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	This ontology document is licensed under the Creative Commons
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 <channel rdf:about="http://ebiquity.umbc.edu//tags/html/?t=natural+language">
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  <title><![CDATA[UMBC ebiquity RSS Tag Search]]></title>
  <link><![CDATA[http://ebiquity.umbc.edu//tags/html/?t=natural+language]]></link>
  <description><![CDATA[UMBC ebiquity RSS Tag Search for natural language]]></description>
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      <rdf:li resource="http://ebiquity.umbc.edu/event/html/id/484/Semantic-knowledge-and-privacy-in-the-physical-web"/>
      <rdf:li resource="http://ebiquity.umbc.edu/event/html/id/483/From-Strings-to-Things"/>
      <rdf:li resource="http://ebiquity.umbc.edu/event/html/id/473/Taming-Wild-Big-Data"/>
      <rdf:li resource="http://ebiquity.umbc.edu/event/html/id/466/PhD-defense-Lushan-Han-Schema-Free-Querying-of-Semantic-Data"/>
      <rdf:li resource="http://ebiquity.umbc.edu/event/html/id/451/Information-Extraction-of-Security-related-entities-and-concepts-from-unstructured-text"/>
      <rdf:li resource="http://ebiquity.umbc.edu/event/html/id/419/Classification-of-patients-using-novel-multivariate-time-series-representations-of-physiological-data"/>
      <rdf:li resource="http://ebiquity.umbc.edu/event/html/id/417/Semantic-Web-Meetup"/>
      <rdf:li resource="http://ebiquity.umbc.edu/event/html/id/393/PowerRelations-A-Question-Answering-System-for-DBPedia"/>
      <rdf:li resource="http://ebiquity.umbc.edu/event/html/id/357/Detecting-Domain-Shift"/>
      <rdf:li resource="http://ebiquity.umbc.edu/event/html/id/355/Clustering-short-status-messages-a-topic-model-based-approach"/>
      <rdf:li resource="http://ebiquity.umbc.edu/getnews/html/id/33/SemNews-news-text-to-Semantic-Web"/>
      <rdf:li resource="http://ebiquity.umbc.edu/getnews/html/id/25/Finin-co-chairs-AI-and-the-Web-track-at-AAAI-2006"/>
      <rdf:li resource="http://ebiquity.umbc.edu/project/html/id/105/ALDA-Automated-Legal-Document-Analytics"/>
      <rdf:li resource="http://ebiquity.umbc.edu/project/html/id/71/ArRf-Activity-Recognition-with-RF"/>
      <rdf:li resource="http://ebiquity.umbc.edu/project/html/id/95/Graph-of-Relations"/>
      <rdf:li resource="http://ebiquity.umbc.edu/project/html/id/112/Online-Health-Information"/>
      <rdf:li resource="http://ebiquity.umbc.edu/project/html/id/69/SemNews"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/1187/Evaluating-Causal-AI-Techniques-for-Health-Misinformation-Detection"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/1111/Ensuring-Privacy-Policy-Compliance-of-Wearables-with-IoT-Regulations"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/1150/Multimodal-Language-Learning-for-Object-Retrieval-in-Low-Data-Regimes-in-the-Face-of-Missing-Modalities"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/1069/ProKnow-Process-knowledge-for-safety-constrained-and-explainable-question-generation-for-mental-health-diagnostic-assistance"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/1050/Drug-Abuse-Ontology-to-Harness-Web-Based-Data-for-Substance-Use-Epidemiology-Research-Ontology-Development-Study"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/1033/Computational-Understanding-of-Narratives-A-Survey"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/1037/Knowledge-Infused-Learning-A-Sweet-Spot-in-Neuro-Symbolic-AI"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/1062/Bridging-the-Gap-Using-Deep-Acoustic-Representations-to-Learn-Grounded-Language-from-Percepts-and-Raw-Speech"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/1022/CyberEnt-A-Cybersecurity-Domain-Specific-Dataset-for-Named-Entity-Recognition"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/999/CyBERT-Contextualized-Embeddings-for-the-Cybersecurity-Domain"/>
      <rdf:li resource="http://ebiquity.umbc.edu/research/area/id/20/Language-technology"/>
      <rdf:li resource="http://ebiquity.umbc.edu/resource/html/id/298/Coarse-and-Fine-Grained-Sentiment-Analysis-of-Online-Text"/>
      <rdf:li resource="http://ebiquity.umbc.edu/resource/html/id/306/Detecting-Domain-Shift"/>
      <rdf:li resource="http://ebiquity.umbc.edu/resource/html/id/369/From-Strings-to-Things-Populating-Knowledge-Bases-from-Text"/>
      <rdf:li resource="http://ebiquity.umbc.edu/resource/html/id/331/GoRelations-an-Intuitive-Query-System-for-DBPedia-and-LOD-"/>
