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 <channel rdf:about="http://ebiquity.umbc.edu//tags/html/?t=knowledge+graphs">
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  <description><![CDATA[UMBC ebiquity RSS Tag Search for knowledge graphs]]></description>
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      <rdf:li resource="http://ebiquity.umbc.edu/project/html/id/110/Medical-Data-Polygraph"/>
      <rdf:li resource="http://ebiquity.umbc.edu/project/html/id/112/Online-Health-Information"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/1210/Security-Compliance-for-Smart-Manufacturing-using-Knowledgegraph-based-Digital-Twin"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/1200/Automating-IoT-Data-Privacy-Compliance-by-Integrating-Knowledge-Graphs-With-Large-Language-Models"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/1191/Integrating-Knowledge-Graphs-with-Retrieval-Augmented-Generation-to-Automate-IoT-Device-Security-Compliance"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/1193/Real-Time-Detection-of-Online-Health-Misinformation-using-an-Integrated-Knowledgegraph-LLM-Approach"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/1169/Enhancing-Knowledge-Graph-Consistency-through-Open-Large-Language-Models-A-Case-Study"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/1073/An-Overview-of-Cybersecurity-Knowledge-Graphs-Mapped-to-the-MITRE-ATT-CK-Framework-Domains"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/1071/Knowledge-Graph-driven-Tabular-Data-Discovery-from-Scientific-Documents"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/1104/Knowledge-Enhanced-Neurosymbolic-Artificial-Intelligence-for-Cybersecurity-and-Privacy"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/1136/Knowledge-Infusion-in-Privacy-Preserving-Data-Generation"/>
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      <rdf:li resource="http://ebiquity.umbc.edu/resource/html/id/381/Semantic-Knowledge-Graphs-for-Cybersecurity"/>
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 <item rdf:about="http://ebiquity.umbc.edu/project/html/id/110/Medical-Data-Polygraph">
  <title><![CDATA[Medical Data Polygraph]]></title>
  <link>http://ebiquity.umbc.edu/project/html/id/110/Medical-Data-Polygraph</link>
  <description><![CDATA[Healthcare organizations exchange sensitive health records, including behavioral health data, across peer-to-peer networks, and it is challenging to find and fix compliance issues proactively.

The Healthcare industry anticipates a growing need to audit substance use disorder patient data, commonly referred to as Part 2 data, having been shared without a release of information signed by the patient. To address this need, we developed and evaluated a novel methodology to detect Part 2 data e...]]></description>
  <dc:date>2022-01-01</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/project/html/id/112/Online-Health-Information">
  <title><![CDATA[Online Health Information]]></title>
  <link>http://ebiquity.umbc.edu/project/html/id/112/Online-Health-Information</link>
  <description><![CDATA[The rise of social media platforms as Online Health Information Sources (OHIS) has increased the spread of health misinformation in cyberspace. The rapid dissemination of false or misleading health information, particularly in public health, can have severe consequences. Misinformation not only endangers public health but also poses significant cybersecurity risks, including eroding trust in credible sources, enabling phishing attacks, and heightening the impact of cyber threats during crises...]]></description>
  <dc:date>2024-01-13</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/1210/Security-Compliance-for-Smart-Manufacturing-using-Knowledgegraph-based-Digital-Twin">
  <title><![CDATA[Security Compliance for Smart Manufacturing using Knowledgegraph based Digital Twin]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/1210/Security-Compliance-for-Smart-Manufacturing-using-Knowledgegraph-based-Digital-Twin</link>
  <description><![CDATA[The combination of Information Technology (IT) and Operational Technology (OT) in smart manufacturing, driven by smart factory innovations and Internet of Things (IoT) devices, generates vast, diverse, and rapidly evolving Big Data, which in turn increases cybersecurity and compliance issues. Adherence to security standards, such as NIST SP 800-171, which requires rigorous access control and audit reporting, is currently obstructed by the resource-intensive and error-prone aspects of manual e...]]></description>
  <dc:date>2025-12-11</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/1200/Automating-IoT-Data-Privacy-Compliance-by-Integrating-Knowledge-Graphs-With-Large-Language-Models">
