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Traditional Chinese medicine has a history of over 5000 years in China and plays an important role in the reproduction and health of the Chinese nation. However, traditional Chinese medicine data is usually unstructured, which seriously affects the popularization and application of traditional Chinese medicine knowledge. Therefore, structuring traditional Chinese medicine data is of great significance. For the problem of unstructured data in traditional Chinese medicine, we designed and implemented a knowledge graph of traditional Chinese medicine. Firstly, six relationships including medicinal herbs, aliases, and functional indications were selected to construct the conceptual layer of the knowledge graph of traditional Chinese medicine. Then, using six types of relationships, data was obtained from professional books such as agricultural lexicons and traditional Chinese medicine websites, and traditional Chinese medicine knowledge was processed to obtain triplet form of traditional Chinese medicine knowledge. Finally, the knowledge of traditional Chinese medicine would be stored in the Neo4j database and presented. The construction of a knowledge graph of traditional Chinese medicine would greatly improve the comprehensibility and accessibility of traditional Chinese medicine knowledge, providing data support for later clinical decision-making and traditional Chinese medicine research. [ABSTRACT FROM AUTHOR]

Databases are a fundamental element of contemporary software applications. The most widely used and recognized type in practice is the relational database, valued for its ability to store and organize data in tabular structures, its emphasis on data consistency and integrity, and its use of a standardized query language, SQL. However, with the rapid increase in both the volume and complexity of data, relational databases have recently encountered challenges in effectively modeling this expanding information. To address performance challenges, new database systems have emerged, offering alternative approaches to data modeling—these are known as NoSQL databases. In this paper, we present an indoor navigation application designed to operate on both a relational database, Microsoft SQL Server, and a graph-based NoSQL database, Neo4j. We describe the algorithms implemented for testing and the performance metrics analyzed to draw our conclusions. The results revealed Neo4j's strength in managing data with complex relationships but also exposed its limitations in handling concurrent access, where SQL Server demonstrated significantly greater stability. [ABSTRACT FROM AUTHOR]

Ship pollution accidents can cause serious harm to marine ecosystems and economic development. This study proposes a ship pollution accident analysis method based on a knowledge graph to solve the problem that complex accident information is challenging to present clearly. Based on the information of 411 ship pollution accidents along the coast of China, the Word2vec's word vector models, BERT–BiLSTM–CRF model and BiLSTM–CRF model, were applied to extract entities and relations, and the Neo4j graph database was used for knowledge graph data storage and visualization. Furthermore, the case information retrieval and cause correlation of ship pollution accidents were analyzed by a knowledge graph. This method established 3928 valid entities and 5793 valid relationships, and the extraction accuracy of the entities and relationships was 79.45% and 82.47%, respectively. In addition, through visualization and Cypher language queries, we can clearly understand the logical relationship between accidents and causes and quickly retrieve relevant information. Using the centrality algorithm, we can analyze the degree of influence between accident causes and put forward targeted measures based on the relevant causes, which will help improve accident prevention and emergency response capabilities and strengthen marine environmental protection. [ABSTRACT FROM AUTHOR]

This work presents a threat modelling approach to represent changes to the attack paths through an Internet of Things (IoT) environment when the environment changes dynamically, that is, when new devices are added or removed from the system or when whole sub-systems join or leave. The proposed approach investigates the propagation of threats using attack graphs, a popular attack modelling method. However, traditional attack-graph approaches have been applied in static environments that do not continuously change, such as enterprise networks, leading to static and usually very large attack graphs. In contrast, IoT environments are often characterised by dynamic change and interconnections; different topologies for different systems may interconnect with each other dynamically and outside the operator's control. Such new interconnections lead to changes in the reachability amongst devices according to which their corresponding attack graphs change. This requires dynamic topology and attack graphs for threat and risk analysis. This article introduces an example scenario based on healthcare systems to motivate the work and illustrate the proposed approach. The proposed approach is implemented using a graph database management tool (GDBM), Neo4j, which is a popular tool for mapping, visualising, and querying the graphs of highly connected data. It is efficient in providing a rapid threat modelling mechanism, making it suitable for capturing security changes in the dynamic IoT environment. Our results show that our developed threat modelling approach copes with dynamic system changes that may occur in IoT environments and enables identifying attack paths, whilst allowing for system dynamics. The developed dynamic topology and attack graphs can cope with the changes in the IoT environment efficiently and rapidly by maintaining their associated graphs. [ABSTRACT FROM AUTHOR]

