Graph Database Research Articles

Research on a Unified Data Model for Power Grids and Communication Networks Based on Graph Databases

Research on a Unified Data Model for Power Grids and Communication Networks Based on Graph Databases

Research on Knowledge Graph Construction Methods for News Domain

With the rapid development of the Internet and artificial intelligence technology, massive text data is growing exponentially, and the news industry is generating massive text data every moment. Most of the existing knowledge graphs focus on tourism, healthcare, finance and other fields, while there are relatively few in the news field, which is not well constructed. Aiming at the above problems, this paper firstly devotes to the construction of ontology library in news domain, and precisely defines 8 types of entities and 9 types of relations, which lays a solid foundation for the construction of news knowledge graph. Subsequently, with the help of web crawler technology, we extensively collected news text and image data, and carried out rigorous knowledge cleaning, extraction and fusion processing on these data to ensure the accuracy and completeness of the data. Finally, with the help of Neo4j graph database, the effective storage of news knowledge is realised, and the news domain knowledge graph is successfully constructed. It provides new ideas and means for information mining and utilisation in the news industry, and also provides rich, high-quality data support for downstream applications, which is expected to promote the intelligent development of the news field.

Closing the gap between early and detailed ship design models

Conventionally, ship design and engineering are segregated activities, carried out with different software packages that thus each have their own place, qualities and tools. And consequently, a different data model. As a report on ongoing work to bridge that gap, this paper first explores existing neutral data models and standards employed or considered in maritime applications and concludes that none of these is directly applicable. It continues with describing the requirements and derived abstract data model of the SEUS project and its design and engineering applications. A graph database is identified as a potentially useful tool for SEUS data modelling, and a hands-on experiment confirms this presumption.

HUSM: High utility subgraph mining in single graph databases

Frequent subgraph mining (FSM) is a crucial research area with diverse applications. However, traditional FSM treats all subgraphs as equally important. In practical applications, some subgraphs may be frequent but not very valuable, while others may be infrequent but highly valuable. To address this issue, we introduce utility pattern mining into single graph mining, enabling the discovery of meaningful patterns based on user's interest rather than relying solely on support. We define a utility function in single graph databases and formally define the problem of high utility subgraph mining (HUSM). In the face of several challenges posed by this new problem, such as the absence of downward closure property and a large number of candidates, we design several utility upper bounds that satisfy the downward closure property. We then develop a HUSM algorithm to efficiently mine all high utility subgraphs in single graph databases. Additionally, we design a lossless concise representation for high utility subgraphs, which has fewer instances than the total number of high utility subgraphs and can derive all of them, making the mining results more representative. The experimental results demonstrate that our method exhibits excellent performance, and the concise representation plays a significant role in simplifying the mining results.

Recommender System in E-Commerce

In the realm of e-commerce, recommendation systems play a pivotal role in guiding users towards relevant products. However, existing systems often grapple with inefficiencies in handling large datasets and fail to deliver personalized recommendations tailored to individual preferences. Addressing these challenges, the study introduces an innovative approach leveraging graph databases to enhance the performance of e- commerce recommendation systems. Through comprehensive analysis, the study delves into four critical aspects: database comparison, user exploration frequency across product categories, the diversity of available category types, and user browsing history analysis. This investigation unveils Neo4j's superior efficiency over MySQL in managing extensive datasets, laying the groundwork for more robust recommendation engines. By scrutinizing user behaviour patterns, the recommender system predicts preferences with precision, promising a tailored and gratifying shopping experience for users. Moreover, with support for a diverse array of category types, users gain flexibility in exploring products based on varied criteria, addressing a crucial need in the market for personalized shopping experiences. Leveraging insights gleaned from user browsing history, the system delivers refined recommendations, poised to elevate user satisfaction and engagement within the competitive landscape of e- commerce. In conclusion, the study highlights the significance of recommendation systems in enhancing the e- commerce experience. By leveraging graph databases, particularly Neo4j, over traditional systems like MySQL, significant improvements in managing extensive datasets are demonstrated.

