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 <channel rdf:about="http://ebiquity.umbc.edu//tags/html/?t=knowledge+extraction">
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  <link><![CDATA[http://ebiquity.umbc.edu//tags/html/?t=knowledge+extraction]]></link>
  <description><![CDATA[UMBC ebiquity RSS Tag Search for knowledge extraction]]></description>
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      <rdf:li resource="http://ebiquity.umbc.edu/event/html/id/349/Learning-by-Reading-Automatic-Knowledge-Extraction-Through-Semantic-Analysis"/>
      <rdf:li resource="http://ebiquity.umbc.edu/event/html/id/220/Streaming-Knowledge-Bases"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/1186/MedReg-KG-KnowledgeGraph-for-Streamlining-Medical-Device-Regulatory-Compliance"/>
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      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/993/The-Effect-of-Text-Ambiguity-on-creating-Policy-Knowledge-Graphs"/>
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      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/912/Knowledge-for-Cyber-Threat-Intelligence"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/1134/Knowledge-for-Cyber-Threat-Intelligence"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/812/Automated-Knowledge-Extraction-from-the-Federal-Acquisition-Regulations-System-FARS-"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/782/Thinking-Fast-and-Slow-Combining-Vector-Spaces-and-Knowledge-Graphs"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/844/Thinking-Fast-and-Slow-Combining-Vector-Spaces-and-Knowledge-Graphs"/>
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 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/349/Learning-by-Reading-Automatic-Knowledge-Extraction-Through-Semantic-Analysis">
  <title><![CDATA[Learning by Reading: Automatic Knowledge Extraction Through Semantic Analysis]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/349/Learning-by-Reading-Automatic-Knowledge-Extraction-Through-Semantic-Analysis</link>
  <description><![CDATA[Ph.D. Dissertation Defense

To support rich semantic analysis of text, traditional natural language processing tools require access to a cache of static knowledge with both broad coverage and deep meaning.  Acquiring this knowledge by hand is so expensive and error-prone, it has been dubbed the "knowledge acquisition bottleneck".  In this work, we present a method for reducing the impact of this bottleneck by automating the knowledge acquisition task using the novel approach of bootstrappin...]]></description>
  <dc:date>2010-07-02</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/220/Streaming-Knowledge-Bases">
  <title><![CDATA[Streaming Knowledge Bases]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/220/Streaming-Knowledge-Bases</link>
  <description><![CDATA[A knowledge base can be thought of as a special kind of database for knowledge management.
It provides the means for computerized collection, organization and retrieval
of knowledge. Due to growth in deployment of sensors, we encounter many scenarios
where data is constantly flowing between sensors and applications. The volume of data
produced is large, so is the rate of the data-flow. In such scenarios, knowledge extraction
boils down to finding useful information i.e. detecting events ...]]></description>
  <dc:date>2007-08-29</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/1186/MedReg-KG-KnowledgeGraph-for-Streamlining-Medical-Device-Regulatory-Compliance">
  <title><![CDATA[MedReg-KG: KnowledgeGraph for Streamlining Medical Device Regulatory Compliance]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/1186/MedReg-KG-KnowledgeGraph-for-Streamlining-Medical-Device-Regulatory-Compliance</link>
  <description><![CDATA[Healthcare providers are deploying a large number
of AI-driven Medical devices to help monitor and medicate
patients. For patients with chronic ailments, like diabetes or
gastric diseases, usage of these devices becomes part of their
daily lifestyle. These medical devices often capture personally
identifiable information (PII) and hence are strictly regulated by
the Food and Drug Administration (FDA) to ensure the safety
and efficacy of the medical device. Medical device regulations
a...]]></description>
  <dc:date>2024-12-15</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/1022/CyberEnt-A-Cybersecurity-Domain-Specific-Dataset-for-Named-Entity-Recognition">
  <title><![CDATA[CyberEnt: A Cybersecurity Domain Specific Dataset for Named Entity Recognition]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/1022/CyberEnt-A-Cybersecurity-Domain-Specific-Dataset-for-Named-Entity-Recognition</link>
  <description><![CDATA[Named Entity Recognition (NER) is a critical component of automated knowledge extraction. It allows Natural Language Processing (NLP) models to label instances of real-world entities that are important in the context of the text. To be able to accomplish this, the NLP model needs to be trained on large corpora of human-annotated text. There are examples of general, domain-agonistic text corpora available, but they are not suited for fields such as cybersecurity, that require domain-specific t...]]></description>
  <dc:date>2022-04-18</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/993/The-Effect-of-Text-Ambiguity-on-creating-Policy-Knowledge-Graphs">
  <title><![CDATA[The Effect of Text Ambiguity on creating Policy Knowledge Graphs]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/993/The-Effect-of-Text-Ambiguity-on-creating-Policy-Knowledge-Graphs</link>
  <description><![CDATA[A growing number of web and cloud-based products and services rely on data sharing between consumers, service providers, and their subsidiaries and third parties. There is a growing concern around the security and privacy of data in such large-scale shared architectures. Most organizations have a human-written privacy policy that discloses all the ways that data is shared, stored, and used. The organizational privacy policies must also be compliant with government and administrative regulatio...]]></description>
  <dc:date>2021-09-30</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/1059/Cyber-All-Intel-An-AI-for-Security-Related-Threat-Intelligence">
  <title><![CDATA[Cyber-All-Intel: An AI for Security Related Threat Intelligence]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/1059/Cyber-All-Intel-An-AI-for-Security-Related-Threat-Intelligence</link>
  <description><![CDATA[Keeping up with threat intelligence is a must for a security analyst today. There is a volume of information present in `the wild' that affects an organization. We need to develop an artificial intelligence system that scours the intelligence sources to keep the analyst updated about various threats that pose a risk to her organization. A security analyst who is better `tapped in' can be more effective. This paper presents Cyber-All-Intel, an artificial intelligence system to aid a security a...]]></description>
  <dc:date>2019-05-07</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/912/Knowledge-for-Cyber-Threat-Intelligence">
  <title><![CDATA[Knowledge for Cyber Threat Intelligence]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/912/Knowledge-for-Cyber-Threat-Intelligence</link>
  <description><![CDATA[Keeping up with threat intelligence is a must for a security analyst today. There is a volume of information present in 'the wild' that affects an organization. We need to develop an artificial intelligence system that scours the intelligence sources, to keep the analyst updated about various threats that pose a risk to her organization. A security analyst who is better 'tapped in' can be more effective.