      <rdf:li resource="http://ebiquity.umbc.edu/resource/html/id/360/Schema-Free-Querying-of-Semantic-Data"/>
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      <rdf:li resource="http://ebiquity.umbc.edu/resource/html/id/374/Structural-Metadata-from-ArXiv-Articles"/>
      <rdf:li resource="http://ebiquity.umbc.edu/resource/html/id/164/Text-Understanding-Agents-and-the-Semantic-Web-HICSS06-"/>
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 </channel>
 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/484/Semantic-knowledge-and-privacy-in-the-physical-web">
  <title><![CDATA[Semantic knowledge and privacy in the physical web]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/484/Semantic-knowledge-and-privacy-in-the-physical-web</link>
  <description><![CDATA[In the past few years, the Internet of Things has started to become a reality; however, its growth has been hampered by privacy and security concerns. One promising approach is to use Semantic Web technologies to mitigate privacy concerns in an informed, flexible way. We present CARLTON, a framework for managing data privacy for entities in a Physical Web deployment using Semantic Web technologies. CARLTON uses context-sensitive privacy policies to protect privacy of organizational and person...]]></description>
  <dc:date>2016-09-27</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/483/From-Strings-to-Things">
  <title><![CDATA[From Strings to Things]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/483/From-Strings-to-Things</link>
  <description><![CDATA[The Web is the greatest source of general knowledge available today. Its current form, however, suffers from two limitations.  The first is that text and multimedia objects on the Web are easy for people to understand but difficult for machines to interpret and use.  The second is that the Web's access paradigm remains dominated by information retrieval, where keyword queries produce a ranked list of documents that must be read to find the desired information.  I'll discuss research in natura...]]></description>
  <dc:date>2016-07-14</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/473/Taming-Wild-Big-Data">
  <title><![CDATA[Taming Wild Big Data]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/473/Taming-Wild-Big-Data</link>
  <description><![CDATA[In this week's Ebiquity meeting, Jennifer Sleeman will talk about "Taming Wild Big Data".

Wild Big Data is data that is hard to extract, understand, and use due to its heterogeneous nature and volume. It typically comes without a schema, is obtained from multiple sources and provides a challenge for information extraction and integration. We describe a way to subduing Wild Big Data that uses techniques and resources that are popular for processing natural language text. The approach is...]]></description>
  <dc:date>2014-11-12</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/466/PhD-defense-Lushan-Han-Schema-Free-Querying-of-Semantic-Data">
  <title><![CDATA[PhD defense: Lushan Han, Schema Free Querying of Semantic Data]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/466/PhD-defense-Lushan-Han-Schema-Free-Querying-of-Semantic-Data</link>
  <description><![CDATA[Schema Free Querying of Semantic Data

Lushan Han

Developing interfaces to enable casual, non-expert users to query complex structured data has been the subject of much research over the past forty years. We refer to them as as schema-free query interfaces, since they allow users to freely query data without understanding its schema, knowing how to refer to objects, or mastering the appropriate formal query language. Schema-free query interfaces address fundamental problems in natural la...]]></description>
  <dc:date>2014-05-23</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/451/Information-Extraction-of-Security-related-entities-and-concepts-from-unstructured-text">
  <title><![CDATA[Information Extraction of Security related entities and concepts from unstructured text]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/451/Information-Extraction-of-Security-related-entities-and-concepts-from-unstructured-text</link>
  <description><![CDATA[Cyber Security has been a big concern especially in past one decade where it is witnessed that targets ranging from large number of internet users to government agencies are being attacked because of vulnerabilities present in the system. Even though these vulnerabilities are identified and published publicly but response has always been slow in covering up these vulnerabilities because there is no automatic mechanism to understand and process this unstructured text that is published on inter...]]></description>
