Domain Ontology Representation Model Based on Graph Databases

An important step in data analysis is the exploration of data. For traditional relational databases one of the most powerful tools for performing such analysis is the relational database and the aggregates and rankings that they can compute: for instance, simple statistics such as the average number of links between two types of entities (relations) are easily computed using a query on a relational database and may already provide valuable information. However, for the exploration of graph data, relational databases may not be most practical and scalable. For instance, a statistic such as the shortest path between two given nodes cannot be computed by a relational database. Surprisingly, however, tools for querying graph and network databases are much less well developed than for relational data, and only recently an increasing number of studies are devoted to graph or network databases. Our position is that the development of such graph databases is important both to make basic graph mining easier and to prepare data for more complex types of analysis. An important component of such databases is the language that is used to enable aggregating queries, such as shortest path queries.In this paper, we propose an extension to a previously proposed query language. This extension allows for querying and analyzing databases by using aggregates and ranking. A notable feature of our language is that it also supports probabilistic graph queries by conceiving of such queries as aggregating queries. We demonstrate its value on a simple data analysis task.

Graph theory, which is a branch of discrete mathematics, has emerged as a powerful tool in various domains, including database management. This abstract investigates the ways in which ideas and methods from graph theory which can be applied to database systems, offering a thorough synopsis of their benefits. Complex interactions within data can be well-modeled by using the basic concepts of graph theory, such as nodes, edges, and relationships. Because of its capacity to represent and query complex relationships, graph databases have become more and more popular in the field of database administration. Graph databases are well-suited for situations such as social networks, recommendation systems, and interconnected data domains because they are excellent at representing and traversing relationships, in contrast to standard relational databases, which are excellent at managing structured data. The abstract delves into the key graph-based data models, such as property graphs, RDF (Resource Description Framework), explaining how they facilitate the representation of diverse relationships. Furthermore, it explores the efficient storage and retrieval mechanisms that leverage graph traversal algorithms to extract valuable insights from interconnected datasets. The document highlights specific use cases where graph theory contributes to database management, including fraud detection, social network analysis, and recommendation systems. Additionally, it discusses the challenges associated with integrating graph databases into existing infrastructures and proposes solutions to address scalability and performance concerns. The abstract also touches upon the advancements in graph database query languages (Cypher) and SPARQL, showcasing their expressive power in querying complex relationships. The inclusion of graph-based indexing and optimization techniques demonstrates how database systems can efficiently handle queries involving large-scale graph data. As graph databases continue to evolve, this abstract concludes by outlining potential future directions in the intersection of graph theory and database management. It emphasizes the importance of ongoing research in developing scalable and efficient solutions for managing interconnected data, ultimately paving the way for more sophisticated and context-aware database systems relationships. Furthermore, it explores the efficient storage and retrieval mechanisms that leverage graph traversal algorithms to extract valuable insights from interconnected datasets.

Background. The relevance of the article is due to the development of modern ontological methods of structuring information and the need to systematize data in many new specific subject areas. Such subject areas include the musical art of the "metal" variety, which is quite common today, but insufficiently studied within the terminology. The subject of the article are ontological models and tools for creating ontological knowledge bases. Objective. The purpose of the paper is to increase the correctness of the semantic search in the knowledge base of the musical supergenre "metal". The scientific problem is the need to improve the terminology in this subject area and build an ontological knowledge model that increases the accuracy of information retrieval for the target audience, compared to the existing relational model implemented on one of the known web resources. Methods. Classification method, generalization method, software optimization methods, analytical method. The way to solve the problem: selection based on the comparative characteristics of the best web resource of the subject area and identifying the shortcomings of its model of knowledge representation, designing an ontological knowledge model and testing its effectiveness. Results. The average SUM for all users is 83.85%, which is a good indicator for ontological knowledge bases. At the same time, a similar method of checking the database of the supergenre "metal" on the basis of the site "Encyclopedia Metallum", which used the classical relational model of database organization, showed much lower results. Thus, the average SUM for 10 users was 75.32%, respectively. Conclusions. The scientific novelty of the obtained results is as follows: For the first time an ontological model (ontology) of the subject area was created: musical supergenre "metal", which showed much higher efficiency of semantic search than the best relational model of this subject area, implemented as a web resource. The developed structure can be used to create ontologies of related musical supergenres with similar terminology. Future research also plans to integrate this ontological knowledge model with applied web-based and desktop applications.

