[PDF] Top 20 DeepPath: A Reinforcement Learning Method for Knowledge Graph Reasoning
Has 10000 "DeepPath: A Reinforcement Learning Method for Knowledge Graph Reasoning" found on our website. Below are the top 20 most common "DeepPath: A Reinforcement Learning Method for Knowledge Graph Reasoning".
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
10
Incorporating Graph Attention Mechanism into Knowledge Graph Reasoning Based on Deep Reinforcement Learning
... Deep Reinforcement Learn- ing (DRL) into the task of predicting the missing links, such as DeepPath (Xiong et ...path-based method. DeepPath is the first work which incorporates DRL into KG ... See full document
9
DIVINE: A Generative Adversarial Imitation Learning Framework for Knowledge Graph Reasoning
... From the results shown in Table 2, we can ob- serve that our framework produces consistent im- provements for the two RL-based methods under varying degrees on both link prediction and fac- t prediction tasks. On the one ... See full document
10
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 ...years. DeepPath (Xiong et ...These ... See full document
6
KBGAN: Adversarial Learning for Knowledge Graph Embeddings
... train knowledge graph embedding mod- ...final knowledge graph ...step reinforcement learning setting, and use a variance-reduction REINFORCE method to ... See full document
11
KagNet: Knowledge Aware Graph Networks for Commonsense Reasoning
... Commonsense knowledge and ...commonsense reasoning (Talmor et ...supervised learning methods for commonsense ...explicit knowledge used in inference. A unique merit of our K A G N E T ... See full document
11
Accelerated Method based on Reinforcement Learning and Case Base Reasoning in Multi agent Systems
... Based Reasoning (CBR) is a knowledge based problem solving technique, which is based on reusing on the previous experiences and has been originated from the researches of cognitive sciences ...this ... See full document
7
Playing Text Adventure Games with Graph Based Deep Reinforcement Learning
... in graph embedding and attention tech- niques (Guan et ...the graph to pay atten- tion to given an input state description in addi- tion to having a mechanism that allows for natu- ral language action ...a ... See full document
9
Collaborative Policy Learning for Open Knowledge Graph Reasoning
... for reasoning on sparse KGs are not enough, and path-based mod- els cannot capture the underlying ...other graph reasoning meth- ...The graph is dense at 100%, and the bene- fits from the ... See full document
10
Representation Learning with Ordered Relation Paths for Knowledge Graph Completion
... Incompleteness is a common problem for ex- isting knowledge graphs (KGs), and the com- pletion of KG which aims to predict links be- tween entities is challenging. Most existing KG completion methods only consider ... See full document
10
Variational Knowledge Graph Reasoning
... for knowledge graph reason- ...plicit reinforcement learning path finding (Xiong et ...sentation learning methods. Empirically, we show that our method has achieved the ... See full document
10
Jointly Embedding Entities and Text with Distant Supervision
... The robust terminologies available in the biomedical domain have been instrumental to sev- eral recent annotation–based approaches. De Vine et al. (2014) use string matching heuristics to find possible occurrences of ... See full document
12
Accurate Text Enhanced Knowledge Graph Representation Learning
... To resolve the ambiguity of entities and rela- tions in different triples (i.e., a relation/entity may have different meanings in different triples), Xiao et al. (2016b) proposed a generative model to han- dle the ... See full document
11
Learning topic description from clustering of trusted user roles and event models characterizing distributed provenance networks: a reinforcement learning approach
... a reinforcement learning based message transfer model for transferring news report messages through a selected path in a trusted provenance network with the objective of maximizing the reward values based ... See full document
34
Assessment of Linearity Improvement in Optical Communication Systems with Machine Learning Methods
... Use of Machine Learning (ML) methodologies in optical communications has paved a new pathway. In this paper, firstly, we discuss the use of ML methodologies for reducing optical fiber nonlinearities, nonlinearity ... See full document
7
Learning Sequence Encoders for Temporal Knowledge Graph Completion
... DIST M ULT against TT RANS E, and the standard embedding methods T RANS E and DIST M ULT . For all approaches, we used ADAM (Kingma and Ba, 2014) for parameter learning in a mini-batch setting with a ... See full document
6
Crafting effective problems for problem-based learning
... that learning is quicker when students possess self-monitoring skills generally referred to as ...a learning and teaching philosophy that fully develops a student’s ability at the meta-cognitive ...of ... See full document
24
SAT and ATPG: Algorithms for Boolean Decision Problems
... Abstract The problems of Boolean satisfiability (SAT) and automatic test pattern gen- eration (ATPG) are strongly related - both in terms of application areas (pre- manufacturing design validation and post-manufacturing ... See full document
33
A Study of a Robotic Assembly System as a Collaborative Multi-Agent Organization
... solving, learning and communication. The system employs the reinforcement learning method for acquiring new solutions and a neural network for the recognition of work-space structure ... See full document
11
Joint Semantic Relevance Learning with Text Data and Graph Knowledge
... with, knowledge embedding can be classified into three categories: raw text learning, labeled text learning and graph knowledge learn- ...text learning, the entities are treated ... See full document
9
Related subjects