dbgroup - User contributions [en]

2019YFE0198600

2022-11-05T09:33:28Z

2021244229: /* 专利 */

{{DISPLAYTITLE:分布式知识图谱数据管理关键技术与系统 (国家重点研发计划项目)}}
== 成果 ==
=== 论文 ===
# 天大. OntoCSM：Ontology-Aware Characteristic Set Merging for RDF Type Discovery. DASFAA 2021 [https://link.springer.com/chapter/10.1007/978-3-030-73194-6_22](CCF B)
# 人大. vRaft: Accelerating the Distributed Consensus Under Virtualized Environments. DASFAA 2021 (CCF B)
# 天大. Optimal Subgraph Matching Queries over Distributed Knowledge Graphs Based on Partial Evaluation. WISE 2021 [https://link.springer.com/chapter/10.1007/978-3-030-90888-1_22](CCF C)
# 天大. OntoSP: Ontology-Based Semantic-Aware Partitioning on RDF Graphs. WISE 2021 [https://link.springer.com/chapter/10.1007/978-3-030-90888-1_21](CCF C)
# 人大. XTuning: Expert Database Tuning System Based on Reinforcement Learning. WISE 2021 [https://link.springer.com/chapter/10.1007/978-3-030-90888-1_8](CCF C)
# 天大. PAIRPQ: An Efficient Path Index for Regular Path Queries on Knowledge Graphs . APWeb-WAIM 2021 [https://link.springer.com/chapter/10.1007/978-3-030-85899-5_8](CCF C)
# 人大. Rethink the Linearizability Constraints of Raft for Distributed Key-Value Stores. ICDE 2021 [https://ieeexplore.ieee.org/abstract/document/9458806](CCF A)
# 天大. UniKG：A Unified Interoperable Knowledge Graph Database System. ICDE 2021 [https://ieeexplore.ieee.org/abstract/document/9458632](CCF A)
# 天大. Deep Attributed Network Representation Learning of Complex Coupling and Interaction. KBS 2021 [https://www.sciencedirect.com/science/article/pii/S0950705120307474](SCI 1区)
# 人大. WATuning: A Workload-Aware Tuning System with Attention-Based Deep Reinforcement Learning. JCST 2021 [https://link.springer.com/content/pdf/10.1007/s11390-021-1350-8.pdf](SCI 3区 / CCF)
# 天大. 知识图谱划分算法研究综述. 计算机学报 2021 [http://cjc.ict.ac.cn/online/cre/wx-202049111757.pdf](CCF A 中文)
# 天大. KGDB：统一模型和语言的知识图谱数据库管理系统. 软件学报 2021 [https://www.ccf.org.cn/ccfdl/ccf_dl_focus/Computer_Research/volume5/zllb3/2022-04-15/760763.shtml](CCF A 中文)
# 天大. 开放领域知识图谱问答研究综述. 计算机科学与探索 2021 [http://fcst.ceaj.org/CN/10.3778/j.issn.1673-9418.2106095](CCF B 中文)
# 天大. HET-KG：Communication-Efficient Knowledge Graph Embedding Training via Hotness-Aware Cache. ICDE 2022 [https://ieeexplore.ieee.org/abstract/document/9835364](CCF A)
# 人大. R2B: High-Efficiency and Fair I/O Allocation for Multiple tenants with Differentiated Demands. DAC 2022 (nan)
# 天大. OntoCA: Ontology-Aware Caching for Distributed SPARQL Query. APWeb-WAIM 2022 (CCF C)
# 人大. LightGC. MidddleWare 2022 (CCF B)
# 天大. KGVQL：A knowledge graph visual query language with bidirectional transformations. KBS 2022 [https://www.sciencedirect.com/science/article/pii/S0950705122004154](SCI 1区)
# 香港城大. Knowledge-enhanced attentive learning for answer selection in community question answering systems.pdf. KBS 2022 [https://www.sciencedirect.com/science/article/pii/S0950705122005512?casa_token=mzB9sYT8vVMAAAAA:vMr8EBQrrrExh5NWiSZKfeBbpBicof0TYjVOn3-l3FmCDWHAsFH9I14nFTWTJo-ziE9F80mOSa5f](SCI 1区)
# 香港城大. Locally weighted factorization machine with fuzzy partition for elderly readmission prediction. KBS 2022 [https://www.sciencedirect.com/science/article/pii/S0950705122001186?casa_token=QkB9N6u72PcAAAAA:sRvZd1kLy8elHutqQCn0Nzglfv5uKVxDqrKgVXYxd82NnvWyUNQTOHAEERyPCAoHffjblSUDY1ON](SCI 1区)
# 天大. Optimizing subgraph matching over distributed knowledge graphs using partial evaluation. WWWJ 2022 [https://link.springer.com/article/10.1007/s11280-022-01075-6](SCI / CCF B)
# 天大. FPIRPQ: Accelerating Regular Path Queries on Knowledge Graphs. WWWJ 2022 (SCI / CCF B)
# 人大. MacroTrend. JCST 2022 (SCI / CCF B)
# 天大. DB4Trans：数据库内置知识图谱嵌入模型训练引擎. 计算机学报 2022 (CCF A 中文)
# 天大. 新一代知识图谱关键技术综述. 计算机研究与发展 2022 [https://kns.cnki.net/kcms/detail/detail.aspx?dbcode=CAPJ&dbname=CAPJLAST&filename=JFYZ20220228000&uniplatform=NZKPT&v=SfE2u2pSsAhf08VDv2pM0iRxKFEzcCxDax50D8XzLgfHAK4BdmA9SHsJAsg6xEmI](CCF A 中文)
# 香港城大. Mass Screening for Low Bone Density Using Basic Check-Up Items. IEEE TCSS 2022 (SCI)
# 香港城大. Field-aware attentive neural factorization with fuzzy mutual information for company investment valuation. Information Sciences 2022 (SCI)
# 人大. Auto-tuning; Database optimization; Correlation expert rules; reinforcement learning; Training time reduction. BDR 2022 (SCI)