  <dc:date>2013-04-01</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/419/Classification-of-patients-using-novel-multivariate-time-series-representations-of-physiological-data">
  <title><![CDATA[Classification of patients using novel multivariate time series representations of physiological data]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/419/Classification-of-patients-using-novel-multivariate-time-series-representations-of-physiological-data</link>
  <description><![CDATA[I will present two novel multivariate time series representations to classify physiological data of different lengths.The representations may be applied to any group of multivariate time series data that examine the state or health of an entity. Multivariate Bag-of-Patterns and Stacked Bags-of-Patterns improve on their univariate counterpart, inspired by the bag-of-words model, by using multiple time series and analyzing the data in a multivariate fashion. My collaborators and I also borrow t...]]></description>
  <dc:date>2011-11-29</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/417/Semantic-Web-Meetup">
  <title><![CDATA[Semantic Web Meetup]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/417/Semantic-Web-Meetup</link>
  <description><![CDATA[The UMBC Ebiquity Lab is hosting the November meeting of the Lotico Central Maryland Semantic Web Meetup from 6:00-8:00 pm in room 456 of the ITE building (directions).  All are welcome.  If you want to attend, please join the  Central MD Semantic Web Meetup group and RSVP.  The meeting will start with a pizza social from 6:00pm to 6:45pm and then continue with a series of short presentations of current Semantic Web research being done in our lab.

  Tim Finin: introduction and overview

...]]></description>
  <dc:date>2011-11-15</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/393/PowerRelations-A-Question-Answering-System-for-DBPedia">
  <title><![CDATA[PowerRelations: A Question Answering System for DBPedia]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/393/PowerRelations-A-Question-Answering-System-for-DBPedia</link>
  <description><![CDATA[Large amounts of structured and semi-structured semantic data are available on the Web. A well-known example is DBpedia, which extracts data from Wikipedia, encodes it in the Semantic Web language RDF, and stores it in a triplestore. Although a formal query language, SPARQL, is available for accessing such data, it remains challenging for users to query the knowledge unless they are familiar with SPARQL and the particular ontologies used. We have developed an intuitive system for users to ex...]]></description>
  <dc:date>2011-04-26</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/357/Detecting-Domain-Shift">
  <title><![CDATA[Detecting Domain Shift]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/357/Detecting-Domain-Shift</link>
  <description><![CDATA[Machine learning systems are typically trained in the lab and then deployed in the wild.  But what happens when the data to which they are exposed in the wild change in a way that hurts accuracy?  For example, a system may be trained to classify movie reviews as either positive or negative (i.e., sentiment classification), but over time book reviews get mixed into the data stream.  The problem of responding to such changes when they are known to have occurred has been studied extensively.  In...]]></description>
  <dc:date>2010-09-03</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/355/Clustering-short-status-messages-a-topic-model-based-approach">
  <title><![CDATA[Clustering short status messages: a topic model based approach]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/355/Clustering-short-status-messages-a-topic-model-based-approach</link>
  <description><![CDATA[Recently, there has been an exponential rise in the use of online social media systems like Twitter and Facebook. Even more usage has been observed during events related to natural disasters, political turmoil or other such crises. Tweets or status messages are short and may not carry enough contextual clues. Hence, applying traditional natural language processing algorithms on such data is challenging. Topic model is a popular method for modeling term frequency occurrences for documents in a...]]></description>
  <dc:date>2010-07-26</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/getnews/html/id/33/SemNews-news-text-to-Semantic-Web">
