Design and Research of Unstructured Data Knowledge Graph Toolbased on Neo4j Graph Database

Ayinuer, Nuertai; Abudurexiti, Ruxianguli; Aizezi, Yasen

doi:10.1109/icccas55266.2022.9824270

Cited by 8 publications

(2 citation statements)

References 3 publications

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“…The unstructured data is visualized using the Neo4j database, providing a data graph in Cypher syntax. The final result of the work defines that the Neo4j-based unstructured data visualization is more accessible and helps to understand the organization's characteristics [4]. Several researches were conducted to assess the efficiency of the Neo4j system with a graph database.…”

Section: Literature Reviewmentioning

confidence: 99%

KMSBOT: Enhancing Educational Institutions with an AI-Powered Semantic Search Engine and Graph Database

subramanian,

Ammunuel

et al. 2023

Preprint

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In the rapidly evolving field of education, the need for a semantic search engine to efficiently retrieve graph-based data is crucial. Universities and colleges generate vast amounts of educational content research articles, and having a semantic search engine can enhance the accuracy of search results, ensuring that students and staff can access the right information effectively. It's imperative to develop an AI-based smart recommendation system equipped with semantic search capabilities to optimize information retrieval and provide accurate mapping for students or scholars. Such a system can revolutionize how educational content is accessed, offering personalized recommendations that align with the individual learning objectives of students and scholars, thus improving their educational experience. KMSBOT is an innovative academic recommendation system designed to enhance the efficiency of existing models. It effectively summarizes academic data and provides tailored information for students, research scholars, and educational faculty. The system employs a three-module approach, utilizing data structuring, NLP processing, and semantic search engine integration. By leveraging Neo4j, NLTK, and BERT in Python, this proposed work ensures optimal performance metrics such as time, accuracy, and loss value. The proposed solution addresses the limitations of traditional recommendation systems and contributes to improving user satisfaction and engagement in academic environments.

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Section: Literature Reviewmentioning

confidence: 99%

KMSBOT: Enhancing Educational Institutions with an AI-Powered Semantic Search Engine and Graph Database

subramanian,

Ammunuel

et al. 2023

Preprint

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“…Each node represents an entity, and each relationship represents the association between nodes. The graph model proposed in this study consists of three clusters of subgraphs framed around the process or the types of collected data [21]. Fig.…”

Section: Meta-graph Schema Designmentioning

confidence: 99%

Fuzzy Logic Inference System for Managing Intensive Care Unit Resources Based on Knowledge Graph

Subahi,

Athama

2023

CMC

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With the rapid growth in the availability of digital health-related data, there is a great demand for the utilization of intelligent information systems within the healthcare sector. These systems can manage and manipulate this massive amount of health-related data and encourage different decision-making tasks. They can also provide various sustainable health services such as medical error reduction, diagnosis acceleration, and clinical services quality improvement. The intensive care unit (ICU) is one of the most important hospital units. However, there are limited rooms and resources in most hospitals. During times of seasonal diseases and pandemics, ICUs face high admission demand. In line with this increasing number of admissions, determining health risk levels has become an essential and imperative task. It creates a heightened demand for the implementation of an expert decision support system, enabling doctors to accurately and swiftly determine the risk level of patients. Therefore, this study proposes a fuzzy logic inference system built on domain-specific knowledge graphs, as a proof-of-concept, for tackling this healthcare-related issue. The system employs a combination of two sets of fuzzy input parameters to classify health risk levels of new admissions to hospitals. The proposed system implemented utilizes MATLAB Fuzzy Logic Toolbox via several experiments showing the validity of the proposed system.

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KMSBOT: enhancing educational institutions with an AI-powered semantic search engine and graph database

Subramanian,

Chandra,

Immanuel

et al. 2024

Soft Comput

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Design and Research of Unstructured Data Knowledge Graph Toolbased on Neo4j Graph Database

Cited by 8 publications

References 3 publications

KMSBOT: Enhancing Educational Institutions with an AI-Powered Semantic Search Engine and Graph Database

KMSBOT: Enhancing Educational Institutions with an AI-Powered Semantic Search Engine and Graph Database

Fuzzy Logic Inference System for Managing Intensive Care Unit Resources Based on Knowledge Graph

KMSBOT: enhancing educational institutions with an AI-powered semantic search engine and graph database

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