=== 专利 ===
# 基于分布式键值库的大规模知识图谱存储方案, 2021, 受理, 天大
# 基于关键嵌入的知识图谱表示学习框架优化方法, 2021, 受理, 天大
# 双曲空间中区分数据类型的知识图谱表示学习方法, 2021, 受理, 天大
# 一种用于知识超图的可解释性链接预测方法, 2021, 受理, 天大

=== 软著 ===

== 技术 ==

=== 技术指标 ===
形成技术报告

=== 测试 ===
第三方测试报告

=== 数据汇交 ===
通过数据汇交网站，打电话问清楚我们这样的项目需要交什么

== 财务 ==
需要形成财务报告
=== 专家评定 ===
=== 测试 ===
测试费用任务书中是2万元
=== 审计 ===
建议找北京审计事务所，审计费用占项目总经费5‰，约合1.07万元

2019YFE0198600

2022-11-05T09:31:43Z

2021244229:

{{DISPLAYTITLE:分布式知识图谱数据管理关键技术与系统 (国家重点研发计划项目)}}
== 成果 ==
=== 论文 ===
# 天大. OntoCSM：Ontology-Aware Characteristic Set Merging for RDF Type Discovery. DASFAA 2021 [https://link.springer.com/chapter/10.1007/978-3-030-73194-6_22](CCF B)
# 人大. vRaft: Accelerating the Distributed Consensus Under Virtualized Environments. DASFAA 2021 (CCF B)
# 天大. Optimal Subgraph Matching Queries over Distributed Knowledge Graphs Based on Partial Evaluation. WISE 2021 [https://link.springer.com/chapter/10.1007/978-3-030-90888-1_22](CCF C)
# 天大. OntoSP: Ontology-Based Semantic-Aware Partitioning on RDF Graphs. WISE 2021 [https://link.springer.com/chapter/10.1007/978-3-030-90888-1_21](CCF C)
# 人大. XTuning: Expert Database Tuning System Based on Reinforcement Learning. WISE 2021 [https://link.springer.com/chapter/10.1007/978-3-030-90888-1_8](CCF C)
# 天大. PAIRPQ: An Efficient Path Index for Regular Path Queries on Knowledge Graphs . APWeb-WAIM 2021 [https://link.springer.com/chapter/10.1007/978-3-030-85899-5_8](CCF C)
# 人大. Rethink the Linearizability Constraints of Raft for Distributed Key-Value Stores. ICDE 2021 [https://ieeexplore.ieee.org/abstract/document/9458806](CCF A)
# 天大. UniKG：A Unified Interoperable Knowledge Graph Database System. ICDE 2021 [https://ieeexplore.ieee.org/abstract/document/9458632](CCF A)
# 天大. Deep Attributed Network Representation Learning of Complex Coupling and Interaction. KBS 2021 [https://www.sciencedirect.com/science/article/pii/S0950705120307474](SCI 1区)
# 人大. WATuning: A Workload-Aware Tuning System with Attention-Based Deep Reinforcement Learning. JCST 2021 [https://link.springer.com/content/pdf/10.1007/s11390-021-1350-8.pdf](SCI 3区 / CCF)
# 天大. 知识图谱划分算法研究综述. 计算机学报 2021 [http://cjc.ict.ac.cn/online/cre/wx-202049111757.pdf](CCF A 中文)
# 天大. KGDB：统一模型和语言的知识图谱数据库管理系统. 软件学报 2021 [https://www.ccf.org.cn/ccfdl/ccf_dl_focus/Computer_Research/volume5/zllb3/2022-04-15/760763.shtml](CCF A 中文)
# 天大. 