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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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 <channel rdf:about="http://ebiquity.umbc.edu//tags/html/?t=similarity+metric">
  <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=similarity+metric]]></link>
  <description><![CDATA[UMBC ebiquity RSS Tag Search for similarity metric]]></description>
  <items>
    <rdf:Seq>
      <rdf:li resource="http://ebiquity.umbc.edu/event/html/id/459/MS-defense-Social-Media-Analytics-Digital-Footprints"/>
      <rdf:li resource="http://ebiquity.umbc.edu/event/html/id/453/Social-Media-Analytics-Digital-Footprints"/>
      <rdf:li resource="http://ebiquity.umbc.edu/event/html/id/438/Masters-Thesis-Research-Update-Sandhya-Krishnan"/>
      <rdf:li resource="http://ebiquity.umbc.edu/event/html/id/397/Community-Detection-in-Twitter"/>
      <rdf:li resource="http://ebiquity.umbc.edu/event/html/id/393/PowerRelations-A-Question-Answering-System-for-DBPedia"/>
      <rdf:li resource="http://ebiquity.umbc.edu/event/html/id/368/Text-Based-Similarity-Metrics-and-Deltas-for-Semantic-Web-Graphs"/>
      <rdf:li resource="http://ebiquity.umbc.edu/event/html/id/350/Text-Based-Similarity-Metrics-and-Delta-for-Semantic-Web-Graphs"/>
      <rdf:li resource="http://ebiquity.umbc.edu/event/html/id/314/Multivariate-Time-Series-Analysis-of-Physiological-and-Clinical-Data"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/620/Identifying-and-characterizing-user-communities-on-Twitter-during-crisis-events"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/555/Community-Detection-in-Twitter"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/504/Text-Based-Similarity-Metrics-and-Delta-for-Semantic-Web-Graphs"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/494/Text-Based-Similarity-and-Delta-for-Semantic-Web-Graphs"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/497/Text-Based-Similarity-Metrics-and-Delta-for-Semantic-Web-Graphs"/>
      <rdf:li resource="http://ebiquity.umbc.edu/resource/html/id/300/Text-Based-Similarity-Metrics-and-Delta-for-Semantic-Web-Graphs"/>
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 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/459/MS-defense-Social-Media-Analytics-Digital-Footprints">
  <title><![CDATA[MS defense: Social Media Analytics: Digital Footprints]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/459/MS-defense-Social-Media-Analytics-Digital-Footprints</link>
  <description><![CDATA[In this work we describe an approach to distinguish real and impostor/ compromised accounts on social media. Compromising a user's social media account is not only a breach of security, but can also lead to dissemination of misinformation at a fast pace on social media. There have been several such high profile attacks recently, including on Twitter feeds of AP, CBS, and Delta Airlines. A fake account for the Prime Minister's Office in India was used to spread malicious rumors last year. Our ...]]></description>
  <dc:date>2013-05-13</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/453/Social-Media-Analytics-Digital-Footprints">
  <title><![CDATA[Social Media Analytics: Digital Footprints]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/453/Social-Media-Analytics-Digital-Footprints</link>
  <description><![CDATA[Social media has greatly impacted the way we communicate today. With approximately 3000 tweets/sec and 55 million FB status updates a day, it is a great way to disseminate information to users across the world.  However such a tool can also be used to disseminate misinformation in a quick and efficient manner which can have a harmful impact in multiple scenarios like national security cases, or business/marketing cases and hence needs to be curbed and kept in check. Our approach involves crea...]]></description>
  <dc:date>2013-04-15</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/438/Masters-Thesis-Research-Update-Sandhya-Krishnan">
  <title><![CDATA[Masters Thesis Research Update: Sandhya Krishnan]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/438/Masters-Thesis-Research-Update-Sandhya-Krishnan</link>
  <description><![CDATA[In this week's lab meeting Sandhya Krishnan will present her proposed thesis topic:

Abstract: Content available on social media, can be used to understand the profile, behavior and interests of a user. Text mining and information retrieval tools can be used to extract words and topics which represent the content of the particular user in a given time frame. The goal if this thesis work is to analyze content of prominent user accounts on Social media [example: social media accounts of polit...]]></description>
  <dc:date>2012-10-08</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/397/Community-Detection-in-Twitter">
  <title><![CDATA[Community Detection in Twitter]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/397/Community-Detection-in-Twitter</link>
  <description><![CDATA[Mohit Kewalramani will defend his MS thesis titled "Community Detection in Twitter".