Differential privacy and SPARQL

Differential privacy is a framework that provides formal tools to develop algorithms to access databases and answer statistical queries with quantifiable accuracy and privacy guarantees. The notions of differential privacy are defined independently of the data model and the query language at steak. Most differential privacy results have been obtained on aggregation queries such as counting or finding maximum or average values, and on grouping queries over aggregations such as the creation of histograms. So far, the data model used by the framework research has typically been the relational model and the query language SQL. However, effective realizations of differential privacy for SQL queries that required joins had been limited. This has imposed severe restrictions on applying differential privacy in RDF knowledge graphs and SPARQL queries. By the simple nature of RDF data, most useful queries accessing RDF graphs will require intensive use of joins. Recently, new differential privacy techniques have been developed that can be applied to many types of joins in SQL with reasonable results. This opened the question of whether these new results carry over to RDF and SPARQL. In this paper we provide a positive answer to this question by presenting an algorithm that can answer counting queries over a large class of SPARQL queries that guarantees differential privacy, if the RDF graph is accompanied with semantic information about its structure. We have implemented our algorithm and conducted several experiments, showing the feasibility of our approach for large graph databases. Our aim has been to present an approach that can be used as a stepping stone towards extensions and other realizations of differential privacy for SPARQL and RDF.

Distinct Shortest Walk Enumeration for RPQs

We consider the Distinct Shortest Walks problem. Given two vertices s and t of a graph database D and a regular path query, we want to enumerate all walks of minimal length from s to t that carry a label that conforms to the query. Usual theoretical solutions turn out to be inefficient when applied to graph models that are closer to real-life systems, in particular because edges may carry multiple labels. Indeed, known algorithms may repeat the same answer exponentially many times. We propose an efficient algorithm for graph databases with multiple labels. The preprocessing runs in O(DxA) and the delay between two consecutive outputs is in O(λxA), where A is a nondeterministic automaton representing the query and L is the minimal length. The algorithm can handle epsilon-transitions in A or queries given as regular expressions at no additional cost.

Chinese text recognition and knowledge graph of Shen Nong Ben Cao Jing based on BERT pre-trained language models

Background: The research and utilization of ancient traditional Chinese medicine (TCM) books are relatively limited at present. With the rapid development of artificial intelligence (AI), knowledge graph related technology has brought light on this field. Objective: To construct the knowledge graph of Shen Nong Ben Cao Jing, analyze basic knowledge of materia medica, explore implicit knowledge, and conduct visualization display, as well to provide methodological references for the study of ancient TCM books. Methods: The types of knowledge entities and relationships between entities in Shen Nong Ben Cao Jing were analyzed. A training corpus dataset was produced by using the BIO sequence labeling method; a self-developed CNLP text labeling system was used for text labeling; the BERT model was used to recognize named entities; the relationships between entities were set based on rules and semantic associations; the data into the Neo4j-community 4.4.9 graph database was imported by using Cypher language for storage and visualization display after knowledge fusion; finally, a knowledge graph was constructed. Results: The knowledge graph of Shen Nong Ben Cao Jing included 5, 273 nodes and 11, 064 relationships. The schema layer contained 14 entity types and 15 relationship types. Through the query, knowledge can be visualized from the aspects of classification, property, and seven mutual relationships of herbal combination. Conclusion: The knowledge graph constructed in this study directly reflects the knowledge recorded in Shen Nong Ben Cao Jing and the relationship between them, which is suitable for knowledge mining and intuitive multi-dimensional display of ancient TCM books.