开放领域知识图谱问答研究综述. 计算机科学与探索 2021 [http://fcst.ceaj.org/CN/10.3778/j.issn.1673-9418.2106095](CCF B 中文)
# 天大. HET-KG：Communication-Efficient Knowledge Graph Embedding Training via Hotness-Aware Cache. ICDE 2022 [https://ieeexplore.ieee.org/abstract/document/9835364](CCF A)
# 人大. R2B: High-Efficiency and Fair I/O Allocation for Multiple tenants with Differentiated Demands. DAC 2022 (nan)
# 天大. OntoCA: Ontology-Aware Caching for Distributed SPARQL Query. APWeb-WAIM 2022 (CCF C)
# 人大. LightGC. MidddleWare 2022 (CCF B)
# 天大. KGVQL：A knowledge graph visual query language with bidirectional transformations. KBS 2022 [https://www.sciencedirect.com/science/article/pii/S0950705122004154](SCI 1区)
# 香港城大. Knowledge-enhanced attentive learning for answer selection in community question answering systems.pdf. KBS 2022 [https://www.sciencedirect.com/science/article/pii/S0950705122005512?casa_token=mzB9sYT8vVMAAAAA:vMr8EBQrrrExh5NWiSZKfeBbpBicof0TYjVOn3-l3FmCDWHAsFH9I14nFTWTJo-ziE9F80mOSa5f](SCI 1区)
# 香港城大. Locally weighted factorization machine with fuzzy partition for elderly readmission prediction. KBS 2022 [https://www.sciencedirect.com/science/article/pii/S0950705122001186?casa_token=QkB9N6u72PcAAAAA:sRvZd1kLy8elHutqQCn0Nzglfv5uKVxDqrKgVXYxd82NnvWyUNQTOHAEERyPCAoHffjblSUDY1ON](SCI 1区)
# 天大. Optimizing subgraph matching over distributed knowledge graphs using partial evaluation. WWWJ 2022 [https://link.springer.com/article/10.1007/s11280-022-01075-6](SCI / CCF B)
# 天大. FPIRPQ: Accelerating Regular Path Queries on Knowledge Graphs. WWWJ 2022 (SCI / CCF B)
# 人大. MacroTrend. JCST 2022 (SCI / CCF B)
# 天大. DB4Trans：数据库内置知识图谱嵌入模型训练引擎. 计算机学报 2022 (CCF A 中文)
# 天大. 新一代知识图谱关键技术综述. 计算机研究与发展 2022 [https://kns.cnki.net/kcms/detail/detail.aspx?dbcode=CAPJ&dbname=CAPJLAST&filename=JFYZ20220228000&uniplatform=NZKPT&v=SfE2u2pSsAhf08VDv2pM0iRxKFEzcCxDax50D8XzLgfHAK4BdmA9SHsJAsg6xEmI](CCF A 中文)
# 香港城大. Mass Screening for Low Bone Density Using Basic Check-Up Items. IEEE TCSS 2022 (SCI)
# 香港城大. Field-aware attentive neural factorization with fuzzy mutual information for company investment valuation. Information Sciences 2022 (SCI)
# 人大. Auto-tuning; Database optimization; Correlation expert rules; reinforcement learning; Training time reduction. BDR 2022 (SCI)