  <title><![CDATA[SemNews: news text to Semantic Web]]></title>
  <link>http://ebiquity.umbc.edu/getnews/html/id/33/SemNews-news-text-to-Semantic-Web</link>
  <description><![CDATA[SemNews Understands the News 

 Prototype UMBC system interprets online news stories  and  publishes text meaning on the Semantic Web

      SemNews is a prototype
application being developed by UMBC Ph.D. student Akshay Java that
uses a sophisticated text understanding system to interpret summaries
of news stories, publishes the results on the semantic web and
provides browsing and query services over them.  The project is the
result of a collaboration between the UMBC's Institute ...]]></description>
  <dc:date>2006-01-12</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/getnews/html/id/25/Finin-co-chairs-AI-and-the-Web-track-at-AAAI-2006">
  <title><![CDATA[Finin co-chairs AI and the Web track at AAAI 2006]]></title>
  <link>http://ebiquity.umbc.edu/getnews/html/id/25/Finin-co-chairs-AI-and-the-Web-track-at-AAAI-2006</link>
  <description><![CDATA[UMBC faculty member Tim Finin is the co-chair of a Special Track on
Artificial Intelligence and the Web at the Twenty-First National
Conference on Artificial Intelligence sponsored by the American
Association for Artificial Intelligence (AAAI).  Finin joins
University of Michigan professor Dragomir Radev to organize this track
as part of AAAI's National Conference to be held in Boston July 16-20,
2006.

The special track on AI and the Web invites technical papers on the
use of AI tec...]]></description>
  <dc:date>2005-07-01</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/project/html/id/105/ALDA-Automated-Legal-Document-Analytics">
  <title><![CDATA[ALDA: Automated Legal Document Analytics]]></title>
  <link>http://ebiquity.umbc.edu/project/html/id/105/ALDA-Automated-Legal-Document-Analytics</link>
  <description><![CDATA[There has been an exponential growth in use of digitized legal documents in recent years. Majority of services on the Internet have associated legal documents such as Terms of Services, Privacy Policies and Service Level agreements. A large corpus of court cases, judgments and compliance/regulations are now digitally available for e-discovery. Moreover, businesses are maintaining large data sets of legal contracts that they have signed with their employees, customers and contractors. Furtherm...]]></description>
  <dc:date>2014-06-01</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/project/html/id/71/ArRf-Activity-Recognition-with-RF">
  <title><![CDATA[ArRf - Activity Recognition with RF]]></title>
  <link>http://ebiquity.umbc.edu/project/html/id/71/ArRf-Activity-Recognition-with-RF</link>
  <description><![CDATA[As the population ages tools for aiding in the care of elderly become increasingly valuable.  There is a need for a suite of tools that monitor senior citizens, help them through their day, and alert others if they need help.  Several good techniques for creating systems that assist senior citizens have emerged.  What all such computer systems lack is a good way to determine what a person is actually doing.  Entering every task that a person does into a computer is time consuming and not prac...]]></description>
  <dc:date>2005-09-01</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/project/html/id/95/Graph-of-Relations">
  <title><![CDATA[Graph of Relations]]></title>
  <link>http://ebiquity.umbc.edu/project/html/id/95/Graph-of-Relations</link>
  <description><![CDATA[ 
Users need better ways to explore linked open data collections and obtain information from it. Using SPARQL requires not only mastering its syntax and semantics but also understanding the RDF data model, the ontology used by the DBpedia, and URIs for entities of interest.  Natural language question answering systems solve the problem, but these are still subjects of research. We are developing a compromise approach in which non-experts specify a graphical ``skeleton'' for a query and anno...]]></description>
  <dc:date>2010-01-01</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/project/html/id/69/SemNews">
  <title><![CDATA[SemNews]]></title>
  <link>http://ebiquity.umbc.edu/project/html/id/69/SemNews</link>
  <description><![CDATA[SemNews monitors different RSS News Sources and provides a structured representation of the meaning of the news. The meaning is extracted using OntoSem, a Natural Language Processing system that uses a constructed world model or an Ontology.

