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 <channel rdf:about="http://ebiquity.umbc.edu//tags/html/?t=topic+modeling">
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  <title><![CDATA[UMBC ebiquity RSS Tag Search]]></title>
  <link><![CDATA[http://ebiquity.umbc.edu//tags/html/?t=topic+modeling]]></link>
  <description><![CDATA[UMBC ebiquity RSS Tag Search for topic modeling]]></description>
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      <rdf:li resource="http://ebiquity.umbc.edu/event/html/id/478/Topic-Modeling-for-RDF-Graphs"/>
      <rdf:li resource="http://ebiquity.umbc.edu/event/html/id/372/Social-media-analytics"/>
      <rdf:li resource="http://ebiquity.umbc.edu/event/html/id/355/Clustering-short-status-messages-a-topic-model-based-approach"/>
      <rdf:li resource="http://ebiquity.umbc.edu/project/html/id/108/Modelling-the-evolution-of-climate-change-research"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/1187/Evaluating-Causal-AI-Techniques-for-Health-Misinformation-Detection"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/981/Understanding-Cybersecurity-Threat-Trends-through-Dynamic-Topic-Modeling"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/963/A-Semantically-Rich-Framework-for-Knowledge-Representation-of-Code-of-Federal-Regulations-CFR-"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/869/Variational-Autoencoders-using-D-Wave-Quantum-Annealing"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/831/Ontology-Grounded-Topic-Modeling-for-Climate-Science-Research"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/810/Discovering-Scientific-Influence-using-Cross-Domain-Dynamic-Topic-Modeling"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/815/Dynamic-Data-Assimilation-for-Topic-Modeling-DDATM-"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/770/Modeling-the-Evolution-of-Climate-Change-Assessment-Research-Using-Dynamic-Topic-Models-and-Cross-Domain-Divergence-Maps"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/769/Advanced-Large-Scale-Cross-Domain-Temporal-Topic-Modeling-Algorithms-to-Infer-the-Influence-of-Recent-Research-on-IPCC-Assessment-Reports"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/768/Dynamic-Topic-Modeling-to-Infer-the-Influence-of-Research-Citations-on-IPCC-Assessment-Reports"/>
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 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/478/Topic-Modeling-for-RDF-Graphs">
  <title><![CDATA[Topic Modeling for RDF Graphs]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/478/Topic-Modeling-for-RDF-Graphs</link>
  <description><![CDATA[Topic models are widely used to thematically describe a collection of
text documents and have become an important technique for systems that
measure document similarity for classification, clustering,
segmentation, entity linking and more.  While they have been applied
to some non-text domains, their use for semi-structured graph data,
such as RDF, has been less explored.  We present a framework for
applying topic modeling to RDF graph data and describe how it can be
used in a number o...]]></description>
  <dc:date>2015-09-21</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/372/Social-media-analytics">
  <title><![CDATA[Social media analytics]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/372/Social-media-analytics</link>
  <description><![CDATA[This week's ebiquity meeting will focus on social media and two research efforts that are part of our Relief Social Media project.

