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  <description><![CDATA[UMBC ebiquity RSS Tag Search for semantic search]]></description>
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      <rdf:li resource="http://ebiquity.umbc.edu/event/html/id/310/Problems-in-Semantic-Search"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/1071/Knowledge-Graph-driven-Tabular-Data-Discovery-from-Scientific-Documents"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/1057/Towards-Semantic-Exploration-of-Tables-in-Scientific-Documents"/>
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 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/310/Problems-in-Semantic-Search">
  <title><![CDATA[Problems in Semantic Search]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/310/Problems-in-Semantic-Search</link>
  <description><![CDATA[Swoogle is UMBC’s home grown semantic web search engine. It crawls the web for Semantic Web documents.


Varish Mulwad and Krishnamurthy Koduvayur Viswanathan  will start the talk with a description of how Swoogle works. We will also take a some other semantic web search engines: Sindice, Watson, Falcon and SWSE. We will end the session by talking about a couple of ideas to improve Swoogle.


Participate remotely via dimdim.]]></description>
  <dc:date>2009-09-08</dc:date>
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 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/1071/Knowledge-Graph-driven-Tabular-Data-Discovery-from-Scientific-Documents">
  <title><![CDATA[Knowledge Graph-driven Tabular Data Discovery from Scientific Documents]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/1071/Knowledge-Graph-driven-Tabular-Data-Discovery-from-Scientific-Documents</link>
  <description><![CDATA[Synthesizing information from collections of tables embedded within scientific and technical documents is increasingly critical to emerging knowledge-driven applications. Given their structural heterogeneity, highly domain-specific content, and diffuse context, inferring a precise semantic understanding of such tables is traditionally better accomplished through linking tabular content to concepts and entities in reference knowledge graphs. However, existing tabular data discovery systems are...]]></description>
  <dc:date>2023-09-01</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/1057/Towards-Semantic-Exploration-of-Tables-in-Scientific-Documents">
  <title><![CDATA[Towards Semantic Exploration of Tables in Scientific Documents]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/1057/Towards-Semantic-Exploration-of-Tables-in-Scientific-Documents</link>
  <description><![CDATA[Structured data artifacts such as tables are widely used in scientific literature to organize and concisely communicate important statistical information. Discovering relevant information in these tables remains a significant challenge owing to their structural heterogeneity, dense and often implicit semantics, and diffuse context. This paper describes how we leverage semantic technologies to enable technical experts to search and explore tabular data embedded within scientific documents. We ...]]></description>
  <dc:date>2023-05-28</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/782/Thinking-Fast-and-Slow-Combining-Vector-Spaces-and-Knowledge-Graphs">
  <title><![CDATA[Thinking, Fast and Slow: Combining Vector Spaces and Knowledge Graphs]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/782/Thinking-Fast-and-Slow-Combining-Vector-Spaces-and-Knowledge-Graphs</link>
  <description><![CDATA[Knowledge graphs and vector space models are robust knowledge representation techniques with individual strengths and weaknesses. Vector space models excel at determining similarity between concepts, but are severely constrained when evaluating complex dependency relations and other logic-based operations that are a strength of knowledge graphs. We describe the VKG structure that helps unify knowledge graphs and vector representation of entities, and enables powerful inference methods and sea...]]></description>
  <dc:date>2017-08-30</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/726/Visualization-of-Pain-Severity-Events-Using-Semantic-Structures">
  <title><![CDATA[Visualization of Pain Severity Events Using Semantic Structures]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/726/Visualization-of-Pain-Severity-Events-Using-Semantic-Structures</link>
  <description><![CDATA[Physicians are often required to make critical medical decisions that may be based on previous events in the patient's health history.  However, these events may be very difficult to locate in the patient record due to the large volume of unstructured textual data in the patient's chart.  Even when the chart is housed in an electronic health record (EMR) system, keyword search within the chart may produce many results that are not relevant or that may overlook related expressions and concepts...]]></description>
  <dc:date>2016-02-03</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/resource/html/id/278/Problems-in-Semantic-Search">
  <title><![CDATA[Problems in Semantic Search]]></title>
  <link>http://ebiquity.umbc.edu/resource/html/id/278/Problems-in-Semantic-Search</link>
  <dc:date>2009-09-04</dc:date>
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