=== 专利 ===

=== 软著 ===

== 技术 ==

=== 技术指标 ===
形成技术报告

=== 测试 ===
第三方测试报告

=== 数据汇交 ===
通过数据汇交网站，打电话问清楚我们这样的项目需要交什么

== 财务 ==
需要形成财务报告
=== 专家评定 ===
=== 测试 ===
测试费用任务书中是2万元
=== 审计 ===
建议找北京审计事务所，审计费用占项目总经费5‰，约合1.07万元

2019YFE0198600

2022-11-05T08:40:52Z

2021244229:

{{DISPLAYTITLE:分布式知识图谱数据管理关键技术与系统 (国家重点研发计划项目)}}
== 成果 ==
=== 论文 ===
# OntoCSM：Ontology-Aware Characteristic Set Merging for RDF Type Discovery [https://link.springer.com/chapter/10.1007/978-3-030-73194-6_22]
# vRaft: Accelerating the Distributed Consensus Under Virtualized Environments
# Optimal Subgraph Matching Queries over Distributed Knowledge Graphs Based on Partial Evaluation [https://link.springer.com/chapter/10.1007/978-3-030-90888-1_22]
# OntoSP: Ontology-Based Semantic-Aware Partitioning on RDF Graphs [https://link.springer.com/chapter/10.1007/978-3-030-90888-1_21]
# XTuning: Expert Database Tuning System Based on Reinforcement Learning [https://link.springer.com/chapter/10.1007/978-3-030-90888-1_8]
# PAIRPQ: An Efficient Path Index for Regular Path Queries on Knowledge Graphs [https://link.springer.com/chapter/10.1007/978-3-030-85899-5_8]
# Rethink the Linearizability Constraints of Raft for Distributed Key-Value Stores [https://ieeexplore.ieee.org/abstract/document/9458806]
# UniKG：A Unified Interoperable Knowledge Graph Database System [https://ieeexplore.ieee.org/abstract/document/9458632]
# Deep Attributed Network Representation Learning of Complex Coupling and Interaction [https://www.sciencedirect.com/science/article/pii/S0950705120307474]
# WATuning: A Workload-Aware Tuning System with Attention-Based Deep Reinforcement Learning [https://link.springer.com/content/pdf/10.1007/s11390-021-1350-8.pdf]
# 知识图谱划分算法研究综述 [http://cjc.ict.ac.cn/online/cre/wx-202049111757.pdf]
# KGDB：统一模型和语言的知识图谱数据库管理系统 [https://www.ccf.org.cn/ccfdl/ccf_dl_focus/Computer_Research/volume5/zllb3/2022-04-15/760763.shtml]
# 开放领域知识图谱问答研究综述 [http://fcst.ceaj.org/CN/10.3778/j.issn.1673-9418.2106095]
# HET-KG：Communication-Efficient Knowledge Graph Embedding Training via Hotness-Aware Cache [https://ieeexplore.ieee.org/abstract/document/9835364]
# R2B: High-Efficiency and Fair I/O Allocation for Multiple tenants with Differentiated Demands
# OntoCA: Ontology-Aware Caching for Distributed SPARQL Query
# LightGC
# KGVQL：A knowledge graph visual query language with bidirectional transformations [https://www.sciencedirect.com/science/article/pii/S0950705122004154]
# Knowledge-enhanced attentive learning for answer selection in community question answering systems.pdf [https://www.sciencedirect.com/science/article/pii/S0950705122005512?casa_token=mzB9sYT8vVMAAAAA:vMr8EBQrrrExh5NWiSZKfeBbpBicof0TYjVOn3-l3FmCDWHAsFH9I14nFTWTJo-ziE9F80mOSa5f]
# Locally weighted factorization machine with fuzzy partition for elderly readmission prediction [https://www.sciencedirect.com/science/article/pii/S0950705122001186?casa_token=QkB9N6u72PcAAAAA:sRvZd1kLy8elHutqQCn0Nzglfv5uKVxDqrKgVXYxd82NnvWyUNQTOHAEERyPCAoHffjblSUDY1ON]
# Optimizing subgraph matching over distributed knowledge graphs using partial evaluation [https://link.springer.com/article/10.1007/s11280-022-01075-6]
# FPIRPQ: Accelerating Regular Path Queries on Knowledge Graphs
# MacroTrend
# DB4Trans：数据库内置知识图谱嵌入模型训练引擎
# 新一代知识图谱关键技术综述 [https://kns.cnki.net/kcms/detail/detail.aspx?dbcode=CAPJ&dbname=CAPJLAST&filename=JFYZ20220228000&uniplatform=NZKPT&v=SfE2u2pSsAhf08VDv2pM0iRxKFEzcCxDax50D8XzLgfHAK4BdmA9SHsJAsg6xEmI]
# Mass Screening for Low Bone Density Using Basic Check-Up Items
# Field-aware attentive neural factorization with fuzzy mutual information for company investment valuation
# Auto-tuning; Database optimization; Correlation expert rules; reinforcement learning; Training time reduction

=== 专利 ===

=== 软著 ===

== 技术 ==

=== 技术指标 ===
形成技术报告

=== 测试 ===
第三方测试报告

=== 数据汇交 ===
通过数据汇交网站，打电话问清楚我们这样的项目需要交什么

== 财务 ==
需要形成财务报告
=== 专家评定 ===
=== 测试 ===
测试费用任务书中是2万元
=== 审计 ===
建议找北京审计事务所，审计费用占项目总经费5‰，约合1.07万元

2019YFE0198600

2022-11-05T08:39:02Z

2021244229:

