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 <channel rdf:about="http://ebiquity.umbc.edu//tags/html/?t=coreference+resolution">
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  <title><![CDATA[UMBC ebiquity RSS Tag Search]]></title>
  <link><![CDATA[http://ebiquity.umbc.edu//tags/html/?t=coreference+resolution]]></link>
  <description><![CDATA[UMBC ebiquity RSS Tag Search for coreference resolution]]></description>
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    <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/439/Cluster-based-Instance-Consolidation-For-Subsequent-Matching"/>
      <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/665/Entity-Type-Recognition-for-Heterogeneous-Semantic-Graphs"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/636/Entity-Type-Recognition-for-Heterogeneous-Semantic-Graphs"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/631/Type-Prediction-for-Efficient-Coreference-Resolution-in-Heterogeneous-Semantic-Graphs"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/633/Online-unsupervised-coreference-resolution-for-semi-structured-heterogeneous-data"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/603/Cluster-based-Instance-Consolidation-For-Subsequent-Matching"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/431/Using-Wikitology-for-Cross-Document-Entity-Coreference-Resolution"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/432/Cross-Document-Coreference-Resolution-A-Key-Technology-for-Learning-by-Reading"/>
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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/439/Cluster-based-Instance-Consolidation-For-Subsequent-Matching">
  <title><![CDATA[Cluster-based Instance Consolidation For Subsequent Matching]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/439/Cluster-based-Instance-Consolidation-For-Subsequent-Matching</link>
  <description><![CDATA[In this week's lab meeting Jennifer Sleaman will present "Cluster-based Instance Consolidation For Subsequent Matching"

Instance consolidation is a way to merge instances that are thought to be the same or closely related that can be used to support coreference resolution and entity linking. For Semantic Web data, consolidating instances can be as simple as relating instances using owl:sameAs, as is the case in linked data, or merging instances that could then be used to populate or enrich...]]></description>
  <dc:date>2012-10-15</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/665/Entity-Type-Recognition-for-Heterogeneous-Semantic-Graphs">
  <title><![CDATA[Entity Type Recognition for Heterogeneous Semantic Graphs]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/665/Entity-Type-Recognition-for-Heterogeneous-Semantic-Graphs</link>
  <description><![CDATA[We describe an approach for identifying fine-grained entity types in heterogeneous data graphs that is effective for unstructured data or when the underlying ontologies or semantic schemas are unknown. Identifying fine-grained entity types, rather than a few high-level types, supports coreference resolution in heterogeneous graphs by reducing the number of possible coreference relations that must be considered.  Big Data problems that involve integrating data from multiple sources can benefit...]]></description>
  <dc:date>2015-03-15</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/636/Entity-Type-Recognition-for-Heterogeneous-Semantic-Graphs">
  <title><![CDATA[Entity Type Recognition for Heterogeneous Semantic Graphs]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/636/Entity-Type-Recognition-for-Heterogeneous-Semantic-Graphs</link>
  <description><![CDATA[We describe an approach to reducing the computational cost of identifying coreferential instances in heterogeneous semantic graphs, where the underlying ontologies may be uninformative or even unknown. The problem is similar to coreference resolution in unstructured text, where a variety of linguistic clues and contextual information is used to infer entity types and predict coreference. Semantic graphs, whether in RDF or another formalism, are semi-structured data with varied contextual clue...]]></description>
  <dc:date>2013-11-15</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/631/Type-Prediction-for-Efficient-Coreference-Resolution-in-Heterogeneous-Semantic-Graphs">
  <title><![CDATA[Type Prediction for Efficient Coreference Resolution in Heterogeneous Semantic Graphs]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/631/Type-Prediction-for-Efficient-Coreference-Resolution-in-Heterogeneous-Semantic-Graphs</link>
  <description><![CDATA[We describe an approach for performing entity type recognition in heterogeneous semantic graphs in order to reduce the computational cost of performing coreference resolution. Our research specifically addresses the problem of working with semi-structured text that uses ontologies that are uninformative or unknown. This problem is similar to coreference resolution in unstructured text, where entities and their types are identified using contextual information and linguistic-based analysis. Se...]]></description>
  <dc:date>2013-09-16</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/633/Online-unsupervised-coreference-resolution-for-semi-structured-heterogeneous-data">
  <title><![CDATA[Online unsupervised coreference resolution for semi-structured heterogeneous data]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/633/Online-unsupervised-coreference-resolution-for-semi-structured-heterogeneous-data</link>
  <description><![CDATA[A pair of RDF instances are said to corefer when they are intended to denote the same
thing in the world, for example, when two nodes of type foaf:Person describe the same
individual. This problem is central to integrating and inter-linking semi-structured
datasets. We are developing an online, unsupervised coreference resolution framework
for heterogeneous, semi-structured data. The online aspect requires us to process
new instances as they appear and not as a batch. The instances are h...]]></description>
  <dc:date>2012-11-30</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/603/Cluster-based-Instance-Consolidation-For-Subsequent-Matching">
  <title><![CDATA[Cluster-based Instance Consolidation For Subsequent Matching]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/603/Cluster-based-Instance-Consolidation-For-Subsequent-Matching</link>
  <description><![CDATA[Instance consolidation is a way to merge instances that are thought to be the same or closely related that can be used to support coreference resolution and entity linking. For Semantic Web data, consolidating instances can be as simple as relating instances using owl:sameAs, as is the case in linked data, or merging instances that could then be used to populate or enrich a knowledge model. In many applications, systems process data incrementally over time and as new data is processed, the st...]]></description>
  <dc:date>2012-11-12</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/431/Using-Wikitology-for-Cross-Document-Entity-Coreference-Resolution">
  <title><![CDATA[Using Wikitology for Cross-Document Entity Coreference Resolution]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/431/Using-Wikitology-for-Cross-Document-Entity-Coreference-Resolution</link>
  <description><![CDATA[We describe the use of the Wikitology knowledge base as a resource for a variety of applications, with special focus on a cross-document entity coreference resolution task. This task involves recognizing when entities and relations mentioned in different documents refer to the same object or relation in the world. Wikitology is a knowledge base system constructed with material from Wikipedia, DBpedia, and Freebase that includes both unstructured text and semi-structured information. Wikitolog...]]></description>
  <dc:date>2009-03-23</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/432/Cross-Document-Coreference-Resolution-A-Key-Technology-for-Learning-by-Reading">
  <title><![CDATA[Cross-Document Coreference Resolution: A Key Technology for Learning by Reading]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/432/Cross-Document-Coreference-Resolution-A-Key-Technology-for-Learning-by-Reading</link>
  <description><![CDATA[Automatic knowledge base population from text is an important technology for a broad range of approaches to learning by reading. Effective automated knowledge base population depends critically upon coreference resolution of entities across sources. Use of a wide range of features, both those that capture evidence for entity merging and those that argue against merging, can significantly improve machine learning-based cross-document coreference resolution. Results from the Global Entity Detec...]]></description>
  <dc:date>2009-03-23</dc:date>
 </item>
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