Dynamic Data Assimilation For Topic Modeling Ddatm
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Emerging Topics in Semantic Technologies
This book includes a selection of thoroughly refereed papers accepted at the Satellite Events of the 17th Internal Semantic Web Conference, ISWC 2018, held in Monterey, CA in October 2018. The key areas addressed by these events include the core Semantic Web technologies such as knowledge graphs and scalable knowledge base systems, ontology design and modelling, semantic deep learning and statistics. Furthermore, several novel applications of semantic technologies to the topics of Internet of Things (IoT), healthcare, social media and social good are discussed. Finally, important topics at the interface of the Semantic Web technologies and their human users are addressed, including visualization and interaction paradigms for Web Data as well as crowdsourcing applications.
Dynamic Data Assimilation for Topic Modeling (DDATM)
Understanding how a particular discipline such as climate science evolves over time has received renewed interest. By understanding this evolution, predicting the future direction of the discipline becomes more achievable. Dynamic Topic Modeling (DTM) has been applied to a number of disciplines to model topic evolution as a means to learn how a particular scientific discipline and its underlying concepts are changing. Understanding how a discipline evolves, and its internal and external influences, can be complicated by how the information retrieved over time is integrated. There are different techniques used to integrate sources of information, however, less research has been dedicated to understanding how to integrate these sources over time. Data assimilation is commonly used in a number of scientific disciplines to both understand and make predictions of various phenomena, using numerical models and assimilated observational data over time.
Dynamic Data Assimilation
Author: Dinesh G. Harkut
language: en
Publisher: BoD – Books on Demand
Release Date: 2020-10-28
Data assimilation is a process of fusing data with a model for the singular purpose of estimating unknown variables. It can be used, for example, to predict the evolution of the atmosphere at a given point and time. This book examines data assimilation methods including Kalman filtering, artificial intelligence, neural networks, machine learning, and cognitive computing.