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 <channel rdf:about="http://ebiquity.umbc.edu//tags/html/?t=tensor">
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      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/963/A-Semantically-Rich-Framework-for-Knowledge-Representation-of-Code-of-Federal-Regulations-CFR-"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/943/Knowledge-Graph-Inference-using-Tensor-Embedding"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/846/Knowledge-Graph-Fact-Prediction-via-Knowledge-Enriched-Tensor-Factorization"/>
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 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/963/A-Semantically-Rich-Framework-for-Knowledge-Representation-of-Code-of-Federal-Regulations-CFR-">
  <title><![CDATA[A Semantically Rich Framework for Knowledge Representation of Code of Federal Regulations (CFR)]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/963/A-Semantically-Rich-Framework-for-Knowledge-Representation-of-Code-of-Federal-Regulations-CFR-</link>
  <description><![CDATA[Federal government agencies and organizations doing business with them have to adhere to the Code of Federal Regulations (CFR). The CFRs are currently available as large text documents that are not machine-processable and so require extensive manual effort to parse and comprehend, especially when sections cross-reference topics spread across various titles. We have developed a novel framework to automatically extract knowledge from CFRs and represent it using a semantically rich knowledgegrap...]]></description>
  <dc:date>2020-12-01</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/943/Knowledge-Graph-Inference-using-Tensor-Embedding">
  <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>
 </item>
 <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>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/910/Joint-Models-to-Refine-Knowledge-Graphs">
  <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>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/862/Reflections-on-Knowledge-Graph-Fact-Prediction-via-Knowledge-Enriched-Tensor-Factorization">
  <title><![CDATA[Reflections on: Knowledge Graph Fact Prediction via Knowledge-Enriched Tensor Factorization]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/862/Reflections-on-Knowledge-Graph-Fact-Prediction-via-Knowledge-Enriched-Tensor-Factorization</link>
  <description><![CDATA[We present a family of four novel methods for embedding knowledge graphs into real-valued tensors that capture the ordered relations found in RDF.  Unlike many previous models, these can easily use prior background knowledge from users or existing knowledge graphs.We demonstrate our models on the task of predicting new facts on eight different knowledge graphs, achieving a 5% to 50% improvement over existing systems. Through experiments, we derived recommendations for selecting the best model...]]></description>
  <dc:date>2019-10-26</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/871/Joint-Models-to-Refine-Knowledge-Graphs">
  <title><![CDATA[Joint Models to Refine Knowledge Graphs]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/871/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>2019-10-25</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/909/Understanding-the-Logical-and-Semantic-Structure-of-Large-Documents">
  <title><![CDATA[Understanding the Logical and Semantic Structure of Large Documents]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/909/Understanding-the-Logical-and-Semantic-Structure-of-Large-Documents</link>
  <description><![CDATA[Current language understanding approaches are mostly focused on small documents, such as newswire articles, blog posts, and product reviews. Understanding and extracting information from large documents like legal documents, reports, proposals, technical manuals, and research articles is still a challenging task. Because the documents may be multi-themed, complex, and cover diverse topics. The content can be split into multiple files or aggregated into one large file. As a result, the content...]]></description>
  <dc:date>2018-05-01</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/820/Frame-Based-Continuous-Lexical-Semantics-through-Exponential-Family-Tensor-Factorization-and-Semantic-Proto-Roles">
  <title><![CDATA[Frame-Based Continuous Lexical Semantics through Exponential Family Tensor Factorization and Semantic Proto-Roles]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/820/Frame-Based-Continuous-Lexical-Semantics-through-Exponential-Family-Tensor-Factorization-and-Semantic-Proto-Roles</link>
  <description><![CDATA[We study how different frame annotations complement one another when learning continuous lexical semantics. We learn the representations from a tensorized skip-gram model that consistently encodes syntactic-semantic content better, with multiple 10% gains over baselines.]]></description>
  <dc:date>2017-08-03</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/766/Inferring-Relations-in-Knowledge-Graphs-with-Tensor-Decompositions">
  <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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