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<!--
	This ontology document is licensed under the Creative Commons
	Attribution License. To view a copy of this license, visit
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	Creative Commons, 559 Nathan Abbott Way, Stanford, California
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 <channel rdf:about="http://ebiquity.umbc.edu//tags/html/?t=entity+disambiguation">
  <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=entity+disambiguation]]></link>
  <description><![CDATA[UMBC ebiquity RSS Tag Search for entity disambiguation]]></description>
  <items>
    <rdf:Seq>
      <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/473/Taming-Wild-Big-Data"/>
      <rdf:li resource="http://ebiquity.umbc.edu/event/html/id/420/Masters-Thesis-Research-Update-Anurag-Dibjyajyoti-and-Sumit"/>
      <rdf:li resource="http://ebiquity.umbc.edu/event/html/id/413/Masters-Thesis-Research-Proposal-Entity-Linking-and-Disambiguation-for-Smartphone-platforms"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/714/Topic-Modeling-for-RDF-Graphs"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/717/Entity-Disambiguation-for-Wild-Big-Data-Using-Multi-Level-Clustering"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/672/Taming-Wild-Big-Data"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/492/Entity-Disambiguation-for-Knowledge-Base-Population"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/386/Scalable-semantic-analytics-on-social-networks-for-addressing-the-problem-of-conflict-of-interest-detection"/>
    </rdf:Seq>
  </items>
 </channel>
 <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/473/Taming-Wild-Big-Data">
  <title><![CDATA[Taming Wild Big Data]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/473/Taming-Wild-Big-Data</link>
  <description><![CDATA[In this week's Ebiquity meeting, Jennifer Sleeman will talk about "Taming Wild Big Data".

Wild Big Data is data that is hard to extract, understand, and use due to its heterogeneous nature and volume. It typically comes without a schema, is obtained from multiple sources and provides a challenge for information extraction and integration. We describe a way to subduing Wild Big Data that uses techniques and resources that are popular for processing natural language text. The approach is...]]></description>
  <dc:date>2014-11-12</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/420/Masters-Thesis-Research-Update-Anurag-Dibjyajyoti-and-Sumit">
  <title><![CDATA[Masters Thesis Research Update - Anurag, Dibjyajyoti and Sumit]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/420/Masters-Thesis-Research-Update-Anurag-Dibjyajyoti-and-Sumit</link>
  <description><![CDATA[In this week's lab meeting, Anurag Korde, Dibyajyoti Ghosh and Sumit More will give an update on how their Masters thesis research is progressing. 

Anurag will talk about "Entity Linking and Disambiguation for Smartphone platforms". With increasing number of social networks, smartphones and applications there is a need of creating a framework for information integration across social networks and applications and create an unified record for each person/entity. The problem in information i...]]></description>
  <dc:date>2011-12-06</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/413/Masters-Thesis-Research-Proposal-Entity-Linking-and-Disambiguation-for-Smartphone-platforms">
  <title><![CDATA[Masters Thesis Research Proposal : Entity Linking and Disambiguation for Smartphone platforms]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/413/Masters-Thesis-Research-Proposal-Entity-Linking-and-Disambiguation-for-Smartphone-platforms</link>
  <description><![CDATA[With increasing number of social networks, smartphones and applications there is a need of creating a framework for information integration across social networks and applications and create an unified record for each person/entity. The problem in information integration is that of entity disambiguation. It determines whether two objects in an ontology refer to same real world object. This will enable system to learn a large amount of contextual information. I will be extending]]></description>
  <dc:date>2011-10-11</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/714/Topic-Modeling-for-RDF-Graphs">
  <title><![CDATA[Topic Modeling for RDF Graphs]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/714/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 of link...]]></description>
  <dc:date>2015-10-12</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/717/Entity-Disambiguation-for-Wild-Big-Data-Using-Multi-Level-Clustering">
  <title><![CDATA[Entity Disambiguation for Wild Big Data Using Multi-Level Clustering]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/717/Entity-Disambiguation-for-Wild-Big-Data-Using-Multi-Level-Clustering</link>
  <description><![CDATA[When RDF instances represent the same entity they are said
to corefer. For example, two nodes from different RDF graphs 1 both refer
to same individual, musical artist James Brown. Disambiguating entities
is essential for knowledge base population and other tasks that result
in integration or linking of data. Often however, entity instance data
originates from different sources and can be represented using differ-
ent schemas or ontologies. In the age of Big Data, data can have other
c...]]></description>
  <dc:date>2015-10-12</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/672/Taming-Wild-Big-Data">
  <title><![CDATA[Taming Wild Big Data]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/672/Taming-Wild-Big-Data</link>
  <description><![CDATA[Wild Big Data is data that is hard to extract, understand, and use due to its heterogeneous nature and volume. It typically comes without a schema, is obtained from multiple sources and provides a challenge for information extraction and integration. We describe a way to subduing Wild Big Data that uses techniques and resources that are popular for processing natural language text. The approach is applicable to data that is presented as a graph of objects and relations between them and to tab...]]></description>
  <dc:date>2014-11-13</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/492/Entity-Disambiguation-for-Knowledge-Base-Population">
  <title><![CDATA[Entity Disambiguation for Knowledge Base Population]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/492/Entity-Disambiguation-for-Knowledge-Base-Population</link>
  <description><![CDATA[The integration of facts derived from information extraction systems into existing knowledge bases requires a system to disambiguate entity mentions in the text. This is challenging due to issues such as non-uniform variations in entity names, mention ambiguity, and entities absent from a knowledge base. We present a state of the art system for entity disambiguation that not only addresses these challenges but also scales to knowledge bases with several million entries using very little resou...]]></description>
  <dc:date>2010-08-23</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/386/Scalable-semantic-analytics-on-social-networks-for-addressing-the-problem-of-conflict-of-interest-detection">
  <title><![CDATA[Scalable semantic analytics on social networks for addressing the problem of conflict of interest detection]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/386/Scalable-semantic-analytics-on-social-networks-for-addressing-the-problem-of-conflict-of-interest-detection</link>
  <description><![CDATA[In this article, we demonstrate the applicability of semantic techniques for detection of Conflict of Interest (COI). We explain the common challenges involved in building scalable Semantic Web applications, in particular those addressing connecting-the-dots problems. We describe in detail the challenges involved in two important aspects on building Semantic Web applications, namely, data acquisition and entity disambiguation (or reference reconciliation). We extend upon our previous work, wh...]]></description>
  <dc:date>2008-02-01</dc:date>
 </item>
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