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Abstract

Ontologies and Big Data Considerations for Effective Intelligence is a key source on the latest advancements in multidisciplinary research methods and applications and examines effective techniques for managing and utilizing information resources. Featuring extensive coverage across a range of relevant perspectives and topics, such as visual analytics, spatial databases, retrieval systems, and ontology models, this book is ideally designed for researchers, graduate students, academics, and industry professionals seeking ways to optimize knowledge management processes.

Content

Inhalt: Interactive visual analytics of big data / Carson K.-S. Leung [and 4 others] --Knowledge discovery for large databases in education institutes / Robab Saadatdoost [and 3 others] --Spatial databases: an overview / Grace L. Samson [and 3 others] -- The impact of the mode of data representation for the result quality of the detection and filtering of spam / Reda Mohamed Hamou, Abdelmalek Amine, Moulay Tahar -- Debunking intermediary censorship framework in social media via a content retrieval and classification software / Baramee Navanopparatskul, Sukree Sinthupinyo, Pirongrong Ramasoota -- Semantic approach to web-based discovery of unknowns to enhance intelligence gathering / Natalia Danilova, David Stupples -- Securing financial XML transactions using intelligent fuzzy classification techniques: a smart fuzzy-based model for financial XML transactions security using XML encryption / Faisal Tawfiq Ammari, Joan Lu -- Building a secured XML real-time interactive data exchange architecture / Yousef E. Rabadi, Joan Lu -- User query enhancement for behavioral targeting / Wei Xiong, Y. F. Brook Wu -- A generic model of ontology to visualize information science domain (OIS) / Ahlam F. Sawsaa, Joan Lu -- Research background on ontology / Ahlam F. Sawsaa, Joan Lu -- Methodology of creating ontology of information science (OIS) / Ahlam F. Sawsaa, Joan Lu -- Modelling design of OIS ontology / Ahlam F. Sawsaa, Joan Lu Findings for ontology in IS and discussion / Ahlam F. Sawsaa, Joan Lu -- Final remarks for the investigation in ontology in IS and possible future directions / Ahlam F. Sawsaa, Joan Lu.

LCSH

Semantic Web.

Subject

Semantic Web.

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Date

22. 6.2023 18:07:12

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Abstract

Whereas traditional science maps emphasize citation statistics and static relationships, this paper presents a term-based method to identify and visualize the evolutionary pathways of scientific topics in a series of time slices. First, we create a data preprocessing model for accurate term cleaning, consolidating, and clustering. Then we construct a simulated data streaming function and introduce a learning process to train a relationship identification function to adapt to changing environments in real time, where relationships of topic evolution, fusion, death, and novelty are identified. The main result of the method is a map of scientific evolutionary pathways. The visual routines provide a way to indicate the interactions among scientific subjects and a version in a series of time slices helps further illustrate such evolutionary pathways in detail. The detailed outline offers sufficient statistical information to delve into scientific topics and routines and then helps address meaningful insights with the assistance of expert knowledge. This empirical study focuses on scientific proposals granted by the United States National Science Foundation, and demonstrates the feasibility and reliability. Our method could be widely applied to a range of science, technology, and innovation policy research, and offer insight into the evolutionary pathways of scientific activities.