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With the wide application of the Semantic Web and the rapid development of the Resource Description Framework (RDF), the demand for data processing of inconsistent or imprecise information has become more and more urgent. Recently, the fuzzy extension of RDF data has received widespread attention because of their ability to represent and process fuzzy information. An important issue for the success of fuzzy RDF applications is how to achieve persistent data storage and query capabilities. In this article, we investigate the storage and query of fuzzy RDF data. To accomplish this, we study how to formally map fuzzy RDF graph represented by the labeled directed graph structure to Property Graphs (PGs) database storage model. We implement the process that the mapping relationship between fuzzy RDF graph and property graph. Moreover, we manage these data by a chosen Neo4j Graph DBMS in order to support expressive querying services over the stored data. Finally, we have compared our method based on Neo4j graph database with the method based on relational database, and the experimental results prove that using the graph database model to store fuzzy RDF graph data sets is effective and feasibility. [ABSTRACT FROM AUTHOR]

Graph representation learning (graph embedding) has led to breakthrough results in various machine learning graph-based applications such as node classification, link prediction and recommendation. Many real-world graphs can be characterized as the property graphs, because besides the structure information, there exists rich property information related to each node in the graphs. Many existing graph representation learning methods—e.g. random walk-based methods like DeepWalk and Node2vec, focus only on the structure of graph for learning the node embedding. Although graph representation learning based on neural networks (e.g. typical GNN methods such as GraphSAGE) uses the property of nodes as the initial features of nodes and then aggregates feature information of the neighbours, their limitation is that the neighbourhood of a node is considered to be uniform—i.e. there is no way to differentiate among neighbours of a node when learning a node embedding. Additionally, their definition of neighbourhood is local, i.e. only nodes connected to the current node are considered as neighbours. Hence, those methods fail to capture implicit/latent relationships among nodes, which are implicit in the given structure. In this study, our aim is to improve the performance of graph representation learning methods on property graphs. We present a new framework called Enhanced Property Graph Embedding (EPGE)—a graph representation learning framework to address above-mentioned limitations. Our proposed framework relies on the notion of latent neighbourhood, as well as systematic sampling of neighbouring nodes to obtain better representation of the nodes. The experimental results on five publicly available graph datasets demonstrate that EPGE outperforms state-of-the-art baselines for the task of node classification. We further evaluate the superiority of our proposed formulation by defining a novel quantitative metric to measure the usefulness of the sampled neighbourhood in the graph. [ABSTRACT FROM AUTHOR]

The data produced by various services should be stored and managed in an appropriate format for gaining valuable knowledge conveniently. This leads to the emergence of various data models, including relational, semi-structured, and graph models, and so on. Considering the fact that the mature relational databases established on relational data models are still predominant in today’s market, it has fueled interest in storing and processing semi-structured data and graph data in relational databases so that mature and powerful relational databases’ capabilities can all be applied to these various data. In this survey, we review existing methods on mapping semi-structured data and graph data into relational tables, analyze their major features, and give a detailed classification of those methods. We also summarize the merits and demerits of each method, introduce open research challenges, and present future research directions. With this comprehensive investigation of existing methods and open problems, we hope this survey can motivate new mapping approaches through drawing lessons from each model’s mapping strategies, as well as a new research topic - mapping multi-model data into relational tables. [ABSTRACT FROM AUTHOR]

Graph databases are nowadays considered the most appropriate solution for highly connected domains. Nevertheless, the lack of a fixed schema perplexes the implementation of business rules and inhibits the usage of graph database technology in practical use cases. To tackle this challenge, we study cardinality constraints in graph databases, as their focus is on the essential component of the property graph data model - relationships between entities. This paper presents an abstract cardinality constraints model for enforcing cardinality constraints in graph databases, which represent complex graph patterns. We extend our initial k-vertex cardinality constraints model to allow the representation of cardinality constraints between a node and a subgraph with given node and/or edge properties. Second, we implement the proposed model as procedures deployed in the Neo4j Graph Database Management System (GDBMS) to prevent adding new edges that violate k-vertex cardinality constraints to the graph database. Finally, we study the performance of the implemented approach on synthetic and real datasets and analyze its performance compared to the initial model. Overall, the query execution time (QET) of the procedure increases exponentially on larger datasets. Still, the added node/edge property-level evaluation does not show a significant performance effect on the edge insertion process.