With the rapid acceleration of urbanization and the widespread adoption of social networks, A vast amount of spatiotemporal check-in data from social media users has been generated. Traditional relational databases have limitations in handling massive spatiotemporal data, whereas graph databases, with their efficiency in data querying and processing, offer a novel solution for the analysis of spatiotemporal data. The paper introduces a method for constructing a user check-in spatiotemporal knowledge graph based on the Neo4j graph database, utilizing a designed graph model to create a visual knowledge graph. By incorporating temporal and spatial characteristics into entities, we realize efficient management and complex query of spatiotemporal data of social media user check-ins. By adding sentiment attributes to relationships through sentiment analysis of check-in text, the application of check-in text is realized. Ultimately, employing Cypher to achieve knowledge querying, we analyze the hot spots of urban activities and categories. [ABSTRACT FROM AUTHOR]

Conversational AI systems have emerged as key enablers of human-like interactions across diverse sectors. Nevertheless, the balance between linguistic nuance and factual accuracy has proven elusive. In this paper, we first introduce LLMXplorer, a comprehensive tool that provides an in-depth review of over 205 large language models (LLMs), elucidating their practical implications, ranging from social and ethical to regulatory, as well as their applicability across industries. Building on this foundation, we propose a novel functional architecture that seamlessly integrates the structured dynamics of knowledge graphs with the linguistic capabilities of LLMs. Validated using real-world AI news data, our architecture adeptly blends linguistic sophistication with factual rigor and further strengthens data security through role-based access control. This research provides insights into the evolving landscape of conversational AI, emphasizing the imperative for systems that are efficient, transparent, and trustworthy. [ABSTRACT FROM AUTHOR]

In order to solve the problem of ''potpourri'' of safety risk prevention and control measures, which is caused by the unclear mechanism of the spatial effect of coal mine safety level heterogeneity and its influencing factors. This paper discriminates the dominant factors of spatio-temporal heterogeneity of coal mine safety production level in China and their spatial effect types by means of GeoDetector and the Spatial Dubin Model (SDM), specifies the categories and degrees of acts on local and surrounding areas by changes in indicators, and provides further visualization of the detection outcomes with the assistance of the Neo4j graph database, and the findings indicate that:(1) All 15 indicators selected in the study have a certain influence on the generation of spatial heterogeneity in China's coal mine safety production level, and all of them show an enhancement relationship after the interaction of indicators. Especially, the combination of excavated environment and other indicators basically has a non-linear enhancement relationship. (2) In terms of spatial effects, the influence of the 5 effects on the spatio-temporal heterogeneity of coal mine safety level is, in descending order, industrial development effect > capital allocation effect > production environment effect > government supervision effect > enterprise management effect, which indicates that macroeconomic and market conditions have a much stronger influence on the generation of spatio-temporal heterogeneity in coal mine production safety status. (3) From the single indicator perspective, the average annual temperature, average annual wind speed, coal consumption and monitoring efficiency primarily affect the dependent variable through direct effects; GDP per capita, average labor compensation as well as railroad operating mileage have positive spatial spillover on the changes of coal mine safety production level in surrounding areas; the evaluation of the spatial effect for average labor compensation exhibits a positive indirect effect with low influence; for the two indicators of production efficiency and ex-factory price, not only do they have negative effects on the local coal mine safety level, but also have significant spillover effects on surrounding areas.

This paper presents a holistic approach to biological and agricultural research focused on the use of interconnected technologies in the context of climate change. Researchers from different countries have analyzed how smart technologies can help agriculture adapt to these changes. The most representative works in the field are analyzed. Among these tech-nologies are graph database systems such as Neo4j, which have demonstrated success in predicting the studied phenomena. The paper describes the development of a soybean crop productivity prediction model using monthly and annual data of meteorological phenomena such as precipitation, air temperature, hydrothermal coefficient, soil moisture and others. Some of the results of this promising research are also presented.

