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 <channel rdf:about="http://ebiquity.umbc.edu//tags/html/?t=graph+embeddings">
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  <title><![CDATA[On the Complementary Nature of Knowledge Graph Embedding, Fine Grain Entity Types, and Language Modeling]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/953/On-the-Complementary-Nature-of-Knowledge-Graph-Embedding-Fine-Grain-Entity-Types-and-Language-Modeling</link>
  <description><![CDATA[We demonstrate the complementary natures of neural knowledge graph embedding, fine-grain entity type prediction, and neural language modeling. We show that a language model-inspired knowledge graph embedding approach yields both improved knowledge graph embeddings and fine-grain entity type representations. Our work also shows that jointly modeling both structured knowledge tuples and language improves both.]]></description>
  <dc:date>2020-11-19</dc:date>
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 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/1070/Cybersecurity-Threat-Intelligence-Augmentation-and-Embedding-Improvement-A-Healthcare-Usecase">
  <title><![CDATA[Cybersecurity Threat Intelligence Augmentation and Embedding Improvement - A Healthcare Usecase]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/1070/Cybersecurity-Threat-Intelligence-Augmentation-and-Embedding-Improvement-A-Healthcare-Usecase</link>
  <description><![CDATA[The implementation of Internet of Things (IoT) devices in medical environments has introduced a growing list of security vulnerabilities and threats. The lack of an extensible big data resource that captures medical device vulnerabilities limits the use of Artificial Intelligence (AI) based cyber defense systems in capturing, detecting, and preventing known and future attacks.  We describe a system that generates a repository of Cyber Threat Intelligence (CTI) about various medical devices an...]]></description>
  <dc:date>2020-11-09</dc:date>
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 <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>
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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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