Proving Correctness of Compilers Using Structured Graphs

2014 ◽  
pp. 221-237 ◽  
Author(s):  
Patrick Bahr
Keyword(s):  
Structured Graphs

scholarly journals OpenKG Chain: A Blockchain Infrastructure for Open Knowledge Graphs

2021 ◽  
pp. 1-18
Author(s):  
Huajun Chen ◽  
Ning Hu ◽  
Guilin Qi ◽  
Haofen Wang ◽  
Zhen Bi ◽  
...  
Keyword(s):  
Open Data ◽  
Knowledge Bases ◽  
Knowledge Graph ◽  
Cumulative Number ◽  
Trust Network ◽  
Fine Grained ◽  
Knowledge Based ◽  
Granularity Level ◽  
Knowledge Graphs ◽  
Structured Graphs

Abstract The early concept of knowledge graph originates from the idea of the Semantic Web, which aims at using structured graphs to model the knowledge of the world and record the relationships that exist between things. Currently publishing knowledge bases as open data on the Web has gained significant attention. In China, CIPS(Chinese Information Processing Society) launched the OpenKG in 2015 to foster the development of Chinese Open Knowledge Graphs. Unlike existing open knowledge-based programs, OpenKG chain is envisioned as a blockchain-based open knowledge infrastructure. This article introduces the first attempt at the implementation of sharing knowledge graphs on OpenKG chain, a blockchain-based trust network. We have completed the test of the underlying blockchain platform, as well as the on-chain test of OpenKG's dataset and toolset sharing as well as fine-grained knowledge crowdsourcing at the triple level. We have also proposed novel definitions: K-Point and OpenKG Token, which can be considered as a measurement of knowledge value and user value. 1033 knowledge contributors have been involved in two months of testing on the blockchain, and the cumulative number of on-chain recordings triggered by real knowledge consumers has reached 550,000 with an average daily peak value of more than 10,000. For the first time, We have tested and realized on-chain sharing of knowledge at entity/triple granularity level. At present, all operations on the datasets and toolset in OpenKG.CN, as well as the triplets in OpenBase, are recorded on the chain, and corresponding value will also be generated and assigned in a trusted mode. Via this effort, OpenKG chain looks to provide a more credible and traceable knowledge-sharing platform for the knowledge graph community.

A bandwidth reduction algorithm for L-shaped and Z-shaped grid structured graphs

2011 ◽  
Vol 39 (6) ◽  
pp. 441-446
Author(s):  
L. Jones Tarcius Doss ◽  
P. Arathi
Keyword(s):  
Reduction Algorithm ◽  
Bandwidth Reduction ◽  
Structured Graphs

scholarly journals Path Problems in Structured Graphs

1986 ◽  
Vol 29 (6) ◽  
pp. 553-563 ◽  
Author(s):  
M. Ancona
Keyword(s):  
Structured Graphs

ON LEARNING LAPLACIANS OF TREE STRUCTURED GRAPHS

2018 ◽  
Author(s):  
Keng-Shih Lu ◽  
Eduardo Pavez ◽  
Antonio Ortega
Keyword(s):  
Structured Graphs

scholarly journals Learning cognitive maps as structured graphs for vicarious evaluation

2019 ◽  
Author(s):  
Rajeev V. Rikhye ◽  
Nishad Gothoskar ◽  
J. Swaroop Guntupalli ◽  
Antoine Dedieu ◽  
Miguel Lázaro-Gredilla ◽  
...  
Keyword(s):  
Cognitive Maps ◽  
Spatial Relations ◽  
Representation Learning ◽  
Learning Mechanisms ◽  
Efficient Planning ◽  
Maze Navigation ◽  
Flexible Behavior ◽  
Hierarchical Abstraction ◽  
Structured Graphs ◽  
Conceptual Relationships

AbstractCognitive maps are mental representations of spatial and conceptual relationships in an environment. These maps are critical for flexible behavior as they permit us to navigate vicariously, but their underlying representation learning mechanisms are still unknown. To form these abstract maps, hippocampus has to learn to separate or merge aliased observations appropriately in different contexts in a manner that enables generalization, efficient planning, and handling of uncertainty. Here we introduce a specific higher-order graph structure – clone-structured cognitive graph (CSCG) – which forms different clones of an observation for different contexts as a representation that addresses these problems. CSCGs can be learned efficiently using a novel probabilistic sequence model that is inherently robust to uncertainty. We show that CSCGs can explain a variety cognitive map phenomena such as discovering spatial relations from an aliased sensory stream, transitive inference between disjoint episodes of experiences, formation of transferable structural knowledge, and shortcut-finding in novel environments. By learning different clones for different contexts, CSCGs explain the emergence of splitter cells and route-specific encoding of place cells observed in maze navigation, and event-specific graded representations observed in lap-running experiments. Moreover, learning and inference dynamics of CSCGs offer a coherent explanation for a variety of place cell remapping phenomena. By lifting the aliased observations into a hidden space, CSCGs reveal latent modularity that is then used for hierarchical abstraction and planning. Altogether, learning and inference using a CSCG provides a simple unifying framework for understanding hippocampal function, and could be a pathway for forming relational abstractions in artificial intelligence.

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