{{DISPLAYTITLE:分布式知识图谱数据管理关键技术与系统 (国家重点研发计划项目)}}
== 成果 ==
=== 论文 ===
# OntoCSM：Ontology-Aware Characteristic Set Merging for RDF Type Discovery [https://link.springer.com/chapter/10.1007/978-3-030-73194-6_22]
# vRaft: Accelerating the Distributed Consensus Under Virtualized Environments
# Optimal Subgraph Matching Queries over Distributed Knowledge Graphs Based on Partial Evaluation [https://link.springer.com/chapter/10.1007/978-3-030-90888-1_22]
# OntoSP: Ontology-Based Semantic-Aware Partitioning on RDF Graphs [https://link.springer.com/chapter/10.1007/978-3-030-90888-1_21]
# XTuning: Expert Database Tuning System Based on Reinforcement Learning [https://link.springer.com/chapter/10.1007/978-3-030-90888-1_8]
# PAIRPQ: An Efficient Path Index for Regular Path Queries on Knowledge Graphs [https://link.springer.com/chapter/10.1007/978-3-030-85899-5_8]
# Rethink the Linearizability Constraints of Raft for Distributed Key-Value Stores [https://ieeexplore.ieee.org/abstract/document/9458806]
# UniKG：A Unified Interoperable Knowledge Graph Database System [https://ieeexplore.ieee.org/abstract/document/9458632]
# Deep Attributed Network Representation Learning of Complex Coupling and Interaction [https://www.sciencedirect.com/science/article/pii/S0950705120307474]
# WATuning: A Workload-Aware Tuning System with Attention-Based Deep Reinforcement Learning [https://link.springer.com/content/pdf/10.1007/s11390-021-1350-8.pdf]
# 知识图谱划分算法研究综述
# KGDB：统一模型和语言的知识图谱数据库管理系统 [https://www.ccf.org.cn/ccfdl/ccf_dl_focus/Computer_Research/volume5/zllb3/2022-04-15/760763.shtml]
# 开放领域知识图谱问答研究综述
# HET-KG：Communication-Efficient Knowledge Graph Embedding Training via Hotness-Aware Cache [https://ieeexplore.ieee.org/abstract/document/9835364]
# R2B: High-Efficiency and Fair I/O Allocation for Multiple tenants with Differentiated Demands
# OntoCA: Ontology-Aware Caching for Distributed SPARQL Query
# LightGC
# KGVQL：A knowledge graph visual query language with bidirectional transformations [https://www.sciencedirect.com/science/article/pii/S0950705122004154]
# Knowledge-enhanced attentive learning for answer selection in community question answering systems.pdf [https://www.sciencedirect.com/science/article/pii/S0950705122005512?casa_token=mzB9sYT8vVMAAAAA:vMr8EBQrrrExh5NWiSZKfeBbpBicof0TYjVOn3-l3FmCDWHAsFH9I14nFTWTJo-ziE9F80mOSa5f]
# Locally weighted factorization machine with fuzzy partition for elderly readmission prediction [https://www.sciencedirect.com/science/article/pii/S0950705122001186?casa_token=QkB9N6u72PcAAAAA:sRvZd1kLy8elHutqQCn0Nzglfv5uKVxDqrKgVXYxd82NnvWyUNQTOHAEERyPCAoHffjblSUDY1ON]
# Optimizing subgraph matching over distributed knowledge graphs using partial evaluation [https://link.springer.com/article/10.1007/s11280-022-01075-6]
# FPIRPQ: Accelerating Regular Path Queries on Knowledge Graphs
# MacroTrend
# DB4Trans：数据库内置知识图谱嵌入模型训练引擎
# 新一代知识图谱关键技术综述 [https://kns.cnki.net/kcms/detail/detail.aspx?dbcode=CAPJ&dbname=CAPJLAST&filename=JFYZ20220228000&uniplatform=NZKPT&v=SfE2u2pSsAhf08VDv2pM0iRxKFEzcCxDax50D8XzLgfHAK4BdmA9SHsJAsg6xEmI]
# Mass Screening for Low Bone Density Using Basic Check-Up Items
# Field-aware attentive neural factorization with fuzzy mutual information for company investment valuation
# Auto-tuning; Database optimization; Correlation expert rules; reinforcement learning;
Training time reduction

=== 专利 ===

=== 软著 ===

== 技术 ==

=== 技术指标 ===
形成技术报告

=== 测试 ===
第三方测试报告

=== 数据汇交 ===
通过数据汇交网站，打电话问清楚我们这样的项目需要交什么

== 财务 ==
需要形成财务报告
=== 专家评定 ===
=== 测试 ===
测试费用任务书中是2万元
=== 审计 ===
建议找北京审计事务所，审计费用占项目总经费5‰，约合1.07万元

Projects

2022-11-03T13:00:39Z

2021244229:

{{DISPLAYTITLE:科研项目}}

[[2019YFE0198600 | 分布式知识图谱数据管理关键技术与系统 (国家重点研发计划项目)]]

[[61972275 | 大规模知识图谱分布式存储与查询关键技术研究 (国家自然科学基金面上项目)]]

[[CCF-HuaweiDBIR2019004B | 统一模型和语言的知识图谱数据库管理系统研发 (CCF-华为数据库创新研究计划项目)]]

[[2020AAA0108500 | 亿级节点时序图谱实时智能分析关键技术与系统 (科技创新2030“新一代人工智能”重大项目)]]

Projects

2022-11-03T13:00:21Z

2021244229:

{{DISPLAYTITLE:科研项目}}

[[projects/2019YFE0198600 | 分布式知识图谱数据管理关键技术与系统 (国家重点研发计划项目)]]

[[61972275 | 大规模知识图谱分布式存储与查询关键技术研究 (国家自然科学基金面上项目)]]

[[CCF-HuaweiDBIR2019004B | 统一模型和语言的知识图谱数据库管理系统研发 (CCF-华为数据库创新研究计划项目)]]

[[2020AAA0108500 | 亿级节点时序图谱实时智能分析关键技术与系统 (科技创新2030“新一代人工智能”重大项目)]]

