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 <channel rdf:about="http://ebiquity.umbc.edu//tags/html/?t=frames">
  <cc:license rdf:resource="http://creativecommons.org/licenses/by/2.0/" />
  <title><![CDATA[UMBC ebiquity RSS Tag Search]]></title>
  <link><![CDATA[http://ebiquity.umbc.edu//tags/html/?t=frames]]></link>
  <description><![CDATA[UMBC ebiquity RSS Tag Search for frames]]></description>
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      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/971/Convolutional-LSTM-for-Planetary-Boundary-Layer-Height-PBLH-Prediction"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/944/Cyber-Attacks-on-Smart-Farming-Infrastructure"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/910/Joint-Models-to-Refine-Knowledge-Graphs"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/871/Joint-Models-to-Refine-Knowledge-Graphs"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/840/Team-UMBC-FEVER-Claim-verification-using-Semantic-Lexical-Resources"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/842/SURFACE-Semantically-Rich-Fact-Validation-with-Explanations"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/866/SURFACE-Semantically-Rich-Fact-Validation-with-Explanations"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/819/A-Unified-Bayesian-Model-of-Scripts-Frames-and-Language-"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/569/JETS-Achieving-Completeness-through-Coverage-and-Closure"/>
      <rdf:li resource="http://ebiquity.umbc.edu/resource/html/id/392/Sixty-years-of-knowledge-graphs-for-language-understanding"/>
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 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/971/Convolutional-LSTM-for-Planetary-Boundary-Layer-Height-PBLH-Prediction">
  <title><![CDATA[Convolutional LSTM for Planetary Boundary Layer Height (PBLH) Prediction]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/971/Convolutional-LSTM-for-Planetary-Boundary-Layer-Height-PBLH-Prediction</link>
  <description><![CDATA[We describe new work that uses deep learning to learn temporal changes in Planetary Boundary Layer Height (PBLH). This work is performed in conjunction with a deep edge detection method that identifies edges in imagery based on ceilometer backscatter signal from LIDAR observations. We implement a convolutional Long Short Term Memory (LSTM) to predict small temporal changes in PBLH estimates.  In the presence of rain, clouds, and other unfavorable conditions, PBLH heights are challenging to es...]]></description>
  <dc:date>2021-03-22</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/944/Cyber-Attacks-on-Smart-Farming-Infrastructure">
  <title><![CDATA[Cyber Attacks on Smart Farming Infrastructure]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/944/Cyber-Attacks-on-Smart-Farming-Infrastructure</link>
  <description><![CDATA[Smart farming also known as precision agriculture is gaining more traction for its promising potential to fulfill increasing global food demand and supply. In a smart farm, technologies and connected devices are used in a variety of ways, from finding the real-time status of crops and soil moisture content to deploying drones to assist with tasks such as applying pesticide spray. However, the use of heterogeneous internet-connected devices has introduced numerous vulnerabilities within the sm...]]></description>
  <dc:date>2020-10-15</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/910/Joint-Models-to-Refine-Knowledge-Graphs">
  <title><![CDATA[Joint Models to Refine Knowledge Graphs]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/910/Joint-Models-to-Refine-Knowledge-Graphs</link>
  <description><![CDATA[A knowledge graph can be viewed as a structural representation of beliefs with nodes and edges in which the nodes represent real-world entities or events and the edges are relations believed to hold between pairs of entities. Multiple levels of processes are involved in extracting such knowledge graphs from natural language text, starting with reading and understanding the text, then constructing a graph of the entities found and the relations between them, and inferring missing relations tha...]]></description>
  <dc:date>2019-12-01</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/871/Joint-Models-to-Refine-Knowledge-Graphs">
  <title><![CDATA[Joint Models to Refine Knowledge Graphs]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/871/Joint-Models-to-Refine-Knowledge-Graphs</link>
  <description><![CDATA[A knowledge graph can be viewed as a structural representation of beliefs with nodes and edges in which the nodes represent real-world entities or events and the edges are relations believed to hold between pairs of entities. Multiple levels of processes are involved in extracting such knowledge graphs from natural language text, starting with reading and understanding the text, then constructing a graph of the entities found and the relations between them, and inferring missing relations tha...]]></description>
  <dc:date>2019-10-25</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/840/Team-UMBC-FEVER-Claim-verification-using-Semantic-Lexical-Resources">