In this thesis, we present, Cyber-All-Intel an artificial intelligence system to aid a...]]></description>
  <dc:date>2019-05-01</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/1134/Knowledge-for-Cyber-Threat-Intelligence">
  <title><![CDATA[Knowledge for Cyber Threat Intelligence]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/1134/Knowledge-for-Cyber-Threat-Intelligence</link>
  <description><![CDATA[Keeping up with threat intelligence is a must for a security analyst today.
There is a volume of information present in 'the wild' that affects an organization.
We need to develop an artificial intelligence system that scours the intelligence
sources, to keep the analyst updated about various threats that pose a risk to her
organization. A security analyst who is better 'tapped in' can be more effective.
In this thesis, we present, Cyber-All-Intel an artificial intelligence system to
ai...]]></description>
  <dc:date>2019-05-01</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/812/Automated-Knowledge-Extraction-from-the-Federal-Acquisition-Regulations-System-FARS-">
  <title><![CDATA[Automated Knowledge Extraction from the Federal Acquisition Regulations System (FARS)]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/812/Automated-Knowledge-Extraction-from-the-Federal-Acquisition-Regulations-System-FARS-</link>
  <description><![CDATA[With increasing regulation of Big Data, it is becoming essential for organizations to ensure compliance with various data protection standards. The Federal Acquisition Regulations System (FARS) within the Code of Federal Regulations (CFR) includes facts and rules for individuals and organizations seeking to do business with the US Federal government. Parsing and gathering knowledge from such lengthy regulation documents is currently done manually and is time and human intensive.Hence, develop...]]></description>
  <dc:date>2017-12-11</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/782/Thinking-Fast-and-Slow-Combining-Vector-Spaces-and-Knowledge-Graphs">
  <title><![CDATA[Thinking, Fast and Slow: Combining Vector Spaces and Knowledge Graphs]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/782/Thinking-Fast-and-Slow-Combining-Vector-Spaces-and-Knowledge-Graphs</link>
  <description><![CDATA[Knowledge graphs and vector space models are robust knowledge representation techniques with individual strengths and weaknesses. Vector space models excel at determining similarity between concepts, but are severely constrained when evaluating complex dependency relations and other logic-based operations that are a strength of knowledge graphs. We describe the VKG structure that helps unify knowledge graphs and vector representation of entities, and enables powerful inference methods and sea...]]></description>
  <dc:date>2017-08-30</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/844/Thinking-Fast-and-Slow-Combining-Vector-Spaces-and-Knowledge-Graphs">
  <title><![CDATA[Thinking, Fast and Slow: Combining Vector Spaces and Knowledge Graphs]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/844/Thinking-Fast-and-Slow-Combining-Vector-Spaces-and-Knowledge-Graphs</link>
  <description><![CDATA[Knowledge graphs and vector space models are robust knowledge representation techniques with individual strengths and weaknesses. Vector space models excel at determining similarity between concepts, but are severely constrained when evaluating complex dependency relations and other logic-based operations that are a strength of knowledge graphs. We describe the VKG structure that helps unify knowledge graphs and vector representation of entities, and enables powerful inference methods and sea...]]></description>
  <dc:date>2017-08-10</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/718/Parallelizing-Natural-Language-Techniques-for-Knowledge-Extraction-from-Cloud-Service-Level-Agreements">
  <title><![CDATA[Parallelizing Natural Language Techniques for Knowledge Extraction from Cloud Service Level Agreements]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/718/Parallelizing-Natural-Language-Techniques-for-Knowledge-Extraction-from-Cloud-Service-Level-Agreements</link>
  <description><![CDATA[To efficiently utilize their cloud based services, consumers have to continuously monitor and manage the Service Level Agreements (SLA) that define the service performance measures. Currently this is still a time and labor intensive process since the SLAs are primarily stored as text documents. We have significantly automated the process of extracting, managing and monitoring cloud SLAs using natural language processing techniques and Semantic Web technologies. In this paper we describe our p...]]></description>
  <dc:date>2015-10-19</dc:date>
 </item>
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