Abstract. Over the recent years, the usage of semantic web technologies and Resources Description Framework (RDF) data models have been notably increased in many fields. Multiple systems are using RDF data to describe information resources and semantic associations. RDF data plays a very important role in advanced information retrieval, and graphs are efficient ways to visualize and represent real world data by providing solutions to many real-time scenarios that can be simulated and implemented using graph databases, and efficiently query graphs with multiple attributes representing different domains of knowledge. Given that graph databases are schema less with efficient storage for semi-structured data, they can provide fast and deep traversals instead of slow RDBMS SQL based joins allowing Atomicity, Consistency, Isolation and durability (ACID) transactions with rollback support, and by utilizing mathematics of graph they can enormous potential for fast data extraction and storage of information in the form of nodes and relationships. In this paper, we are presenting an architectural design with complete implementation of BIM-GIS integrated RDF graph database. The proposed integration approach is composed of four main phases: ontological BIM and GIS model’s construction, mapping and semantic integration using interoperable data formats, then an import into a graph database with querying and filtering capabilities. The workflows and transformations of IFC and CityGML schemas into object graph databases model are developed and applied to an intelligent urban mobility web application on a game engine platform validate the integration methodology.

The Next Generation Linkage Management System (NGLMS) was designed around keeping all data in a graph database. However, this constraint, while easily achievable for greenfield projects and/or new data linkage units, may not be easily met where legacy data exists.
 Objectives and ApproachThe NGLMS was extended to encompassed system where data was held partially or even completely in a relational database. By grouping the data managed by the NGLMS into system metadata, record link data and record data and allowing system metadata and record data to be stored separately and independently in either a relational or a graph database, the NGLMS allows hybrid installations of mixed graph and relational data and, with some loss of functionality, purely relational installations.
 ResultsThe functionality of the NGLMS was expanded to allow review of existing legacy data stored in a relational database system. Through minor changes to the server used by the NGLMS Clerical Review tool (NGLMS-CR) the review tools was able to present the same interface and allow the same integration as project stored completely within the graph database.
 Hybrid projects where link information was stored in a graph could be accommodated with no loss of functionality.Relational-only projects allowed clerical review of identified clusters in a manner identical to the graph-only NGLMS but involved some curtailment of the advanced clustering, time-slicing, compositional and concurrent functionality of the NGLMS due to the loss of the functionality provided by a graph database. They do provide an upgrade pathway to hybrid projects and then graph-only projects.
 Conclusion / ImplicationsAllowing the NGLMS to be configured as a hybrid system enables a gradual adoption of the NGLMS toolset and software.
 
 purely relational data still allows the use of the NGLMS-CR with customisable workpools and workflows
 a hybrid relational/graph system where record data is still kept in a relational store but cluster and linkage information is kept in the graph store allows the use of legacy data without major disruption and provides a pathway to full adoption

In recent years, the increase in the amount of data generated in basic social practices and specifically in all fields of research has boosted the rise of new database models, many of which have been employed in the field of Molecular Biology. NoSQL graph databases have been used in many types of research with biological data, especially in cases where data integration is a determining factor. For the most part, they are used to represent relationships between data along two main lines: (i) to infer knowledge from existing relationships; (ii) to represent relationships from a previous data knowledge. In this work, a short history in a timeline of events introduces the mutual evolution of databases and Molecular Biology. We present how graph databases have been used in Molecular Biology research using High Throughput Sequencing data, and discuss their role and the open field of research in this area.

This paper presents an approach to detect behavioral design patterns from source code using static analysis techniques. It depends on the concept of Code Property Graph and enriching graph with relationships and properties specific to Design Patterns, to simplify the process of Design Pattern detection. This approach used NoSQL graph database (Neo4j) and uses graph traversal language (Gremlin) for doing graph matching. Our approach, converts the tasks of design pattern detection to a graph matching task by representing Design Patterns in form of graph queries and running it on graph database.

Graph databases are becoming a topic of interest in the research community because of the possibilities that offers in the era of big data. Recently, different types of graph databases have been proposed, however, majority of graph databases are under fifteen years of age and have constant improvements. This article presents a review of graph databases and introduces an architecture which represents the basis for most of the graph databases. Besides, according to data storage type and the data model, a graph database taxonomy is proposed. Such taxonomy allows a categorization from graph databases studied in this research. In the last part we present a survey that describe deeply some popular graph databases. This survey has the aim to provide a guideline to the selection of one of these databases.KeywordsGraph databasesData modelsGraph data management

In this paper, a Graph-based semantic Data Model (GDM) is proposed with the primary objective of bridging the gap between the human perception of an enterprise and the needs of computing infrastructure to organize information in some particular manner for efficient storage and retrieval. The Graph Data Model (GDM) has been proposed as an alternative data model to combine the advantages of the relational model with the positive features of semantic data models. The proposed GDM offers a structural representation for interacting to the designer, making it always easy to comprehend the complex relations amongst basic data items. GDM allows an entire database to be viewed as a Graph (V, E) in a layered organization. Here, a graph is created in a bottom up fashion where V represents the basic instances of data or a functionally abstracted module, called primary semantic group (PSG) and secondary semantic group (SSG). An edge in the model implies the relationship among the secondary semantic groups. The contents of the lowest layer are the semantically grouped data values in the form of primary semantic groups. The SSGs are nothing but the higher-level abstraction and are created by the method of encapsulation of various PSGs, SSGs and basic data elements. This encapsulation methodology to provide a higher-level abstraction continues generating various secondary semantic groups until the designer thinks that it is sufficient to declare the actual problem domain. GDM, thus, uses standard abstractions available in a semantic data model with a structural representation in terms of a graph. The operations on the data model are formalized in the proposed graph algebra. A Graph Query Language (GQL) is also developed, maintaining similarity with the widely accepted user-friendly SQL. Finally, the paper also presents the methodology to make this GDM compatible with the distributed environment, and a corresponding query processing technique for distributed environment is also suggested for the sake of completeness.