Conferences

2022-09-29T02:33:52Z

2021244229: /* 经费报销 */

{{DISPLAYTITLE:科学研究}}
</big>
== 重要时间 ==

{| class="wikitable"
|-
! CCF !! Conference !! Abstract !! Full Paper !! Short Paper !! Demo !! Poster !! Link
|-
| A || SIGMOD-22 || || 2021.9.15 || 2021.9.15 || || || https://2022.sigmod.org/
|-
| A || VLDB-22 || || 20th of each month || || || || https://vldb.org/2022/
|-
| A || ICDE-23 || || 2022.7.8 / 2022.10.8 || || || || https://icde2023.ics.uci.edu/research-papers-track/
|-
| A || WWW-22 || 2021.10.14 || 2021.10.21 || || 2022.02.03 || 2022.02.03 || https://www2022.thewebconf.org/
|-
| A || IJCAI-22 || TBD || || || || || https://ijcai-22.org/
|-
| A || AAAI-22 || 2021.8.30 || 2021.9.8 || || || || https://aaai.org/Conferences/AAAI-22/
|-
| A || KDD-22 || TBD || || || || ||
|-
| A || SIGIR-22 || TBD || || || || || https://sigir.org/sigir2022/
|-
| A || ACL-22 || || 2022.1.7 || 2022.1.7 || || || https://2022.aclweb.org
|-
| A || NeurIPS-22 ||TBD || || || || ||
|-
| A || ICML-22 || TBD || || || || ||
|-
| B || EMNLP-22 || TBD || || || || ||
|-
| B || CIKM-22 || 2022.5.9 || 2022.5.16 || || || || https://www.cikm2022.org/calls
|-
| B || EDBT-22 || || 2021.10.1 || 2021.10.1|| || || https://conferences.inf.ed.ac.uk/edbticdt2022/
|-
| B || ISWC-22 || 2022.4.28 || 2022.5.5 || || 2022.7.13 || 2022.7.13 || https://iswc2022.semanticweb.org/index.php/
|-
| B || DASFAA-22 || 2021.10.28 || 2021.11.4 || 2021.11.4 || 2021.11.28 || || https://www.dasfaa2022.org/
|-
| C || WISE-22 || 2022.3.1 || 2022.3.8 || || || || https://wise2022.sigappfr.org/
|}

== 学术写作 ==
[[ Writing | 学术写作指南 ]]

== 经费报销 ==
[[ reimbursement | 报销指南 ]]

Conferences

2022-09-29T02:33:04Z

2021244229:

CRPQ

2022-04-09T06:40:04Z

2021244229: /* RPQ */

== 综述 ==
# Ali W, Saleem M, Yao B, et al. A survey of RDF stores & SPARQL engines for querying knowledge graphs[J]. The VLDB Journal, 2021: 1-26.
# 王鑫, 邹磊, 王朝坤, 彭鹏, 冯志勇. 知识图谱数据管理研究综述. 软件学报, 30(7):2139-2174, 2019. [http://jos.org.cn/html/2019/7/5841.htm]
# Besta M, Peter E, Gerstenberger R, et al. Demystifying graph databases: Analysis and taxonomy of data organization, system designs, and graph queries[J]. arXiv preprint arXiv:1910.09017, 2019. [https://arxiv.org/abs/1910.09017]
# Wylot, Marcin, et al. "RDF Data Storage and Query Processing Schemes: A Survey." ACM Computing Surveys (CSUR) 51(4):84, 2018.[https://dl.acm.org/doi/abs/10.1145/3177850]

== RPQ ==
# Arroyuelo D, Hogan A, Navarro G, et al. Time-and Space-Efficient Regular Path Queries on Graphs[J]. arXiv preprint arXiv:2111.04556, 2021. [https://arxiv.org/pdf/2111.04556.pdf]
# Casel K, Schmid M L. Fine-Grained Complexity of Regular Path Queries[J]. arXiv preprint arXiv:2101.01945, 2021. [https://arxiv.org/pdf/2101.01945.pdf]
# Guo X, Gao H, Zou Z. Distributed processing of regular path queries in RDF graphs[J]. Knowledge and Information Systems, 2021, 63(4): 993-1027. [https://link.springer.com/article/10.1007/s10115-020-01536-2]
# Regular Path Query Evaluation on Streaming Graphs [https://cs.uwaterloo.ca/~apacaci/papers/sigmod2020.pdf]
# Martens W, Trautner T. Dichotomies for evaluating simple regular path queries[J]. ACM Transactions on Database Systems (TODS), 2019, 44(4): 1-46. [https://dl.acm.org/doi/10.1145/3331446]
# Grahne G, Thomo A. Provenance for Regular Path Queries[J]. arXiv preprint arXiv:2001.09864, 2020. [https://arxiv.org/pdf/2001.09864.pdf]
# Mendelzon A O, Wood P T. Finding regular simple paths in graph databases[J]. SIAM Journal on Computing, 1995, 24(6): 1235-1258. [https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.96.9492&rep=rep1&type=pdf]
# Isabel F. Cruz, Alberto O. Mendelzon, Peter T. Wood: G+: Recursive Queries Without Recursion. Expert Database Conf. 1988: 645-666 [https://dblp.org/db/conf/eds/eds88.html#CruzMW88]
# Cruz I F, Mendelzon A O, Wood P T. A graphical query language supporting recursion[J]. ACM SIGMOD Record, 1987, 16(3): 323-330. [https://dl.acm.org/doi/abs/10.1145/38714.38749?casa_token=YjRGjPP3oSsAAAAA:j_UgeWBy0fbnssow1haatx1j1ijvendVHguQJUch7Nlia_snX1ox3Tb03TUiMK-ujzxp_2iPVK48]