  <title><![CDATA[Automating IoT Data Privacy Compliance by Integrating Knowledge Graphs With Large Language Models]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/1200/Automating-IoT-Data-Privacy-Compliance-by-Integrating-Knowledge-Graphs-With-Large-Language-Models</link>
  <description><![CDATA[Regulatory compliance is mandatory for Internet of Things (IoT) manufacturers, particularly under stringent frameworks such as the General Data Protection Regulation (GDPR), which governs the handling of personal data. We introduce a novel framework for automating IoT compliance verification by integrating a Large Language Model (LLM) with a domain-specific Knowledge Graph (KG). The framework achieves two primary objectives: 1) leveraging the LLM to interpret natural-language compliance queri...]]></description>
  <dc:date>2025-07-25</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/1191/Integrating-Knowledge-Graphs-with-Retrieval-Augmented-Generation-to-Automate-IoT-Device-Security-Compliance">
  <title><![CDATA[Integrating Knowledge Graphs with Retrieval-Augmented Generation to Automate IoT Device Security Compliance]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/1191/Integrating-Knowledge-Graphs-with-Retrieval-Augmented-Generation-to-Automate-IoT-Device-Security-Compliance</link>
  <description><![CDATA[As IoT device adoption grows, ensuring cybersecurity compliance with IoT standards, like National Institute of Standards and Technology Interagency (NISTIR) 8259A, has become increasingly complex. These standards are typically presented in lengthy, text-based formats that are difficult to process and query automatically. We built a knowledge graph to address this challenge to represent the key concepts, relationships, and references within NISTIR 8259A. We further integrate this knowledge gra...]]></description>
  <dc:date>2025-07-14</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/1193/Real-Time-Detection-of-Online-Health-Misinformation-using-an-Integrated-Knowledgegraph-LLM-Approach">
  <title><![CDATA[Real-Time Detection of Online Health Misinformation using an Integrated Knowledgegraph-LLM Approach]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/1193/Real-Time-Detection-of-Online-Health-Misinformation-using-an-Integrated-Knowledgegraph-LLM-Approach</link>
  <description><![CDATA[Winner of Best Student Paper Award 
The dramatic surge of health misinformation on social media platforms poses a significant threat to public health, contributing to hesitancy in vaccines, delayed medical interventions, and the adoption of untested or harmful treatments. We present a novel, hybrid AI-driven framework designed for the real-time detection of health misinformation on social media platforms while prioritizing user privacy. The framework integrates the strengths of Large Langua...]]></description>
  <dc:date>2025-07-11</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/1169/Enhancing-Knowledge-Graph-Consistency-through-Open-Large-Language-Models-A-Case-Study">
  <title><![CDATA[Enhancing Knowledge Graph Consistency through Open Large Language Models: A Case Study]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/1169/Enhancing-Knowledge-Graph-Consistency-through-Open-Large-Language-Models-A-Case-Study</link>
  <description><![CDATA[High-quality knowledge graphs (KGs) play a crucial role in many applications. However, KGs created by automated information extraction systems can suffer from erroneous extractions or be inconsistent with provenance/source text. It is important to identify and correct such problems. In this paper, we study leveraging the emergent reasoning capabilities of large language models (LLMs) to detect inconsistencies between extracted facts and their provenance. With a focus on “open” LLMs that c...]]></description>
  <dc:date>2024-03-25</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/1073/An-Overview-of-Cybersecurity-Knowledge-Graphs-Mapped-to-the-MITRE-ATT-CK-Framework-Domains">
  <title><![CDATA[An Overview of Cybersecurity Knowledge Graphs Mapped to the MITRE ATT&CK Framework Domains]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/1073/An-Overview-of-Cybersecurity-Knowledge-Graphs-Mapped-to-the-MITRE-ATT-CK-Framework-Domains</link>
  <description><![CDATA[A large volume of cybersecurity-related data sets
are generated daily from systems following disparate protocols
and standards. It is humanly impossible for cybersecurity experts
to manually sieve through these large data sets, with different
schema and metadata, to determine potential attacks or issues.