The extracted meaning from the RSS descriptions of the news articles are then converted into Semantic Web representation such as RDF. This is also stored in a Redland Triple store and lets users perform semantic queries over the documents. This enables...]]></description>
  <dc:date>2005-01-01</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/1187/Evaluating-Causal-AI-Techniques-for-Health-Misinformation-Detection">
  <title><![CDATA[Evaluating Causal AI Techniques for Health  Misinformation Detection]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/1187/Evaluating-Causal-AI-Techniques-for-Health-Misinformation-Detection</link>
  <description><![CDATA[Abstract—The proliferation of health misinformation on social media, particularly regarding chronic conditions such as diabetes, hypertension, and obesity, poses significant public health risks. This study evaluates the feasibility of leveraging Natural Language Processing (NLP) techniques for real-time misinformation detection and classification, focusing on Reddit discussions. Using logistic regression as a baseline model, supplemented by Latent Dirichlet Allocation (LDA) for topic modeli...]]></description>
  <dc:date>2025-03-17</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/1111/Ensuring-Privacy-Policy-Compliance-of-Wearables-with-IoT-Regulations">
  <title><![CDATA[Ensuring Privacy Policy Compliance of Wearables with IoT Regulations]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/1111/Ensuring-Privacy-Policy-Compliance-of-Wearables-with-IoT-Regulations</link>
  <description><![CDATA[In an era where wearables, particularly those in non-hospital settings, collect and transmit sensitive personal data, it is imperative to implement stringent privacy safeguards. The National Institute of Standards and Technology (NIST) Internal Report 8228 provides regulations for securing Internet of Things (IoT) devices, data, and the privacy of individuals. We have developed a novel framework for examining the privacy policies governing the data and information utilized by wearable devices...]]></description>
  <dc:date>2023-11-01</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/1150/Multimodal-Language-Learning-for-Object-Retrieval-in-Low-Data-Regimes-in-the-Face-of-Missing-Modalities">
  <title><![CDATA[Multimodal Language Learning for Object Retrieval in Low Data Regimes in the Face of Missing Modalities]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/1150/Multimodal-Language-Learning-for-Object-Retrieval-in-Low-Data-Regimes-in-the-Face-of-Missing-Modalities</link>
  <description><![CDATA[Our study is motivated by robotics, where when dealing with robots or other physical systems, we often need to balance competing concerns of relying on complex, multimodal data coming from a variety of sensors with a general lack of large representative datasets.  Despite the complexity of modern robotic platforms and the need for multimodal interaction, there has been little research on integrating more than two modalities in a low data regime with the real-world constraint that sensors fail...]]></description>
  <dc:date>2023-10-01</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/1069/ProKnow-Process-knowledge-for-safety-constrained-and-explainable-question-generation-for-mental-health-diagnostic-assistance">
  <title><![CDATA[ProKnow: Process knowledge for safety constrained and explainable question generation for mental health diagnostic assistance]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/1069/ProKnow-Process-knowledge-for-safety-constrained-and-explainable-question-generation-for-mental-health-diagnostic-assistance</link>
  <description><![CDATA[Virtual Mental Health Assistants (VMHAs) are utilized in health care to provide patient services such as counseling and suggestive care. They are not used for patient diagnostic assistance because they cannot adhere to safety constraints and specialized clinical process knowledge (ProKnow) used to obtain clinical diagnoses. In this work, we define ProKnow as an ordered set of information that maps to evidence-based guidelines or categories of conceptual understanding to experts in a domain. W...]]></description>
  <dc:date>2023-01-09</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/1050/Drug-Abuse-Ontology-to-Harness-Web-Based-Data-for-Substance-Use-Epidemiology-Research-Ontology-Development-Study">
  <title><![CDATA[Drug Abuse Ontology to Harness Web-Based Data for Substance Use Epidemiology Research: Ontology Development Study]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/1050/Drug-Abuse-Ontology-to-Harness-Web-Based-Data-for-Substance-Use-Epidemiology-Research-Ontology-Development-Study</link>
  <description><![CDATA[Background: Web-based resources and social media platforms play an increasingly important role in health-related knowledge and experience sharing. There is a growing interest in the use of these novel data sources for epidemiological surveillance of substance use behaviors and trends.