== CRPQ ==
# Barceló P, Libkin L, Lin A W, et al. Expressive languages for path queries over graph-structured data[J]. ACM Transactions on Database Systems (TODS), 2012, 37(4): 1-46. [https://dl.acm.org/doi/10.1145/2389241.2389250]

== Others ==
# Shemetova E, Azimov R, Orachev E, et al. One Algorithm to Evaluate Them All: Unified Linear Algebra Based Approach to Evaluate Both Regular and Context-Free Path Queries[J]. arXiv preprint arXiv:2103.14688, 2021. [https://arxiv.org/pdf/2103.14688.pdf]

CRPQ

2022-04-09T06:37:47Z

2021244229: /* 综述 */

== 综述 ==
# Ali W, Saleem M, Yao B, et al. A survey of RDF stores & SPARQL engines for querying knowledge graphs[J]. The VLDB Journal, 2021: 1-26.
# 王鑫, 邹磊, 王朝坤, 彭鹏, 冯志勇. 知识图谱数据管理研究综述. 软件学报, 30(7):2139-2174, 2019. [http://jos.org.cn/html/2019/7/5841.htm]
# Besta M, Peter E, Gerstenberger R, et al. Demystifying graph databases: Analysis and taxonomy of data organization, system designs, and graph queries[J]. arXiv preprint arXiv:1910.09017, 2019. [https://arxiv.org/abs/1910.09017]
# Wylot, Marcin, et al. "RDF Data Storage and Query Processing Schemes: A Survey." ACM Computing Surveys (CSUR) 51(4):84, 2018.[https://dl.acm.org/doi/abs/10.1145/3177850]

== RPQ ==
# Mendelzon A O, Wood P T. Finding regular simple paths in graph databases[J]. SIAM Journal on Computing, 1995, 24(6): 1235-1258. [https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.96.9492&rep=rep1&type=pdf]
# Isabel F. Cruz, Alberto O. Mendelzon, Peter T. Wood: G+: Recursive Queries Without Recursion. Expert Database Conf. 1988: 645-666 [https://dblp.org/db/conf/eds/eds88.html#CruzMW88]
# Cruz I F, Mendelzon A O, Wood P T. A graphical query language supporting recursion[J]. ACM SIGMOD Record, 1987, 16(3): 323-330. [https://dl.acm.org/doi/abs/10.1145/38714.38749?casa_token=YjRGjPP3oSsAAAAA:j_UgeWBy0fbnssow1haatx1j1ijvendVHguQJUch7Nlia_snX1ox3Tb03TUiMK-ujzxp_2iPVK48]
# Arroyuelo D, Hogan A, Navarro G, et al. Time-and Space-Efficient Regular Path Queries on Graphs[J]. arXiv preprint arXiv:2111.04556, 2021. [https://arxiv.org/pdf/2111.04556.pdf]
# Casel K, Schmid M L. Fine-Grained Complexity of Regular Path Queries[J]. arXiv preprint arXiv:2101.01945, 2021. [https://arxiv.org/pdf/2101.01945.pdf]
# Grahne G, Thomo A. Provenance for Regular Path Queries[J]. arXiv preprint arXiv:2001.09864, 2020. [https://arxiv.org/pdf/2001.09864.pdf]
# Regular Path Query Evaluation on Streaming Graphs [https://cs.uwaterloo.ca/~apacaci/papers/sigmod2020.pdf]
# Guo X, Gao H, Zou Z. Distributed processing of regular path queries in RDF graphs[J]. Knowledge and Information Systems, 2021, 63(4): 993-1027. [https://link.springer.com/article/10.1007/s10115-020-01536-2]
# Martens W, Trautner T. Dichotomies for evaluating simple regular path queries[J]. ACM Transactions on Database Systems (TODS), 2019, 44(4): 1-46. [https://dl.acm.org/doi/10.1145/3331446]

== CRPQ ==
# Barceló P, Libkin L, Lin A W, et al. Expressive languages for path queries over graph-structured data[J]. ACM Transactions on Database Systems (TODS), 2012, 37(4): 1-46. [https://dl.acm.org/doi/10.1145/2389241.2389250]

== Others ==
# Shemetova E, Azimov R, Orachev E, et al. One Algorithm to Evaluate Them All: Unified Linear Algebra Based Approach to Evaluate Both Regular and Context-Free Path Queries[J]. arXiv preprint arXiv:2103.14688, 2021. [https://arxiv.org/pdf/2103.14688.pdf]