This article presents a comprehensive analysis of various graph database models, highlighting their respective advantages and limitations. Graph databases are valuable in ap- plications such as recommendation systems, social networks, and fraud detection because they excel at maintaining complicated data relationships. Neo4j, Janus Graph, and Arango DB are the major graph database suppliers. Scalability, performance, and ease of use are among the benefits offered by these providers. Janus Graph is a suitable candidate for large-scale deployments because to its distributed graph processing capabilities, but Neo4j is noted for its scalability and ease of use. Arango DB is a multi-model database that can handle a variety of data models, including graph databases. The significance of Graph databases over Machine Learning (ML) in many sectors has also been investigated.

Several application domains hold highly connected data, like supply chain and social network. In this context, NoSQL graph databases raise as a promising solution since relationships are first class citizens in their data model. Nevertheless, a traditional database design methodology initially defines a conceptual schema of the domain data, and the Enhanced Entity-Relationship (EER) model is a common tool. This paper presents a rule-based conversion process from an EER schema to Neo4j schema constraints, as Neo4j is the most representative NoSQL graph database management system with an expressive data model. Different from related work, our conversion process deals with all EER model concepts and generates rules for ensuring schema constraints through a set of Cypher instructions ready to run into a Neo4j database instance, as Neo4J is a schemaless system, and it is not possible to create a schema a priori. We also present an experimental evaluation that demonstrates the viability of our process in terms of performance.

Property Graph databases (PGs) are emerging as efficient graph stores with flexible schemata. This raises the need to have a unified view over heterogenous data produced from these stores. Ontology based Data Access (OBDA) has become the most dominant approach to integrate heterogeneous data sources by providing a unified conceptual view (ontology) over them. The corner stone of any OBDA system is to define mappings between the data source and the target (domain) ontology. However, manual mapping generation is time consuming and requires great efforts. This paper proposes ProGOMap ( Pro perty G raph to O ntology M apper) system that automatically generates mappings from property graphs to a domain ontology. ProGOMap starts by generating a putative ontology with direct axioms from PG. A novel ontology learning algorithm is proposed to enrich the putative ontology with subclass axioms inferred from PG. The putative ontology is then aligned to an existing domain ontology using string similarity metrics. Another algorithm is proposed to align object properties between the two ontologies considering different modelling criteria. Finally, mappings are generated from alignment results. Experiments were done on eight data sets with different scenarios to evaluate the effectiveness of the generated mappings. The experimental results achieved mapping accuracy up to 97% and 81% when addressing PG-to-ontology terminological and structural heterogeneities, respectively. Ontology learning by inferring subclass axioms from a property graph helps to address the heterogeneity between the PG and ontology models.

Current knowledge graphs encompass diverse data formats, including images, text, tables, audio files, and videos. Additionally, the graph database ecosystem is required to support multiple co-existing data models. Addressing these challenges is essential for promoting interoperability between data sources. This demo introduces MillenniumDB, a high-performing, open-source graph database handling this diversity of data formats and models.

The analysis and utilization of grid big data, big data driven grid analysis and optimization have received extensive attention in recent years, and non-relational databases can handle big data problems efficiently. The graph database is a kind of non-relational database. This paper takes the graph database Neo4j as an example. Based on the domain modeling theory and follows the CIM model, the grid data modeling principle is proposed. Under these principles, some power grid data models and their applicable occasions, and methods for converting these models to each other are proposed. Finally, three graph database models are built using the modeling method proposed in this paper. The query performance of Neo4j and MySQL is compared by three examples. The conclusion is that in the traversal with obvious topological characteristics, such as shortest path traversal, deep traversal and time tree traversal, etc., the graph database model proposed in this paper can greatly improve the retrieval efficiency. It proves the effectiveness of the proposed modeling method and the efficiency of the graph database in specific retrieval.

A graph database is a type of NoSQL database that uses graph structure for semantic queries with nodes, edges, and properties to represent and store data. It has been applied in many fields, such as education, health, business, and social network, with many famous applications such as Google, Facebook, and eBay. One of the main advantages of the graph database is its effective performance in data queries. This paper presents a comprehensive comparison of the performance based on the execution time of a NoSQL graph database named Neo4J with a popular relational database system, MySQL, which is used as the underlying technology in developing a software system. Query types are categorized into four groups: selection/ search, recursion, aggregation, and pattern matching. We examined representative questions for each group and executed them on Neo4j and MySQL using a real-life dataset named Career Village. The results show that Neo4j’s data query performance is better than MySQL in most results.