Methods: The domain and scope of the DAO were defined using competency questions from popular ontology methodology (101 ontology development). The 101 method includes determining the domain and scope of ontol...]]></description>
  <dc:date>2022-12-23</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/1033/Computational-Understanding-of-Narratives-A-Survey">
  <title><![CDATA[Computational Understanding of Narratives: A Survey]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/1033/Computational-Understanding-of-Narratives-A-Survey</link>
  <description><![CDATA[Storytelling and the delivery of societal narratives enable human beings to communicate, connect, and understand one another and the world around them. Narratives can be defined as spoken, visual, or written accounts of interconnected events and actors, generally evolving through some notion of time. Today, information is typically conveyed over online communication mediums, such as social media and blogging websites. Consequently, the act of narrative delivery itself has shifted from simply ...]]></description>
  <dc:date>2022-09-05</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/1037/Knowledge-Infused-Learning-A-Sweet-Spot-in-Neuro-Symbolic-AI">
  <title><![CDATA[Knowledge-Infused Learning: A Sweet Spot in Neuro-Symbolic AI]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/1037/Knowledge-Infused-Learning-A-Sweet-Spot-in-Neuro-Symbolic-AI</link>
  <description><![CDATA[Deep learning has revolutionized the artificial intelligence (AI) landscape by enhancing machine capabilities to understand data-dependant relationships. On the other hand, knowledge may not directly correlate or depend on the data but represents facts that are true. Combining knowledge with the data-driven deep learning techniques improves upon what can be learned from data alone, resulting in improved performance with reduced training, user-level explainability, modeling uncertainty in deep...]]></description>
  <dc:date>2022-08-01</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/1062/Bridging-the-Gap-Using-Deep-Acoustic-Representations-to-Learn-Grounded-Language-from-Percepts-and-Raw-Speech">
  <title><![CDATA[Bridging the Gap: Using Deep Acoustic Representations to Learn Grounded Language from Percepts and Raw Speech]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/1062/Bridging-the-Gap-Using-Deep-Acoustic-Representations-to-Learn-Grounded-Language-from-Percepts-and-Raw-Speech</link>
  <description><![CDATA[Learning to understand grounded language, which connects natural language to percepts, is a critical research area. Prior work in grounded language acquisition has focused primarily on textual inputs. In this work, we demonstrate the feasibility of performing grounded language acquisition on paired visual percepts and raw speech inputs. This will allow human-robot interactions in which language about novel tasks and environments is learned from end-users, reducing dependence on textual inputs...]]></description>
  <dc:date>2022-06-28</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/999/CyBERT-Contextualized-Embeddings-for-the-Cybersecurity-Domain">
  <title><![CDATA[CyBERT: Contextualized Embeddings for the Cybersecurity Domain]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/999/CyBERT-Contextualized-Embeddings-for-the-Cybersecurity-Domain</link>
  <description><![CDATA[We present CyBERT, a domain-specific Bidirectional Encoder Representations from Transformers (BERT) model, fine-tuned with a large corpus of textual cybersecurity data. State-of-the-art natural language models that can process dense, fine-grained textual threat, attack, and vulnerability information can provide numerous benefits to the cybersecurity community. The primary contribution of this paper is to provide the security community with an initial fine-tuned BERT model that can perform a v...]]></description>
  <dc:date>2021-12-15</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/research/area/id/20/Language-technology">
  <title><![CDATA[Language technology]]></title>
  <link>http://ebiquity.umbc.edu/research/area/id/20/Language-technology</link>
  <description><![CDATA[Information retrieval and natural language processing]]></description>
  <dc:date>2026-05-17</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/resource/html/id/298/Coarse-and-Fine-Grained-Sentiment-Analysis-of-Online-Text">
  <title><![CDATA[Coarse and Fine Grained Sentiment Analysis of Online Text]]></title>
  <link>http://ebiquity.umbc.edu/resource/html/id/298/Coarse-and-Fine-Grained-Sentiment-Analysis-of-Online-Text</link>
  <description><![CDATA[Sentiment analysis - the automated extraction of expressions of positive and negative attitudes from text - has received a great amount of attention over the last ten years. Over the same period, via the widespread growth in the use of what we have come to call social media, there has been an explosion in the amount of publically available user generated text on the Web. This text has the potential of providing a source of real time, time tagged sentiments from people all over the globe.

