Due to the rapid development of information ex- traction (IE), huge volumes of data have been extracted. How to automatically verify the data becomes a vital problem for various data- driven applications, e.g., ... See full document
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Visual-Semantic Graph Reasoning for Pedestrian Attribute Recognition
... transfer knowledge among different attributes; (6) DeepMAR (Li, Chen, and Huang 2015) is a joint at- tribute learning model which considers inter-attribute cor- relation by weighted cross entropy loss function; ... See full document
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Question Answering by Reasoning Across Documents with Graph Convolutional Networks
... acquire knowledge from text ...require reasoning across the doc- ument, but they can be answered relying on in- formation contained in a single sentence (Weis- senborn et ... See full document
12
Dynamically Fused Graph Network for Multi hop Reasoning
... We do not assume a global knowledge base. In- stead, we use the Stanford corenlp toolkit (Man- ning et al., 2014) to recognize named entities from the context C. The number of extracted entities is denoted as N . ... See full document
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Knowledge Graph and Text Jointly Embedding
... the knowledge graph and the text corpus, ...analogical reasoning task show that jointly em- bedding is comparable to or slightly better than word2vec ... See full document
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GraphBTM: Graph Enhanced Autoencoded Variational Inference for Biterm Topic Model
... Instead of adding external knowledge, one po- tential way is to add a sparse prior on the topic distribution. Chien and Chang (2014) used a spike model to control the sparsity of selected topics. Lin et al. (2014) ... See full document
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DeepPath: A Reinforcement Learning Method for Knowledge Graph Reasoning
... most reasoning tasks. 4.3.2 Qualitative Analysis of Reasoning Paths To analyze the properties of reasoning paths, we show a few reasoning paths found by the agent in Table ...first ... See full document
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KagNet: Knowledge Aware Graph Networks for Commonsense Reasoning
... watch, tv, sort, pain, etc. } . We are aware of the fact that such retrieved mentioned concepts are not always perfect (e.g. “sort” is not a semantically re- lated concept, “close” is a polysemous concept). How to ... See full document
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Collaborative Policy Learning for Open Knowledge Graph Reasoning
... interpretable graph reasoning ...open knowledge graph reasoning—a task that aims to reason for missing facts over a graph augmented by a background text ...multi-hop graph ... See full document
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Incorporating Graph Attention Mechanism into Knowledge Graph Reasoning Based on Deep Reinforcement Learning
... of a path and reasons about conjunctions of multi- hop relations non-atomically (Neelakantan et al., 2015). Guu et al. propose a soft edge traversal operator, which can be recursively applied to pre- dict paths and ... See full document
9
Knowledge graph embedding by dynamic translation
... Knowledge graph is one of the most popular approaches for representing knowledge on the current ...typical knowledge graph often describes the knowledge as multi- relational data ... See full document
10
Projecting the Knowledge Graph to Syntactic Parsing
... We integrate the Knowledge Base data into the training algorithm using a sampling function. The idea is to correct errors in the candidate parse by using the KB. The sample function corrects only relations among ... See full document
5
FarsBase: The Persian Knowledge Graph | www.semantic-web-journal.net
... few knowledge graphs are constructed based on raw-text, most notably KnowledgeVault and ...Such knowledge graphs either use crowd- sourcing (which needs a high amount of user contribution) or heavily rely ... See full document
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A general framework for genome rearrangement with biological constraints
... Take genomes A and B partitioned into n genes where each gene occurs exactly once in each genome, and a labe- ling of a breakpoint graph G(A, B). For all the vertices v = ◦ we have deg g (G(A, B), v) = deg b (G(A, ... See full document
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