Mohit Kewalramani will present the topic that he is addressing in his MS research.  An important task in analyzing highly networked information sources like Twitter is to identify communities that are formed. A community can be defined as a group of nodes that have more links within the set than outside it. We plan to present a technique for detecting communiti...]]></description>
  <dc:date>2010-10-12</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/355/Clustering-short-status-messages-a-topic-model-based-approach">
  <title><![CDATA[Clustering short status messages: a topic model based approach]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/355/Clustering-short-status-messages-a-topic-model-based-approach</link>
  <description><![CDATA[Recently, there has been an exponential rise in the use of online social media systems like Twitter and Facebook. Even more usage has been observed during events related to natural disasters, political turmoil or other such crises. Tweets or status messages are short and may not carry enough contextual clues. Hence, applying traditional natural language processing algorithms on such data is challenging. Topic model is a popular method for modeling term frequency occurrences for documents in a...]]></description>
  <dc:date>2010-07-26</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/project/html/id/108/Modelling-the-evolution-of-climate-change-research">
  <title><![CDATA[Modelling the evolution of climate change research]]></title>
  <link>http://ebiquity.umbc.edu/project/html/id/108/Modelling-the-evolution-of-climate-change-research</link>
  <description><![CDATA[We are developing algorithms using dynamic topic modeling to understand influence and predict future trends in a scientific discipline. As an initial use case, we are applying this to climate change and use assessment reports of the Intergovernmental Panel on Climate Change (IPCC) and the papers they cite. Since 1990, an IPCC report has been published every five years that includes four separate volumes, each of which has many chapters. Each report cites tens of thousands of research papers, ...]]></description>
  <dc:date>2015-01-01</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/1187/Evaluating-Causal-AI-Techniques-for-Health-Misinformation-Detection">
  <title><![CDATA[Evaluating Causal AI Techniques for Health  Misinformation Detection]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/1187/Evaluating-Causal-AI-Techniques-for-Health-Misinformation-Detection</link>
  <description><![CDATA[Abstract—The proliferation of health misinformation on social media, particularly regarding chronic conditions such as diabetes, hypertension, and obesity, poses significant public health risks. This study evaluates the feasibility of leveraging Natural Language Processing (NLP) techniques for real-time misinformation detection and classification, focusing on Reddit discussions. Using logistic regression as a baseline model, supplemented by Latent Dirichlet Allocation (LDA) for topic modeli...]]></description>
  <dc:date>2025-03-17</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/981/Understanding-Cybersecurity-Threat-Trends-through-Dynamic-Topic-Modeling">
  <title><![CDATA[Understanding Cybersecurity Threat Trends through Dynamic Topic Modeling]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/981/Understanding-Cybersecurity-Threat-Trends-through-Dynamic-Topic-Modeling</link>
  <description><![CDATA[Cybersecurity threats continue to increase and are impacting almost all aspects of modern life. Being aware of how vulnerabilities and their exploits are changing gives helpful insights into combating new threats. Applying dynamic topic modeling to a timestamped cybersecurity document collection shows how the significance and details of concepts found in them are evolving.  We correlate two different temporal corpora, one with reports about specific exploits and another with research-oriented...]]></description>
  <dc:date>2021-06-01</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/963/A-Semantically-Rich-Framework-for-Knowledge-Representation-of-Code-of-Federal-Regulations-CFR-">
  <title><![CDATA[A Semantically Rich Framework for Knowledge Representation of Code of Federal Regulations (CFR)]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/963/A-Semantically-Rich-Framework-for-Knowledge-Representation-of-Code-of-Federal-Regulations-CFR-</link>
  <description><![CDATA[Federal government agencies and organizations doing business with them have to adhere to the Code of Federal Regulations (CFR). The CFRs are currently available as large text documents that are not machine-processable and so require extensive manual effort to parse and comprehend, especially when sections cross-reference topics spread across various titles. We have developed a novel framework to automatically extract knowledge from CFRs and represent it using a semantically rich knowledgegrap...]]></description>
  <dc:date>2020-12-01</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/869/Variational-Autoencoders-using-D-Wave-Quantum-Annealing">
  <title><![CDATA[Variational Autoencoders using D-Wave Quantum Annealing]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/869/Variational-Autoencoders-using-D-Wave-Quantum-Annealing</link>
  <description><![CDATA[Exploring the use of deep learning algorithms on the quantum computer will provide insight into how the quantum computer, in particular quantum annealing, can be applied to climate related research to accelerate the learning process. Current research has explored using Restricted Boltzmann Machines (RBM) using D-Wave's quantum annealer. This work has explored problems such as MNIST image recognition tasks. In addition, another body of research has explored variational inference methods using ...]]></description>
