Proceedings of the 5th ACM SIGMOD Joint International Workshop on Graph Data Management Experiences & Systems (GRADES) and Network Data Analytics (NDA)最新文献

Multilayer graphs: a unified data model for graph databases 多层图:图数据库的统一数据模型

Proceedings of the 5th ACM SIGMOD Joint International Workshop on Graph Data Management Experiences & Systems (GRADES) and Network Data Analytics (NDA) Pub Date : 2022-06-12 DOI: 10.1145/3534540.3534696

Renzo Angles, A. Hogan, O. Lassila, Carlos Rojas, D. Schwabe, Pedro A. Szekely, D. Vrgoc

引用次数: 7

Efficient provenance-aware querying of graph databases with datalog 基于datalog的图形数据库的高效溯源查询

Proceedings of the 5th ACM SIGMOD Joint International Workshop on Graph Data Management Experiences & Systems (GRADES) and Network Data Analytics (NDA) Pub Date : 2022-06-12 DOI: 10.1145/3534540.3534689

Yann Ramusat, S. Maniu, P. Senellart

引用次数: 2

Flexible application-aware approximation for modern distributed graph processing frameworks 现代分布式图处理框架的灵活的应用感知近似

Proceedings of the 5th ACM SIGMOD Joint International Workshop on Graph Data Management Experiences & Systems (GRADES) and Network Data Analytics (NDA) Pub Date : 2022-06-12 DOI: 10.1145/3534540.3534693

Michael Schramm, Sukanya Bhowmik, K. Rothermel

{"title":"Flexible application-aware approximation for modern distributed graph processing frameworks","authors":"Michael Schramm, Sukanya Bhowmik, K. Rothermel","doi":"10.1145/3534540.3534693","DOIUrl":"https://doi.org/10.1145/3534540.3534693","url":null,"abstract":"The interest in the ability of processing data that has an underlying graph structure has grown in the recent past. This has led to the development of many distributed graph processing systems. However, due to rapidly growing amount of data, e.g., web graphs and social graphs, even such distributed graph processing frameworks end up requiring several minutes or even several hours to execute popular graph algorithms. This leads to the question: do we always need to know the exact answer for a large graph? The aforementioned modern distributed graph processing frameworks execute graph algorithms by exchanging messages between vertices. This paper introduces a novel message-dropping approach for approximation in these frameworks. As dropping messages would result in degradation of quality of result, our objective is to drop messages that have least adverse impact on quality. More specifically, we propose an application-aware approach that dynamically drops messages at runtime. We evaluate the effects of our approach for the PageRank algorithm on several representative real-world web graphs and compare its performance to that of existing approximation techniques for modern frameworks..","PeriodicalId":309669,"journal":{"name":"Proceedings of the 5th ACM SIGMOD Joint International Workshop on Graph Data Management Experiences & Systems (GRADES) and Network Data Analytics (NDA)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133758868","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Knowledge graph representation learning and graph neural networks for language understanding 知识图表示学习和用于语言理解的图神经网络

Proceedings of the 5th ACM SIGMOD Joint International Workshop on Graph Data Management Experiences & Systems (GRADES) and Network Data Analytics (NDA) Pub Date : 2022-06-12 DOI: 10.1145/3534540.3534710

Jing Huang

{"title":"Knowledge graph representation learning and graph neural networks for language understanding","authors":"Jing Huang","doi":"10.1145/3534540.3534710","DOIUrl":"https://doi.org/10.1145/3534540.3534710","url":null,"abstract":"As AI technologies become mature in natural language processing, speech recognition and computer vision, \"intelligent\" user interfaces emerge to handle complex and diverse tasks that require human-like knowledge and reasoning capability. In Part 1, I will present our recent work on knowledge graph representation learning using Graph Neural Networks (GNNs): the first approach is called orthogonal transform embedding (OTE), which integrates graph context into the embedding distance scoring function and improves prediction accuracy on complex relations such as the difficult N-to-1, 1-to-N and N-to-N cases; the second approach is called multi-hop attention GNN (MAGNA), a principled way to incorporate multi-hop context information into every layer of attention computation. MAGNA uses a diffusion prior on attention values, to efficiently account for all paths between the pair of disconnected nodes. Experimental results on knowledge graph completion as well as node classification benchmarks show that MAGNA achieves state-of-the-art results. In Part 2, I will present how we take advantage of GNNs for language understanding and reasoning tasks. We show that combined with large pre-trained language models and knowledge graph embeddings, GNNs are proven effective in multi-hop reading comprehension across documents, improving time sensitivity for question answering over temporal knowledge graphs, and constructing robust syntactic information for aspect-level sentiment analysis.","PeriodicalId":309669,"journal":{"name":"Proceedings of the 5th ACM SIGMOD Joint International Workshop on Graph Data Management Experiences & Systems (GRADES) and Network Data Analytics (NDA)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116228164","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Anti-vertex for neighborhood constraints in subgraph queries 子图查询中邻域约束的反顶点