The International Statistical Classification of Diseases and Related Health Problems, 10th Revision (ICD-10) is internationally used for coding diagnoses, with the ICD-10 German Modification (GM) being prescribed for morbidity coding in Germany. ICD-10-GM is subject to annual revisions. This can lead to backward compatibility issues leading to undesirable consequences for cross-version data analysis. A study of annual crosswalk-tables concerning 21 ICD-10-GM versions showed that the ratio of difficult transitions from an older to a newer version (0.89 %) and vice versa (0.48 %) is not particularly significant but should nevertheless not be neglected. In this paper we present two solutions (Neo4J database and FHIR ConceptMaps) for the automated handling of different ICD-10-GM versions. [ABSTRACT FROM AUTHOR]

Copyright of Dicle University Journal of Engineering / Dicle Üniversitesi Mühendislik Dergisi is the property of Dicle Universitesi and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

With the rapid advancement of technology and the vast volume of unstructured data available on the Internet, there is a pressing need to extract information from diverse data formats effectively. This is essential as valuable pieces of information may be lost. To address this issue, researchers are using Machine Learning (ML) and Natural Language Processing (NLP) techniques to extract information from unstructured text, including the utilization of Knowledge Graphs (KGs). This paper demonstrates end-to-end experimental studies of KG construction from unstructured text using open-source techniques and concrete real-world examples in different problem domains. The unstructured data underwent a text processing pipeline consisting of coreference resolution, named entity linking, and relationship extraction. The pipeline is designed to support automatic data storage in a graph database known as Neo4j. This storage includes the extracted entities and their relationships. Experiments were conducted on a real-world unstructured BBC News Dataset to analyze the outcome obtained from the pipeline. The experience can facilitate the adoption of KG creation for practitioners to capture valuable information from a large volume of unstructured text. The results from the relationship extraction step using two techniques were evaluated, including extracted entities, relationship types, accuracies of 61.4% with OpenNRE and 87% with REBEL, and processing time. Further, the data processing pipeline was applied to analyze the unstructured dataset from the Transportation Safety Board’s (TSB) Findings for aviation safety analysis. The results showed that structured relationships identified through the pipeline provided valuable indicators, as they captured critical aviation safety information, such as the flight, aircraft type, event, etc. This pipeline can be fine-tuned with a domain-specific knowledge base to provide higher accuracy and better entity detection.

Knowledge Graph Question Answering (KGQA) plays a crucial role in extracting valuable insights from interconnected information. Existing methods, while commendable, face challenges such as contextual ambiguity and limited adaptability to diverse knowledge domains. This paper introduces TransKGQA, a novel approach addressing these challenges. Leveraging Sentence Transformers, TransKGQA enhances contextual understanding, making it adaptable to various knowledge domains. The model employs question-answer pair augmentation for robustness and introduces a threshold mechanism for reliable answer retrieval. TransKGQA overcomes limitations in existing works by offering a versatile solution for diverse question types. Experimental results, notably with the sentence-transformers/all-MiniLM-L12-v2 model, showcase remarkable performance with an F1 score of 78%. This work advances KGQA systems, contributing to knowledge graph construction, enhanced question answering, and automated Cypher query execution.

Databases are a fundamental element of contemporary software applications. The most widely used and recognized type in practice is the relational database, valued for its ability to store and organize data in tabular structures, its emphasis on data consistency and integrity, and its use of a standardized query language, SQL. However, with the rapid increase in both the volume and complexity of data, relational databases have recently encountered challenges in effectively modeling this expanding information. To address performance challenges, new database systems have emerged, offering alternative approaches to data modeling—these are known as NoSQL databases. In this paper, we present an indoor navigation application designed to operate on both a relational database, Microsoft SQL Server, and a graph-based NoSQL database, Neo4j. We describe the algorithms implemented for testing and the performance metrics analyzed to draw our conclusions. The results revealed Neo4j’s strength in managing data with complex relationships but also exposed its limitations in handling concurrent access, where SQL Server demonstrated significantly greater stability.