 
Twitter has evolved into a source of social, political and real time information in addition to being a means of mass-communication and marketing. Monitoring and analyzing information on Twitter can lead to invaluable insights, which might otherwise be hard to get using conventional media resources. An important task in analyzing highly networked information sources like twitter is to identify communities...]]></description>
  <dc:date>2011-05-16</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/393/PowerRelations-A-Question-Answering-System-for-DBPedia">
  <title><![CDATA[PowerRelations: A Question Answering System for DBPedia]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/393/PowerRelations-A-Question-Answering-System-for-DBPedia</link>
  <description><![CDATA[Large amounts of structured and semi-structured semantic data are available on the Web. A well-known example is DBpedia, which extracts data from Wikipedia, encodes it in the Semantic Web language RDF, and stores it in a triplestore. Although a formal query language, SPARQL, is available for accessing such data, it remains challenging for users to query the knowledge unless they are familiar with SPARQL and the particular ontologies used. We have developed an intuitive system for users to ex...]]></description>
  <dc:date>2011-04-26</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/368/Text-Based-Similarity-Metrics-and-Deltas-for-Semantic-Web-Graphs">
  <title><![CDATA[Text Based Similarity Metrics and Deltas for Semantic Web Graphs]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/368/Text-Based-Similarity-Metrics-and-Deltas-for-Semantic-Web-Graphs</link>
  <description><![CDATA[Recognizing that two Semantic Web documents or graphs are similar and characterizing their differences is useful in many tasks, including retrieval, updating, version control and knowledge base editing. I will describe several text-based similarity metrics that characterize the relation between Semantic Web graphs and evaluate these metrics for three specific cases of similarity: similarity in classes and properties, similarity disregarding differences in base-URIs, and versioning relationshi...]]></description>
  <dc:date>2010-10-05</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/350/Text-Based-Similarity-Metrics-and-Delta-for-Semantic-Web-Graphs">
  <title><![CDATA[Text Based Similarity Metrics and Delta for Semantic Web Graphs]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/350/Text-Based-Similarity-Metrics-and-Delta-for-Semantic-Web-Graphs</link>
  <description><![CDATA[Recognizing that two semantic web documents or graphs are similar, and
characterizing their differences is useful in many tasks, including
retrieval, updating, version control and knowledge base editing.  We
describe a number of text based similarity metrics that characterize
the relation between semantic web graphs and evaluate these metrics
for three specific cases of similarity that we have identified:
similarity in classes and properties used while differing only in
literal content...]]></description>
  <dc:date>2010-06-28</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/314/Multivariate-Time-Series-Analysis-of-Physiological-and-Clinical-Data">
  <title><![CDATA[Multivariate Time Series Analysis of Physiological and Clinical Data]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/314/Multivariate-Time-Series-Analysis-of-Physiological-and-Clinical-Data</link>
  <description><![CDATA[Patricia Ordóñez Rozo will talk abut her
PhD research

Multivariate Time Series Amalgams (MTSAs) provide an integrated,
multivariate approach to representing clinical and physiological
data. The hybrid representation automates the personalization of
baselines and thresholds based on a patient’s medical history while
also incorporating traditional baselines and thresholds. The
visualization of this representation captures the rate of change of
provider-selected parameters and the ...]]></description>
  <dc:date>2009-09-23</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/620/Identifying-and-characterizing-user-communities-on-Twitter-during-crisis-events">
  <title><![CDATA[Identifying and characterizing user communities on Twitter during crisis events]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/620/Identifying-and-characterizing-user-communities-on-Twitter-during-crisis-events</link>
  <description><![CDATA[Twitter is a prominent online social media which is used to share information and opinions. Previous research has shown that current real world news topics and events dominate the discussions on Twitter. In this paper, we present a preliminary study to identify and characterize communities from a set of users who post messages on Twitter during crisis events. We present our work in progress by analyzing three major crisis events of 2011 as case studies (Hurricane Irene, Riots in England, and ...]]></description>
