Search (4 results, page 1 of 1)

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Abstract

Predicting a researcher's knowledge trajectories beyond their current foci can leverage potential inter-/cross-/multi-disciplinary interactions to achieve exploratory innovation. In this study, we present a method of diffusion-based network analytics for knowledge trajectory recommendation. The method begins by constructing a heterogeneous bibliometric network consisting of a co-topic layer and a co-authorship layer. A novel link prediction approach with a diffusion strategy is then used to capture the interactions between social elements (e.g., collaboration) and knowledge elements (e.g., technological similarity) in the process of exploratory innovation. This diffusion strategy differentiates the interactions occurring among homogeneous and heterogeneous nodes in the heterogeneous bibliometric network and weights the strengths of these interactions. Two sets of experiments-one with a local dataset and the other with a global dataset-demonstrate that the proposed method is prior to 10 selected baselines in link prediction, recommender systems, and upstream graph representation learning. A case study recommending knowledge trajectories of information scientists with topical hierarchy and explainable mediators reveals the proposed method's reliability and potential practical uses in broad scenarios.

Date

22. 6.2023 18:07:12

Type

a

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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.

Type

a

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Abstract

There is no information search system can assist young children's known picture book search needs since the information is not organized according to their cognitive abilities and needs. Therefore, this study explored young children's known picture book search strategies and extracted picture book search elements by simulating a search scenario and playing a picture book search game. The study found 29 elements children used to search for known picture books. Then, these elements are classified into three dimensions: The first dimension is the concept category of an element. The second dimension is an element's status in the story. The third dimension indicates where an element appears in a picture book. Additionally, it revealed a young children's general search strategy: Children first use auditory elements that they hear from the adults during reading. After receiving error returns, they add visual elements that they see by themselves in picture books. The findings can not only help to understand young children's known-item search and reformulation strategies during searching but also provide theoretical support for the development of a picture book information organization schema in the search system.

Type

a

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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.