Proceedings of the 5th ACM SIGMOD Joint International Workshop on Graph Data Management Experiences & Systems (GRADES) and Network Data Analytics (NDA) Pub Date : 2022-06-12 DOI: 10.1145/3534540.3534690

Kasra Jamshidi, Mugilan Mariappan, Keval Vora

{"title":"Anti-vertex for neighborhood constraints in subgraph queries","authors":"Kasra Jamshidi, Mugilan Mariappan, Keval Vora","doi":"10.1145/3534540.3534690","DOIUrl":"https://doi.org/10.1145/3534540.3534690","url":null,"abstract":"This paper focuses on subgraph queries where constraints are present in the neighborhood of the explored subgraphs. We describe anti-vertex, a declarative construct that indicates absence of a vertex, i.e., the resulting subgraph should not have a vertex in its specified neighborhood that matches the anti-vertex. We formalize the semantics of anti-vertex to benefit from automatic reasoning and optimization, and to enable standardized implementation across query languages and runtimes. The semantics are defined for various matching semantics that are commonly employed in subgraph querying (isomorphism, homomorphism, and no-repeated-edge matching) and for the widely adopted property graph model. We illustrate several examples where anti-vertices can be employed to help familiarize with the anti-vertex concept. We further showcase how anti-vertex support can be added in existing graph query languages by developing prototype extensions of Cypher language. Finally, we study how anti-vertices interact with the symmetry breaking technique in subgraph matching frameworks so that their meaning remains consistent with the expected outcome of constrained neighborhoods to connected vertices.","PeriodicalId":309669,"journal":{"name":"Proceedings of the 5th ACM SIGMOD Joint International Workshop on Graph Data Management Experiences & Systems (GRADES) and Network Data Analytics (NDA)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129528140","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Knowledge graph semantics 知识图语义

Proceedings of the 5th ACM SIGMOD Joint International Workshop on Graph Data Management Experiences & Systems (GRADES) and Network Data Analytics (NDA) Pub Date : 2022-06-12 DOI: 10.1145/3534540.3534709

J. Hendler

引用次数: 0

Converting property graphs to RDF: a preliminary study of the practical impact of different mappings 将属性图转换为RDF:对不同映射的实际影响的初步研究

Proceedings of the 5th ACM SIGMOD Joint International Workshop on Graph Data Management Experiences & Systems (GRADES) and Network Data Analytics (NDA) Pub Date : 2022-06-12 DOI: 10.1145/3534540.3534695

Shahrzad Khayatbashi, Sebastián Ferrada, O. Hartig

{"title":"Converting property graphs to RDF: a preliminary study of the practical impact of different mappings","authors":"Shahrzad Khayatbashi, Sebastián Ferrada, O. Hartig","doi":"10.1145/3534540.3534695","DOIUrl":"https://doi.org/10.1145/3534540.3534695","url":null,"abstract":"Today's space of graph database solutions is characterized by two main technology stacks that have evolved separate from one another: on one hand, there are systems that focus on supporting the RDF family of standards; on the other hand, there is the Property Graph category of systems. As a basis for bringing these stacks together and, in particular, to facilitate data exchange between the different types of systems, different direct mappings between the underlying graph data models have been introduced in the literature. While fundamental properties are well-documented for most of these mappings, the same cannot be said about the practical implications of choosing one mapping over another. Our research aims to contribute towards closing this gap. In this paper we report on a preliminary study for which we have selected two direct mappings from (Labeled) Property Graphs to RDF, where one of them uses features of the RDF-star extension to RDF. We compare these mappings in terms of the query performance achieved by two popular commercial RDF stores, GraphDB and Stardog, in which the converted data is imported. While we find that, for both of these systems, none of the mappings is a clear winner in terms of guaranteeing better query performance, we also identify types of queries that are problematic for the systems when using one mapping but not the other.","PeriodicalId":309669,"journal":{"name":"Proceedings of the 5th ACM SIGMOD Joint International Workshop on Graph Data Management Experiences & Systems (GRADES) and Network Data Analytics (NDA)","volume":"123 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123285651","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Batch dynamic algorithm to find k-core hierarchies 批处理动态算法查找k核层次结构

Proceedings of the 5th ACM SIGMOD Joint International Workshop on Graph Data Management Experiences & Systems (GRADES) and Network Data Analytics (NDA) Pub Date : 2022-06-12 DOI: 10.1145/3534540.3534694

Kasimir Gabert, Ali Pinar, Ümit V. Çatalyürek

引用次数: 0

Proceedings of the 5th ACM SIGMOD Joint International Workshop on Graph Data Management Experiences & Systems (GRADES) and Network Data Analytics (NDA) 第五届ACM SIGMOD图形数据管理经验与系统(等级)与网络数据分析(NDA)联合国际研讨会论文集

Proceedings of the 5th ACM SIGMOD Joint International Workshop on Graph Data Management Experiences & Systems (GRADES) and Network Data Analytics (NDA) Pub Date : 1900-01-01 DOI: 10.1145/3534540

引用次数: 0