Graph databases are nowadays considered the most appropriate solution for highly connected domains. Nevertheless, the lack of a fixed schema perplexes the implementation of business rules and inhibits the usage of graph database technology in practical use cases. To tackle this challenge, we study cardinality constraints in graph databases, as their focus is on the essential component of the property graph data model - relationships between entities. This paper presents an abstract cardinality constraints model for enforcing cardinality constraints in graph databases, which represent complex graph patterns. We extend our initial k -vertex cardinality constraints model to allow the representation of cardinality constraints between a node and a subgraph with given node and/or edge properties. Second, we implement the proposed model as procedures deployed in the Neo4j Graph Database Management System (GDBMS) to prevent adding new edges that violate k -vertex cardinality constraints to the graph database. Finally, we study the performance of the implemented approach on synthetic and real datasets and analyze its performance compared to the initial model. Overall, the query execution time (QET) of the procedure increases exponentially on larger datasets. Still, the added node/edge property-level evaluation does not show a significant performance effect on the edge insertion process. [ABSTRACT FROM AUTHOR]

With the rapid development of cloud computing, knowledge graph data outsourcing has become a popular trend. Knowledge graphs in many fields such as medical and finance have privacy-sensitive characteristics. However, cloud servers are not completely credible. In order to protect the confidentiality and integrity of data on cloud servers, encryption and other methods are used to protect the security of knowledge graph data. This paper proposes an encrypted knowledge graph storage and retrieval scheme based on searchable encryption, which can effectively protect the confidentiality and integrity of data and support retrieval on encrypted data. This scheme fully considers the necessity of sequential reading of knowledge graph entities and their relationships, thereby optimizing the encrypted index design and speeding up the retrieval efficiency. The experimental results show that the average query time of the one-hop subgraph of the encrypted knowledge graph is 2.09 times that of the non-encrypted knowledge graph, which verifies that the scheme achieves a good balance between security and query efficiency. [ABSTRACT FROM AUTHOR]

Knowledge graph has wide applications in the field of computer science. In the knowledge service environment, the information is large and explosive, and it is difficult to find knowledge of common phenomena. The urban traffic knowledge graph is a knowledge system that formally describes urban traffic concepts, entities and their interrelationships. It has great application potential in application scenarios such as user travel, route planning, and urban planning. This paper first defines the urban traffic knowledge graph and the star subgraph query of the urban traffic knowledge graph. Then, the road network data and trajectory data are collected to extract the urban traffic knowledge, and the urban traffic knowledge graph is constructed with this knowledge. Finally, a star subgraph query algorithm on the urban traffic knowledge graph is proposed. The discussion of the star subgraph query mode gives the corresponding application scenarios of our method in the urban traffic knowledge graph. Experimental results verify the performance advantages of this method. [ABSTRACT FROM AUTHOR]

• Graph partitioning is a challenging task for the massive graphs in distributed computing. • Both the size and the volume of the graph blow up over time. • Existing partitioning methods do not consider the volume of graph data. • We propose a graph partitioning algorithm based on the volume metric. • Our proposed algorithm produces balanced distribution in size and volume in less partitioning time. The graph partitioning challenge is well known and ongoing classical problem. Many heuristic methods tried to propose solutions focusing mainly on load processing and cost-efficiency. With the emergency of big data technology, the graph partitioning challenge became even more demanding, as an imminent need to handle big volume of data in real time. This reveals a new challenge as most of the existing studies does not consider the volume metric with their streaming graph algorithms causing imbalanced workloads and graph storage. With this article, we propose a specific lightweight algorithm which we called "Hammer" Algorithm. Our proposed Hammer algorithm is a streaming graph based on volume metric to ensure optimal load processing and communication cost efficiency. Our proof of concept was done on real world dataset and the Hammer algorithm showed considerable performance against some existing graph partitioning algorithms. [ABSTRACT FROM AUTHOR]

RDF triplestores and property graph databases are two approaches for data management which are based on modeling, storing and querying graph-like data. In spite of such common principle, they present special features that complicate the task of database interoperability. While there exist some methods to transform RDF graphs into property graphs, and vice versa, they lack compatibility and a solid formal foundation. This paper presents three direct mappings (schema-dependent and schema-independent) for transforming an RDF database into a property graph database, including data and schema. We show that two of the proposed mappings satisfy the properties of semantics preservation and information preservation. The existence of both mappings allows us to conclude that the property graph data model subsumes the information capacity of the RDF data model.

