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  <title><![CDATA[Knowledge Graph Inference using Tensor Embedding]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/943/Knowledge-Graph-Inference-using-Tensor-Embedding</link>
  <description><![CDATA[Axiom based inference provides a  clear and consistent way of reasoning to add more information to a knowledge graph.  However, constructing a set of axioms is expensive and requires domain expertise, time, and money.  It is also difficult to reuse or adapt a set of axioms to a knowledge graph in a new domain or even in the same domain but using a slightly different representation approach. This work makes three main contributions,  it (1) provides a family of representation learning algorith...]]></description>
  <dc:date>2020-09-12</dc:date>
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 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/846/Knowledge-Graph-Fact-Prediction-via-Knowledge-Enriched-Tensor-Factorization">
  <title><![CDATA[Knowledge Graph Fact Prediction via Knowledge-Enriched Tensor Factorization]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/846/Knowledge-Graph-Fact-Prediction-via-Knowledge-Enriched-Tensor-Factorization</link>
  <description><![CDATA[We present a family of novel methods for embedding knowledge graphs into real-valued tensors. These tensor-based embeddings capture the ordered relations that are typical in the knowledge graphs represented by semantic web languages like RDF. Unlike many previous models, our methods can easily use prior background knowledge provided by users or extracted automatically from existing knowledge graphs. In addition to providing more robust methods for knowledge graph embedding, we provide a prova...]]></description>
  <dc:date>2019-12-01</dc:date>
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  <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>
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