  <title><![CDATA[Team UMBC-FEVER: Claim verification using Semantic Lexical Resources]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/840/Team-UMBC-FEVER-Claim-verification-using-Semantic-Lexical-Resources</link>
  <description><![CDATA[We describe our system used in the 2018 FEVER shared task. The system employed a frame-based information retrieval approach to select Wikipedia sentences providing evidence and a two-layer multilayer perceptron to classify a claim as correct or not. Our submission achieved a score of 0.3966 on the Evidence F1 metric with an accuracy of 44.79% and a FEVER score of 0.2628 F1 points.]]></description>
  <dc:date>2018-11-01</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/842/SURFACE-Semantically-Rich-Fact-Validation-with-Explanations">
  <title><![CDATA[SURFACE: Semantically Rich Fact Validation with Explanations]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/842/SURFACE-Semantically-Rich-Fact-Validation-with-Explanations</link>
  <description><![CDATA[Judging the veracity of a sentence making one or more claims is an important and challenging problem with many dimensions. The recent FEVER task asked participants to classify input sentences as either SUPPORTED, REFUTED or NotEnoughInfo using Wikipedia as a source of true facts. SURFACE does this task and explains its decision through a selection of sentences from the trusted source. Our multi-task neural approach uses semantic lexical frames from FrameNet to jointly (i) find relevant eviden...]]></description>
  <dc:date>2018-11-01</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/866/SURFACE-Semantically-Rich-Fact-Validation-with-Explanations">
  <title><![CDATA[SURFACE: Semantically Rich Fact Validation with Explanations]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/866/SURFACE-Semantically-Rich-Fact-Validation-with-Explanations</link>
  <description><![CDATA[Judging the veracity of a sentence making one or more claims is an important and challenging problem with many dimensions. The recent FEVER task asked participants to classify input sentences as either SUPPORTED, REFUTED or NotEnoughInfo using Wikipedia as a source of true facts. SURFACE does this task and explains its decision through a selection of sentences from the trusted source. Our multi-task neural approach uses semantic lexical frames from FrameNet to jointly (i) find relevant eviden...]]></description>
  <dc:date>2018-10-31</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/819/A-Unified-Bayesian-Model-of-Scripts-Frames-and-Language-">
  <title><![CDATA[A Unified Bayesian Model of Scripts, Frames and Language.]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/819/A-Unified-Bayesian-Model-of-Scripts-Frames-and-Language-</link>
  <description><![CDATA[We present the first probabilistic model to capture all levels of the Minsky Frame structure, with the goal of corpus-based induction of scenario definitions. Our model unifies prior efforts in discourse-level modeling with that of Fill-more's related notion of frame, as captured in sentence-level, FrameNet semantic parses; as part of this, we resurrect the coupling among Minsky's frames, Schank's scripts and Fill-more's frames, as originally laid out by those authors. Empirically, our approa...]]></description>
  <dc:date>2016-02-12</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/569/JETS-Achieving-Completeness-through-Coverage-and-Closure">
  <title><![CDATA[JETS: Achieving Completeness through Coverage and Closure]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/569/JETS-Achieving-Completeness-through-Coverage-and-Closure</link>
  <description><![CDATA[Work in progress on JETS, the successor to PLANES, is described. JETS is a natural language question answering system intended to interface users with a large relational database. The architecture is designed to extend the conceptual coverage of JETS to better meet the conversational and database usage requirements of users. The implementation of JETS is designed to gain a high degree of closure over concept manipulation, contributing to a solution to the problems of perspicuity and scale. Sp...]]></description>
  <dc:date>1979-08-20</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/resource/html/id/392/Sixty-years-of-knowledge-graphs-for-language-understanding">
  <title><![CDATA[Sixty years of knowledge graphs for language understanding]]></title>
  <link>http://ebiquity.umbc.edu/resource/html/id/392/Sixty-years-of-knowledge-graphs-for-language-understanding</link>
  <description><![CDATA[There is a long history of using structured knowledge of one kind or another to support AI tasks, especially ones involving natural language understanding. Over the years, the names and details have changed, from semantic networks to frames to logic programs to databases to expert systems to knowledge bases to the semantic web and currently to knowledge graphs. However, a common thread is that an organized representation of knowledge that can be queried and evolved is a core component of many...]]></description>
  <dc:date>2019-09-19</dc:date>
 </item>
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