The scale of knowledge is growing rapidly in the big data environment, and traditional knowledge organization and services have faced the dilemma of semantic inaccuracy and untimeliness. From a knowledge fusion perspective--combining the precise semantic superiority of traditional ontology with the large-scale graph processing power and the predicate attribute expression ability of property graph--this paper presents an ontology and property graph fusion framework (OPGFF). The fusion process is divided into content layer fusion and constraint layer fusion. The result of the fusion, that is, the knowledge representation model is called knowledge big graph. In addition, this paper applies the knowledge big graph model to the ownership network in the China's financial field and builds a financial ownership knowledge big graph. Furthermore, this paper designs and implements six consistency inference algorithms for finding contradictory data and filling in missing data in the financial ownership knowledge big graph, five of which are completely domain agnostic. The correctness and validity of the algorithms have been experimentally verified with actual data. The fusion OPGFF framework and the implementation method of the knowledge big graph could provide technical reference for big data knowledge organization and services. [ABSTRACT FROM AUTHOR]

The General Data Protection Regulation (GDPR) was created for regulating how organizations that collect personal data process and protect it. In cases of digital handling of personal data, GDPR compliance must be proven by analyzing the actions that a system applies in order to gather, process and safeguard the data. We advocate that compliance must be considered in the design phase of the system, by analyzing the dependencies between system entities (e.g. personal data, users etc.) and the processes enacted upon them. Then, it is possible to generate a series of data reports that can be assessed by regulators who inspect the system for GDPR compliance. However, there can not be a universal methodology that covers all application domains and systems. To show proof of concept, we apply the methodology to a remote patient monitoring service that runs in the cloud. [ABSTRACT FROM AUTHOR]

During the last decades, there have been significant changes in science that have provoked a big increase in the number of articles published every year. This increment implies a new difficulty for scientists, who have to do an extra effort for selecting literature relevant for their activity. In this work, we present a pipeline for the generation of scientific literature knowledge graphs in the agriculture domain. The pipeline combines Semantic Web and natural language processing technologies, which make data understandable by computer agents, empowering the development of final user applications for literature searches. This workflow consists of (1) RDF generation, including metadata and contents; (2) semantic annotation of the content; and (3) property graph population by adding domain knowledge from ontologies, in addition to the previously generated RDF data describing the articles. This pipeline was applied to a set of 127 agriculture articles, generating a knowledge graph implemented in Neo4j, publicly available on Docker. The potential of our model is illustrated through a series of queries and use cases, which not only include queries about authors or references but also deal with article similarity or clustering based on semantic annotation, which is facilitated by the inclusion of domain ontologies in the graph. [ABSTRACT FROM AUTHOR]

RDF databases and graph databases are two approaches of data management which are based on modeling, storing and querying data following a graph structure. RDF databases are based on a single graph data model which allows to describe Web resources in terms of their relations and attributes. On the other hand, most graph databases are based on the property graph data model, a type of graph where nodes and edges can contain properties represented as key-value pairs. This paper presents two methods for transforming RDF data into property graphs. The first method defines schema and data transformations as it assumes the existence of an RDF schema. The second method is schema-independent, so it allows to transform any kind of RDF dataset. Both methods are useful to store RDF data into a Graph Data Management System. [ABSTRACT FROM AUTHOR]

The designation of the cluster number K and the initial centroids is essential for K-modes clustering algorithm. However, most of the improved methods based on K-modes specify the K value manually and generate the initial centroids randomly, which makes the clustering algorithm significantly dependent on human-based decisions and unstable on the iteration time. To overcome this limitation, we propose a cohesive K-modes (CK-modes) algorithm to generate the cluster number K and the initial centroids automatically. Explicitly, we construct a labeled property graph based on index-free adjacency to capture both global and local cohesion of the node in the sample of the input datasets. The cohesive node calculated based on the property similarity is exploited to split the graph to a K-node tree that determines the K value, and then the initial centroids are selected from the split subtrees. Since the property graph construction and the cohesion calculation are only performed once, they account for a small amount of execution time of the clustering operation with multiple iterations, but significantly accelerate the clustering convergence. Experimental validation in both real-world and synthetic datasets shows that the CK-modes algorithm outperforms the state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]