The challenge of securing IT environments has reached a new complexity level as a growing number of organisations adopt cloud solutions. This trend increases the possibility of overseen attack paths in an organisation's IT infrastructure. This paper proposes a methodology for assessing the security of a Microsoft cloud tenant based on the relationships between different cloud entities through the use of graphs. This paper argues for using graph theory as an effective method to understand and uncover complex entity attack paths. To achieve this, we implemented a graph analytics platform using data from a Microsoft cloud test tenant. Methods based on graph theory proved to measurably reduce possible attack paths. Our research can support defenders who want to better understand the interrelationships of Microsoft cloud entities as well as identify and remediate possible attack paths. [ABSTRACT FROM AUTHOR]

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]

This paper presents a method to store information about megalithic monument-building components as graph nodes in a knowledge graph (KG). As a case study, we analyse the dolmens from the region of Pavia (Portugal). To build the KG, information has been extracted from unstructured data to populate a schema model based on the International Committee for Documentation – Conceptual Reference Model (CIDOC-CRM). In order to prepare the archaeological monument’s information for bulk loading, it was transformed into semi-structured data. While the semi-structured file was used to populate the classes with their respective properties and instances, the KG labels and types were defined using some of the entities and relations defined by the CIDOC-CRM. The knowledge-driven model was built to represent dolmens in a formal and structured manner using Neo4j, a property-graph database. Modelling a labelled property graph based on predefined labels as a KG enables to transform textual semantic data into instances and properties. Thus, we show that it is possible to represent at a granular level all the information about the structural components of monuments since heterogeneities, granularities, and large amounts of data can be handled by a KG. Therefore, a KG implemented using a native graph database can improve data storage and processing, making it interoperable between humans, humans and machines and machine to machine.

Abstract This paper presents a model-based approach for converting Common Grid Model Exchange Specification (CGMES) power system models into operational networks. The approach utilizes the Model-Driven Architecture (MDA), borrowed from software engineering, to create a Platform-Independent Model (PIM) saved in a graph database, neo4j. The PIM is then transformed into Platform-Specific Model (PSM) using cypher queries. Finally, from the PSM, a Pandapower network is generated that is used later for running power flow simulations. The Common Information Model (CIM) ontology classes, which form the metamodel behind CGMES, are loaded into the same database, connecting them to the grid instances, allowing for automatic validation and inference. The proposed approach has been successfully applied to large-scale real-world grids that include renewable energy sources.

With the proliferation of data, relational database management systems face the challenge of managing big volumes of data because of the absence of dynamic data models, high functioning, high scalability, high availability, and low cost. The NoSQL technology has the features which RDBMS lacks. Therefore, many organizations are migrating from RDBMS to NoSQL databases. The migration from RDBMS to NoSQL is not always effective if the data is lost, if the data is replicated while transmission, and if the performance is degraded. For this reason, enhancement of transformation algorithm proposed by Shabana Ramzan et al. has been performed to implement the methodology which is used to successfully migrate data from structured and normalized SQL database MySQL to NoSQL graph database Neo4j. SchemaCrawler engine has been used to metamodeling from source to destination database and execute the transformation rules. This study has also evaluated and validated the performance of the enhanced algorithm after successful migration of data. The NoSQL graph database Neo4j handles the most common form of data that are unstructured, semi-structured, and structured as well as they support the ACID property of relational databases. This feature is not supported by other NoSQL databases such as Oracle NoSQL, MongoDB, and HBase databases.

Conversational AI systems have emerged as key enablers of human-like interactions across diverse sectors. Nevertheless, the balance between linguistic nuance and factual accuracy has proven elusive. In this paper, we first introduce LLMXplorer, a comprehensive tool that provides an in-depth review of over 205 large language models (LLMs), elucidating their practical implications, ranging from social and ethical to regulatory, as well as their applicability across industries. Building on this foundation, we propose a novel functional architecture that seamlessly integrates the structured dynamics of knowledge graphs with the linguistic capabilities of LLMs. Validated using real-world AI news data, our architecture adeptly blends linguistic sophistication with factual rigor and further strengthens data security through role-based access control. This research provides insights into the evolving landscape of conversational AI, emphasizing the imperative for systems that are efficient, transparent, and trustworthy.