  <dc:date>2018-12-10</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/831/Ontology-Grounded-Topic-Modeling-for-Climate-Science-Research">
  <title><![CDATA[Ontology-Grounded Topic Modeling for Climate Science Research]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/831/Ontology-Grounded-Topic-Modeling-for-Climate-Science-Research</link>
  <description><![CDATA[In scientific disciplines where research findings have a strong impact on society, reducing the amount of time it takes to understand, synthesize, and exploit the research is invaluable.  Topic modeling is an effective technique for summarizing a collection of documents to find the main themes among them and to classify other documents that have a similar mixture of co-occurring words. We show how grounding a topic model with an ontology, extracted from a glossary of important domain phrases,...]]></description>
  <dc:date>2018-10-08</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/810/Discovering-Scientific-Influence-using-Cross-Domain-Dynamic-Topic-Modeling">
  <title><![CDATA[Discovering Scientific Influence using Cross-Domain Dynamic Topic Modeling]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/810/Discovering-Scientific-Influence-using-Cross-Domain-Dynamic-Topic-Modeling</link>
  <description><![CDATA[We describe an approach using dynamic topic
modeling to model influence and predict future trends in
a scientific discipline. Our study focuses on climate change
and uses assessment reports of the Intergovernmental Panel
on Climate Change (IPCC) and the papers they cite. Since
1990, an IPCC report has been published every five years
that includes four separate volumes, each of which has many
chapters. Each report cites tens of thousands of research
papers, which comprise a correlated ...]]></description>
  <dc:date>2017-12-11</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/815/Dynamic-Data-Assimilation-for-Topic-Modeling-DDATM-">
  <title><![CDATA[Dynamic Data Assimilation for Topic Modeling (DDATM)]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/815/Dynamic-Data-Assimilation-for-Topic-Modeling-DDATM-</link>
  <description><![CDATA[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 influ...]]></description>
  <dc:date>2017-07-31</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/770/Modeling-the-Evolution-of-Climate-Change-Assessment-Research-Using-Dynamic-Topic-Models-and-Cross-Domain-Divergence-Maps">
  <title><![CDATA[Modeling the Evolution of Climate Change Assessment Research Using Dynamic Topic Models and Cross-Domain Divergence Maps]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/770/Modeling-the-Evolution-of-Climate-Change-Assessment-Research-Using-Dynamic-Topic-Models-and-Cross-Domain-Divergence-Maps</link>
  <description><![CDATA[Climate change is an important social issue and the subject of much research, both to understand the history of the Earth's changing climate and to foresee what changes to expect in the future. Approximately every five years, starting in 1990, the Intergovernmental Panel on Climate Change (IPCC) publishes a set of reports that cover the current state of climate change research, how this research will impact the world, risks, and approaches to mitigate the effects of climate change. Each repor...]]></description>
  <dc:date>2017-03-27</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/769/Advanced-Large-Scale-Cross-Domain-Temporal-Topic-Modeling-Algorithms-to-Infer-the-Influence-of-Recent-Research-on-IPCC-Assessment-Reports">
  <title><![CDATA[Advanced Large Scale Cross Domain Temporal Topic Modeling Algorithms to Infer the Influence of Recent Research on IPCC Assessment Reports]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/769/Advanced-Large-Scale-Cross-Domain-Temporal-Topic-Modeling-Algorithms-to-Infer-the-Influence-of-Recent-Research-on-IPCC-Assessment-Reports</link>
  <description><![CDATA[One way of understanding the evolution of science within a particular scientific discipline is by studying the temporal influences that research publications had on that discipline. We provide a methodology for conducting such an analysis by employing cross-domain topic modeling and local cluster mappings of those publications with the historical texts to understand exactly when and how they influenced the discipline. We apply our method to the Intergovernmental Panel on Climate Change (IPCC)...]]></description>
  <dc:date>2016-12-12</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/768/Dynamic-Topic-Modeling-to-Infer-the-Influence-of-Research-Citations-on-IPCC-Assessment-Reports">
  <title><![CDATA[Dynamic Topic Modeling to Infer the Influence of Research Citations on IPCC Assessment Reports]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/768/Dynamic-Topic-Modeling-to-Infer-the-Influence-of-Research-Citations-on-IPCC-Assessment-Reports</link>
  <description><![CDATA[A common Big Data problem is the need to integrate large temporal data sets from various data sources into one comprehensive structure. Having the ability to correlate evolving facts between data sources can be especially useful in supporting a number of desired application functions such as inference and influence identification. As a real world application we use climate change publications based on the Intergovernmental Panel on Climate Change, which publishes climate change assessment rep...]]></description>
  <dc:date>2016-12-05</dc:date>
 </item>
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