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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[Inferring Relations in Knowledge Graphs with Tensor Decompositions]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/766/Inferring-Relations-in-Knowledge-Graphs-with-Tensor-Decompositions</link>
  <description><![CDATA[Multi-relational data, like knowledge graphs, are generated from multiple data sources by extracting entities and their relationships. We often want to include inferred, implicit or likely relationships that are not explicitly stated, which can be viewed as link-prediction in a graph. Tensor decomposition models have been shown to produce state-of-the-art results in link-prediction tasks. We describe a simple but novel extension to an existing tensor decomposition model to predict missing lin...]]></description>
  <dc:date>2016-12-05</dc:date>
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