A myriad of applications and tool sets are offered to automate
the analysis of large cyber data sets. Semantic Web’s community
has been studying the field of cybersecurity for over a...]]></description>
  <dc:date>2023-10-03</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/1071/Knowledge-Graph-driven-Tabular-Data-Discovery-from-Scientific-Documents">
  <title><![CDATA[Knowledge Graph-driven Tabular Data Discovery from Scientific Documents]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/1071/Knowledge-Graph-driven-Tabular-Data-Discovery-from-Scientific-Documents</link>
  <description><![CDATA[Synthesizing information from collections of tables embedded within scientific and technical documents is increasingly critical to emerging knowledge-driven applications. Given their structural heterogeneity, highly domain-specific content, and diffuse context, inferring a precise semantic understanding of such tables is traditionally better accomplished through linking tabular content to concepts and entities in reference knowledge graphs. However, existing tabular data discovery systems are...]]></description>
  <dc:date>2023-09-01</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/1104/Knowledge-Enhanced-Neurosymbolic-Artificial-Intelligence-for-Cybersecurity-and-Privacy">
  <title><![CDATA[Knowledge-Enhanced Neurosymbolic Artificial Intelligence for Cybersecurity and Privacy]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/1104/Knowledge-Enhanced-Neurosymbolic-Artificial-Intelligence-for-Cybersecurity-and-Privacy</link>
  <description><![CDATA[Neurosymbolic artificial intelligence (AI) is an emerging and quickly advancing field that
combines the subsymbolic strengths of (deep) neural networks and the explicit, symbolic
knowledge contained in knowledge graphs (KGs) to enhance explainability and safety in
AI systems. This approach addresses a key criticism of current generation systems,
namely, their inability to generate human-understandable explanations for their
outcomes and ensure safe behaviors, especially in scenarios with...]]></description>
  <dc:date>2023-09-01</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/1136/Knowledge-Infusion-in-Privacy-Preserving-Data-Generation">
  <title><![CDATA[Knowledge Infusion in Privacy Preserving Data Generation]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/1136/Knowledge-Infusion-in-Privacy-Preserving-Data-Generation</link>
  <description><![CDATA[Security monitoring is crucial for maintaining a strong IT infrastructure by protecting against emerging threats, identifying vulnerabilities, and detecting potential points of failure. It involves deploying advanced tools to continuously monitor networks, systems, and configurations. However, organizations face challenges in adapting modern techniques like Machine Learning (ML) due to privacy and security risks associated with sharing internal data.  Compliance with regulations like GDPR fur...]]></description>
  <dc:date>2023-08-06</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/1132/Knowledge-Graphs-and-Reinforcement-Learning-A-Hybrid-Approach-for-Cybersecurity-Problems">
  <title><![CDATA[Knowledge Graphs and Reinforcement Learning: A Hybrid Approach for Cybersecurity Problems]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/1132/Knowledge-Graphs-and-Reinforcement-Learning-A-Hybrid-Approach-for-Cybersecurity-Problems</link>
  <description><![CDATA[With the explosion of available data and computational power, machine learning and deep learning techniques are being increasingly used to solve problems. The domain of cybersecurity is no exception, as we have seen multiple papers in the recent past using data-driven machine learning approaches for different tasks.

Rule-based and supervised machine learning-based approaches are often brittle in detecting attacks, can be defeated by adversaries that adapt, and cannot use the knowledge of e...]]></description>
  <dc:date>2023-07-19</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/research/area/id/39/knowledge-graphs">
  <title><![CDATA[knowledge graphs]]></title>
  <link>http://ebiquity.umbc.edu/research/area/id/39/knowledge-graphs</link>
  <dc:date>2026-04-16</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/resource/html/id/381/Semantic-Knowledge-Graphs-for-Cybersecurity">
  <title><![CDATA[Semantic Knowledge Graphs for Cybersecurity]]></title>
  <link>http://ebiquity.umbc.edu/resource/html/id/381/Semantic-Knowledge-Graphs-for-Cybersecurity</link>
  <dc:date>2020-06-23</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/resource/html/id/392/Sixty-years-of-knowledge-graphs-for-language-understanding">
  <title><![CDATA[Sixty years of knowledge graphs for language understanding]]></title>
  <link>http://ebiquity.umbc.edu/resource/html/id/392/Sixty-years-of-knowledge-graphs-for-language-understanding</link>
  <description><![CDATA[There is a long history of using structured knowledge of one kind or another to support AI tasks, especially ones involving natural language understanding. Over the years, the names and details have changed, from semantic networks to frames to logic programs to databases to expert systems to knowledge bases to the semantic web and currently to knowledge graphs. However, a common thread is that an organized representation of knowledge that can be queried and evolved is a core component of many...]]></description>
  <dc:date>2019-09-19</dc:date>
 </item>
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