  <dc:date>2012-10-29</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/555/Community-Detection-in-Twitter">
  <title><![CDATA[Community Detection in Twitter]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/555/Community-Detection-in-Twitter</link>
  <description><![CDATA[Twitter has recently evolved into a source of social, political and real time information in addition to being a means of mass-communication and marketing. Monitoring
and analyzing information on Twitter can lead to invaluable insights, which might otherwise
be hard to get using conventional media resources. An important task in analyzing highly networked information sources like twitter is to identify communities that are formed. A
community on twitter can be defined as a set of users tha...]]></description>
  <dc:date>2011-05-25</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/504/Text-Based-Similarity-Metrics-and-Delta-for-Semantic-Web-Graphs">
  <title><![CDATA[Text Based Similarity Metrics and Delta for Semantic Web Graphs]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/504/Text-Based-Similarity-Metrics-and-Delta-for-Semantic-Web-Graphs</link>
  <description><![CDATA[Recognizing that two Semantic Web documents or graphs are similar and characterizing their differences is useful in many tasks, including retrieval, updating, version control and knowledge base editing. We describe several text-based similarity metrics that characterize the relation between Semantic Web graphs and evaluate these metrics for three specialc cases of similarity: similarity in classes and properties, similarity disregarding differences in base-URIs, and versioning relation- ship....]]></description>
  <dc:date>2010-11-07</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/494/Text-Based-Similarity-and-Delta-for-Semantic-Web-Graphs">
  <title><![CDATA[Text Based Similarity and Delta for Semantic Web Graphs]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/494/Text-Based-Similarity-and-Delta-for-Semantic-Web-Graphs</link>
  <description><![CDATA[Recognizing that two Semantic Web documents or graphs
are similar, and characterizing their differences is useful in many tasks,
including retrieval, updating, version control and knowledge base editing.
We describe a number of text based similarity metrics that characterize
the relation between Semantic Web graphs and evaluate these metrics
for three specific cases of similarity that we have identified: similarity in
classes and properties used while differing only in literal content, ...]]></description>
  <dc:date>2010-08-07</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/497/Text-Based-Similarity-Metrics-and-Delta-for-Semantic-Web-Graphs">
  <title><![CDATA[Text Based Similarity Metrics and Delta for Semantic Web Graphs]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/497/Text-Based-Similarity-Metrics-and-Delta-for-Semantic-Web-Graphs</link>
  <description><![CDATA[Recognizing that two semantic web documents or graphs are similar, and characterizing their differences is useful in many tasks, including retrieval, updating, version control and knowledge base editing. We describe a number of text based similarity metrics that characterize the relation between semantic web graphs and evaluate these metrics for three specific cases of similarity that we have identified: similarity in classes and properties used while differing only in literal content, differ...]]></description>
  <dc:date>2010-06-28</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/resource/html/id/300/Text-Based-Similarity-Metrics-and-Delta-for-Semantic-Web-Graphs">
  <title><![CDATA[Text Based Similarity Metrics and Delta for Semantic Web Graphs]]></title>
  <link>http://ebiquity.umbc.edu/resource/html/id/300/Text-Based-Similarity-Metrics-and-Delta-for-Semantic-Web-Graphs</link>
  <description><![CDATA[Recognizing that two semantic web documents or graphs are similar, and characterizing their differences is useful in many tasks, including retrieval, updating, version control and knowledge base editing. We describe a number of text based similarity metrics that characterize the relation between semantic web graphs and evaluate these metrics for three specific cases of similarity that we have identified: similarity in classes and properties used while differing only in literal content, differ...]]></description>
  <dc:date>1999-11-30</dc:date>
 </item>
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