Cardinality estimation for property graph queries with gated learning approach on the graph database

Cardinality estimation for property graph queries with gated learning approach on the graph database

BF-BigGraph: An efficient subgraph isomorphism approach using machine learning for big graph databases

Graph databases are flexible NoSQL databases used to efficiently store and query complex and big data. One of the most difficult problems in graph databases is the problem of subgraph isomorphism, which involves finding a matching pattern in a given graph. Subgraph isomorphism algorithms generally encounter problems in the efficient processing of complex queries based on a lack of pruning methods and the use of a matching order. In this study, we present a new subgraph isomorphism approach based on the best-first search design strategy and name it BF-BigGraph. Our approach includes a machine learning technique to efficiently find the best matching order for various complex queries. The parameters we used in our approach as heuristics to improve the performance of complex queries on graph-based NoSQL databases are database volatility, database size, type of query, and the size of the query. We utilized the Random Forest machine learning method to narrow candidate nodes to a higher level of search and effectively reduce the search space for efficient querying and retrieval. We compared BF-BigGraph with state-of-the-art approaches, namely BB-Graph, Neo4j’s Cypher, DualIso, GraphQL, TurboIso, and VF3 using publicly available databases including undirected graphs; WorldCup, Pokec, Youtube, and a big graph database of a real demographic application (a population database) with approximately 70 million nodes of a big directed graph. The performance results of our approach for different types of complex queries on all these databases are significantly better in terms of computation time and required memory than other competing approaches in the literature.

Privacy-Preserving Graph Matching Query Supporting Quick Subgraph Extraction

Graph matching, as one of the most fundamental problems in graph database, has a wide range of applications. Due to the large scale of graph database and the hardness of graph matching, it is an attractive alternative to make use of the cloud to store massive data graphs and conduct the complex graph matching. To protect the privacy, data graphs are usually encrypted before being outsourced to the cloud. A few schemes have been proposed to support graph matching query over encrypted graphs. However, none of them can realize efficient subgraph extraction when the matched subgraph needs to be exactly located at the data graph. The graph user has to perform the complex subgraph isomorphism operation to extract the isomorphic subgraph from the matched data graph in state-of-the-art schemes. The time complexity of judging subgraph isomorphism is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$O(n!n^{2})$</tex-math></inline-formula> at most, where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$n$</tex-math></inline-formula> is the number of vertices in the data graph. In order to solve this problem, we propose a privacy-preserving graph matching query scheme supporting quick subgraph extraction in this paper. In our design, two non-colluding cloud servers are adopted to accomplish the matching operation jointly. Neither of them can infer the plaintexts of graphs. The first cloud server can prune some impossible matched vertices from the data graph, and get a possible matched matrix to represent which vertices in the data graph might match with the vertices in the query graph. The second cloud server performs the related matrix operations based on its stored information and the information sent by the first cloud server. The two cloud servers jointly verify the correctness of the matched matrix. The graph user can directly and quickly extract the matrix of the subgraph isomorphic to the query graph from the data graph matrix based on the matched matrix, and further recover this subgraph. No subgraph isomorphism operation is involved in the procedure of subgraph extraction for the graph user. The time complexity of subgraph extraction is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$O(m^{2})$</tex-math></inline-formula> in our scheme, where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$m$</tex-math></inline-formula> is the number of vertices in the query graph. The extensive experiments with real-world database demonstrate the efficiency of the proposed privacy-preserving graph matching scheme.

A Graph-Based Keyword Extraction Method for Academic Literature Knowledge Graph Construction

In this paper, we construct an academic literature knowledge graph based on the relationship between documents to facilitate the storage and research of academic literature data. Keywords are an important type of node in the knowledge graph. To solve the problem that there are no keywords in some documents for several reasons in the process of knowledge graph construction, an improved keyword extraction algorithm called TP-CoGlo-TextRank is proposed by using word frequency, position, word co-occurrence frequency, and a word embedding model. By combining the word frequency and position in the document, the importance of words is distinguished. By introducing the GloVe word-embedding model, which brings the external knowledge of documents into the TextRank algorithm, and combining the internal word co-occurrence frequency in the documents, the word-adjacency relationship is transferred non-uniformly. Finally, the words with the highest scores are combined into phrases if they are adjacent in the original text. The validity of the TP-CoGlo-TextRank algorithm is verified by experiments. On this basis, the Neo4j graph database is used to store and display the academic literature knowledge graph, to provide data support for research tasks such as text clustering, automatic summarization, and question-answering systems.

