Domain-specific knowledge graphs: A survey

Journal of Network and Computer Applications, Journal Year: 2021, Volume and Issue: 185, P. 103076 - 103076

Published: April 20, 2021

Language: Английский

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Medical Knowledge Graph: Data Sources, Construction, Reasoning, and Applications

Big Data Mining and Analytics, Journal Year: 2023, Volume and Issue: 6(2), P. 201 - 217

Published: Jan. 26, 2023

Medical knowledge graphs (MKGs) are the basis for intelligent health care, and they have been in use a variety of medical applications. Thus, understanding research application development MKGs will be crucial future relevant biomedical field. To this end, we offer an in-depth review MKG work. Our begins with examination four types information sources, graph creation methodologies, six major themes development. Furthermore, three popular models reasoning from viewpoint discussed. A implementation path (RIP) is proposed as means expressing procedures MKG. In addition, explore applications based on RIP classify them into nine types. Finally, summarize current state more than 130 publications challenges opportunities.

Language: Английский

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A survey of multimodal information fusion for smart healthcare: Mapping the journey from data to wisdom

Information Fusion, Journal Year: 2023, Volume and Issue: 102, P. 102040 - 102040

Published: Sept. 27, 2023

Multimodal medical data fusion has emerged as a transformative approach in smart healthcare, enabling comprehensive understanding of patient health and personalized treatment plans. In this paper, journey from to information knowledge wisdom (DIKW) is explored through multimodal for healthcare. We present review focused on the integration various modalities. The explores different approaches such feature selection, rule-based systems, machine ;earning, deep learning, natural language processing, fusing analyzing data. This paper also highlights challenges associated with By synthesizing reviewed frameworks theories, it proposes generic framework that aligns DIKW model. Moreover, discusses future directions related four pillars healthcare: Predictive, Preventive, Personalized, Participatory approaches. components survey presented form foundation more successful implementation Our findings can guide researchers practitioners leveraging power state-of-the-art revolutionize healthcare improve outcomes.

Language: Английский

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A New Complex Fuzzy Inference System With Fuzzy Knowledge Graph and Extensions in Decision Making

IEEE Access, Journal Year: 2020, Volume and Issue: 8, P. 164899 - 164921

Published: Jan. 1, 2020

Context and Background:Complex fuzzy theory has a strong practical implication in many real-world applications. Complex Fuzzy Inference System (CFIS) is powerful technique to overcome the challenges of uncertain, periodic data. However, question raised for CFIS: How can we deduce predict result case there little knowledge about data information rule base? This significance because real applications do not have enough base inference so that performance systems may be low. Thus, it necessary an approximate reasoning method represent derive final results. Motivation: Recently, Mamdani (M-CFIS) been proposed with specific mechanism according type. A new improvement so-called Rule Reduction (M-CFIS-R) designed utilize granular computing complex similarity measures reduce as gain better decision-making problems. However M-CFIS-R, testing are checked by matching each base, which leads high cost computational time. Besides, if contain records inferred output cannot generated. happens commerce small at time creation needs feed rules. Methodology: In order handle those issues, this article first proposes Knowledge Graph terms linguistic labels their relationships set. An adjacent matrix generated inference. When record Testing dataset given, would fuzzified labelled. Each component called Fast Search Algorithm. Then, label Max-Min operator. also propose four extensions including Sugeno Systems, Tsukamoto Measures Integrals M-CFIS-R. Results: The experiments on UCI Machine Learning datasets show classifies samples correctly M-CFIS-R very lower run (6.45 times average). performed through tests via 2 main scenarios. Conclusion: system good reducing acceptable accuracy. It ability work having limited base.

Language: Английский

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Topic analysis and development in knowledge graph research: A bibliometric review on three decades

Neurocomputing, Journal Year: 2021, Volume and Issue: 461, P. 497 - 515

Published: June 17, 2021

Language: Английский

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Knowledge graph enrichment from clinical narratives using NLP, NER, and biomedical ontologies for healthcare applications

International Journal of Information Technology, Journal Year: 2023, Volume and Issue: 15(1), P. 53 - 65

Published: Jan. 1, 2023

Language: Английский

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A utility-based machine learning-driven personalized lifestyle recommendation for cardiovascular disease prevention

Journal of Biomedical Informatics, Journal Year: 2023, Volume and Issue: 141, P. 104342 - 104342

Published: March 22, 2023

Language: Английский

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Enhancing Hypoglycemia Prediction in Type 1 Diabetes Through Semantic Knowledge Integration and Machine Learning Optimization

Lecture notes in computer science, Journal Year: 2025, Volume and Issue: unknown, P. 33 - 44

Published: Jan. 1, 2025

Language: Английский

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Navigating the Nebula: A Methodology for Expert-Focused Design Study in Complex, Highly Regulated and Stakeholder-Rich Domains. The Case of Ai-Empowered Clinical Data Systems

Published: Jan. 1, 2025

Language: Английский

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Subgraph2vec: A Random Walk-Based Algorithm for Embedding Knowledge Graphs

Communications in computer and information science, Journal Year: 2025, Volume and Issue: unknown, P. 16 - 25

Published: Jan. 1, 2025

Language: Английский

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