CRPQ

2022-04-09T06:36:42Z

2021244229: /* 综述 */

== 综述 ==
# 王鑫, 邹磊, 王朝坤, 彭鹏, 冯志勇. 知识图谱数据管理研究综述. 软件学报, 30(7):2139-2174, 2019. [http://jos.org.cn/html/2019/7/5841.htm]
# Besta M, Peter E, Gerstenberger R, et al. Demystifying graph databases: Analysis and taxonomy of data organization, system designs, and graph queries[J]. arXiv preprint arXiv:1910.09017, 2019. [https://arxiv.org/abs/1910.09017]
# Wylot, Marcin, et al. "RDF Data Storage and Query Processing Schemes: A Survey." ACM Computing Surveys (CSUR) 51(4):84, 2018.[https://dl.acm.org/doi/abs/10.1145/3177850]

== RPQ ==
# Mendelzon A O, Wood P T. Finding regular simple paths in graph databases[J]. SIAM Journal on Computing, 1995, 24(6): 1235-1258. [https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.96.9492&rep=rep1&type=pdf]
# Isabel F. Cruz, Alberto O. Mendelzon, Peter T. Wood: G+: Recursive Queries Without Recursion. Expert Database Conf. 1988: 645-666 [https://dblp.org/db/conf/eds/eds88.html#CruzMW88]
# Cruz I F, Mendelzon A O, Wood P T. A graphical query language supporting recursion[J]. ACM SIGMOD Record, 1987, 16(3): 323-330. [https://dl.acm.org/doi/abs/10.1145/38714.38749?casa_token=YjRGjPP3oSsAAAAA:j_UgeWBy0fbnssow1haatx1j1ijvendVHguQJUch7Nlia_snX1ox3Tb03TUiMK-ujzxp_2iPVK48]
# Arroyuelo D, Hogan A, Navarro G, et al. Time-and Space-Efficient Regular Path Queries on Graphs[J]. arXiv preprint arXiv:2111.04556, 2021. [https://arxiv.org/pdf/2111.04556.pdf]
# Casel K, Schmid M L. Fine-Grained Complexity of Regular Path Queries[J]. arXiv preprint arXiv:2101.01945, 2021. [https://arxiv.org/pdf/2101.01945.pdf]
# Grahne G, Thomo A. Provenance for Regular Path Queries[J]. arXiv preprint arXiv:2001.09864, 2020. [https://arxiv.org/pdf/2001.09864.pdf]
# Regular Path Query Evaluation on Streaming Graphs [https://cs.uwaterloo.ca/~apacaci/papers/sigmod2020.pdf]
# Guo X, Gao H, Zou Z. Distributed processing of regular path queries in RDF graphs[J]. Knowledge and Information Systems, 2021, 63(4): 993-1027. [https://link.springer.com/article/10.1007/s10115-020-01536-2]
# Martens W, Trautner T. Dichotomies for evaluating simple regular path queries[J]. ACM Transactions on Database Systems (TODS), 2019, 44(4): 1-46. [https://dl.acm.org/doi/10.1145/3331446]

== CRPQ ==
# Barceló P, Libkin L, Lin A W, et al. Expressive languages for path queries over graph-structured data[J]. ACM Transactions on Database Systems (TODS), 2012, 37(4): 1-46. [https://dl.acm.org/doi/10.1145/2389241.2389250]

== Others ==
# Shemetova E, Azimov R, Orachev E, et al. One Algorithm to Evaluate Them All: Unified Linear Algebra Based Approach to Evaluate Both Regular and Context-Free Path Queries[J]. arXiv preprint arXiv:2103.14688, 2021. [https://arxiv.org/pdf/2103.14688.pdf]

秦雨舟

2022-04-09T06:33:46Z

2021244229:

天津大学智能与计算学部电子信息硕士研究生（全日制）

'''研究方向'''：图数据库、知识图谱数据管理

'''通讯地址'''：天津市津南区海河教育园区雅观路135号天津大学北洋园校区智能与计算学部

'''邮政编码'''：300354

'''办公地址'''：55教学楼B区324室

'''电子邮箱'''：yuzhou_qin@tju.edu.cn

== 简历 ==
2021年7月于东北大学获得软件工程学位

2021年9月至今天津大学智能与计算学部硕士研究生在读

秦雨舟

2022-04-09T06:32:14Z

2021244229: Created page with "天津大学智能与计算学部电子信息硕士研究生（全日制） '''研究方向'''：知识图谱表示学习、知识超图表示学习 '''通讯地址'''..."

天津大学智能与计算学部电子信息硕士研究生（全日制）

'''研究方向'''：知识图谱表示学习、知识超图表示学习

'''通讯地址'''：天津市津南区海河教育园区雅观路135号天津大学北洋园校区智能与计算学部

'''邮政编码'''：300354

'''办公地址'''：55教学楼B区324室

'''电子邮箱'''：cxwang1998@tju.edu.cn

== 简历 ==
2021年7月于华北电力大学获得（测控技术与仪器）学位

2021年9月至今天津大学智能与计算学部硕士研究生在读