ST-AGNet: Dynamic power system state prediction with spatial–temporal attention graph-based network

Accurate and timely prediction of power system states is one of the most important challenging tasks in modern power systems. Considering the integration of renewable energy sources, recent deep learning-based models have been well studied and found to have benefits in exploiting spatial–temporal relationships in power system data. However, the complexity of different power system topology structures is not substantially captured since the existing models did not fully consider the graph-based information retrieved from power networks. To resolve the problem, a spatial–temporal attention graph-based network (ST-AGNet), an adaptive power system state prediction approach that utilizes graph-based information data to account various typologies of complex power systems, is proposed. Initially, the power flow model is used for generating historical system state data. With the graph-based topology information, the input dataset with the spatial and temporal features is fed into the proposed network for the training and validating process. Meanwhile, the connectivity of the time-varying graph-based information are accounted in the proposed model. Case studies demonstrate the superiority of the ST-AGNet model over the existing baselines under four different scales of complex systems, which can significantly support dynamic power system analysis and operational tasks.

Semi-automatic Knowledge Graph Construction Based on Deep Learning

The paper studied course knowledge graph in teaching resources and curriculum knowledge management tasks from the perspective of knowledge management. Considering that the course of Python Language Programming itself has formed a relatively complete knowledge system and knowledge point structure, the paper adopted a top-down approach to build the knowledge graph. Firstly, the paper obtained different types of course-related corpus and data from different sources, and then constructed the ontology layer of Python programming course. At the ontology level, the paper defined the concept type, relation type and attribute type of the course domain respectively. Considering the completeness of knowledge points in the curriculum domain knowledge graph, the paper extracted all entities, relationships, attributes, and its values from the curriculum corpus using a semi-automatic extraction method that takes into account both accuracy and efficiency based on the modeling results of the ontology layer. Then they were transformed into triples in the form of &lt; entity, relationship, entity &gt; or &lt; entity, attribute, attribute value &gt; to build data layer of knowledge graph. Finally, visualization of triplet data was realized through Neo4j graph database.

Semantics-Enabled Data Federation: Bringing Materials Scientists Closer to FAIR Data

The development and discovery of new materials can be significantly enhanced through the adoption of FAIR (Findable, Accessible, Interoperable, and Reusable) data principles and the establishment of a robust data infrastructure in support of materials informatics. A FAIR data infrastructure and associated best practices empower materials scientists to access and make the most of a wealth of information on materials properties, structures, and behaviors, allowing them to collaborate effectively, and enable data-driven approaches to material discovery. To make data findable, accessible, interoperable, and reusable to materials scientists, we developed and are in the process of expanding a materials data infrastructure to capture, store, and link data to enable a variety of analytics and visualizations. Our infrastructure follows three key architectural design philosophies: (i) capture data across a federated storage layer to minimize the storage footprint and maximize the query performance for each data type, (ii) use a knowledge graph-based data fusion layer to provide a single logical interface above the federated data repositories, and (iii) provide an ensemble of FAIR data access and reuse services atop the knowledge graph to make it easy for materials scientists and other domain experts to explore, use, and derive value from the data. This paper details our architectural approach, open-source technologies used to build the capabilities and services, and describes two applications through which we have successfully demonstrated its use. In the first use case, we created a system to enable additive manufacturing data storage and process parameter optimization with a range of user-friendly visualizations. In the second use case, we created a system for exploring data from cathodic arc deposition experiments to develop a new steam turbine coating material, fusing a combination of materials data with physics-based equations to enable advanced reasoning over the combined knowledge using a natural language chatbot-like user interface.

Enhancing fraud detection in banking by integration of graph databases with machine learning

The banking sector's shift from traditional physical locations to digital channels has offered customers unprecedented convenience and increased the risk of fraud for customers and institutions alike. In this study, we discuss the pressing need for robust fraud detection & prevention systems in the context of evolving technological environments. We introduce a graph-based machine learning model that is specifically designed to detect fraudulent activity in various types of banking operations, such as credit card transactions, debit card transactions, and online banking transactions. This model uses advanced methods for anomalies, behaviors, and patterns to analyze past transactions and user behavior almost immediately. We provide an in-depth methodology for evaluating fraud detection systems based on parameters such as Accuracy Recall rate and False positive rate ROC curves. The findings can be used by financial institutions to develop and enhance fraud detection strategies as they demonstrate the effectiveness and reliability of the proposed approach. This study emphasizes the critical role that innovative technologies play in safeguarding the financial sector from the ever-changing strategies of fraudsters while also enhancing banking security.•This paper aims to implement the detection of fraudulent transactions using a state-of-the-art Graph Database approach.•The relational graph of features in the dataset used is modelled using Neo4J as a graph database.•Applying JSON features from the exported graph to various Machine Learning models, giving effective outcomes.

Risk identification in Japanese consumer product injury data throughout an ontology-based knowledge base

Risk identification and further risk assessment of consumer product injury data are critical tools for accident prevention. However, in Japan, existing risk assessment methods face challenges due to issues such as inconsistent semantic understanding of risk-related vocabulary among Japanese analysts, posing obstacles to the effective identification and assessment of risk information. This study develops an ontology-based knowledge base (JCPRI-KB), which includes an ontology (JCPRI-Onto) and a total of 41 standardised vocabulary sets, for the risk information identification and standardization of consumer product injury data in Japan. A graph database was used to represent and store the risk knowledge of JCPRI-KB. Finally, the JCPRI-KB is applied for a case study of risk identification and further risk assessment of child product injury data. The risk assessment results of this study demonstrate that the JCPRI-KB offers a promising new approach to the field of consumer product safety in Japan, contributing to the enhancement of accuracy and efficiency in risk identification and thus promoting consumer product safety.

Connecting Asset Stores with Graph Databases

Connecting Asset Stores with Graph Databases

System of complex data analysis of thematic sites ISCAD IS

Objectives. Currently, the main source of information is the Internet. The huge amount of information available on the Internet makes it urgent to comprehensively analyze data from open Internet sources.The goal of this work is to create a multi-purpose, modifiable cluster for in-depth analysis of data from Internet sources, the main objectives of which are to identify the most important publications in a certain subject area, thematic analysis of these publications, identifying the leader of a scientific direction and determining trends in the development of areas and interaction of groups of people.Methods. To solve this problem, a methodology was developed for constructing a multi-purpose cluster using technologies for quickly constructing a thematic graph database, a knowledge graph, methods and models of machine learning for in-depth analysis of data.Results. A system for comprehensive analysis of data from thematic sites ISKAD IS has been developed, a methodology for quickly constructing a thematic graph database and a comprehensive technology for in-depth analysis of data from Internet sources and analysis of data from the most important well-known world sites have been tested.Conclusion. An IT environment has been created for the rapid construction of thematic graph databases. The results of using the technology for quickly constructing graph databases are shown using examples of the work of ISKAD IS.

Query execution time estimation in graph databases based on graph neural networks

Query execution time estimation is an essential task for databases, accurate estimation results can help administrators to manage and monitor systems. This study proposes an interaction-aware and dependency-aware query execution time estimation approach that utilizes graph neural networks to capture dependence and interaction relationships. We divide graph query execution time estimation tasks into three stages: workload generation and running, graph-based feature modeling and representation, training and estimation. Specifically, we generate query workloads and run them to collect the database and plan information when queries are executed. Then, the collected plan and database components are modeled into vertexes, the interaction and dependency between them are modeled into edges of graph-based feature representation. We develop an estimation model based on graph neural networks, in which the vertex embedding network is proposed to deal with the vertex heterogeneity, and the message passing network is proposed to aggregate the local representation into the global representation to obtain an embedding that can represent the higher-order feature information of the whole graph, and the estimation network is proposed to estimate execution times. The experiment results on datasets show that our estimation approach can improve estimation quality and outperform other estimation approaches in terms of estimation accuracy.