T...]]></description>
  <dc:date>2010-05-11</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/resource/html/id/306/Detecting-Domain-Shift">
  <title><![CDATA[Detecting Domain Shift]]></title>
  <link>http://ebiquity.umbc.edu/resource/html/id/306/Detecting-Domain-Shift</link>
  <description><![CDATA[Machine learning systems are typically trained in the lab and then deployed in the wild. But what happens when the data to which they are exposed in the wild change in a way that hurts accuracy? For example, a system may be trained to classify movie reviews as either positive or negative (i.e., sentiment classification), but over time book reviews get mixed into the data stream. The problem of responding to such changes when they are known to have occurred has been studied extensively. In thi...]]></description>
  <dc:date>2010-09-03</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/resource/html/id/369/From-Strings-to-Things-Populating-Knowledge-Bases-from-Text">
  <title><![CDATA[From Strings to Things: Populating Knowledge Bases from Text]]></title>
  <link>http://ebiquity.umbc.edu/resource/html/id/369/From-Strings-to-Things-Populating-Knowledge-Bases-from-Text</link>
  <description><![CDATA[The Web is the greatest source of general knowledge available today. Its current form, however, suffers from two limitations.  The first is that text and multimedia objects on the Web are easy for people to understand but difficult for machines to interpret and use.  The second is that the Web's access paradigm remains dominated by information retrieval, where keyword queries produce a ranked list of documents that must be read to find the desired information.  I'll discuss research in natura...]]></description>
  <dc:date>2016-06-28</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/resource/html/id/331/GoRelations-an-Intuitive-Query-System-for-DBPedia-and-LOD-">
  <title><![CDATA[GoRelations: an Intuitive Query System for DBPedia (and LOD)]]></title>
  <link>http://ebiquity.umbc.edu/resource/html/id/331/GoRelations-an-Intuitive-Query-System-for-DBPedia-and-LOD-</link>
  <description><![CDATA[Users need better ways to explore linked open data collections and obtain information from it. Using SPARQL requires not only mastering its syntax and semantics but also understanding the RDF data model, the ontology used by the DBpedia, and URIs for entities of interest. Natural language question answering systems solve the problem, but these are still subjects of research. We are developing a compromise approach in which non-experts specify a graphical “skeleton” for a query and annotat...]]></description>
  <dc:date>2011-11-15</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/resource/html/id/360/Schema-Free-Querying-of-Semantic-Data">
  <title><![CDATA[Schema Free Querying of Semantic Data]]></title>
  <link>http://ebiquity.umbc.edu/resource/html/id/360/Schema-Free-Querying-of-Semantic-Data</link>
  <description><![CDATA[Slides for Lushan Han's dissertation defense, Schema Free Querying of Semantic Data.

Developing interfaces to enable casual, non-expert users to query complex structured data has been the subject of much research over the past forty years. We refer to them as as schema-free query interfaces, since they allow users to freely query data without understanding its schema, knowing how to refer to objects, or mastering the appropriate formal query language. Schema-free query interfaces addre...]]></description>
  <dc:date>2014-05-23</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>
 <item rdf:about="http://ebiquity.umbc.edu/resource/html/id/374/Structural-Metadata-from-ArXiv-Articles">
  <title><![CDATA[Structural Metadata from ArXiv Articles]]></title>
  <link>http://ebiquity.umbc.edu/resource/html/id/374/Structural-Metadata-from-ArXiv-Articles</link>
  <description><![CDATA[{
  "@context": "http://schema.org/",
  "@type": "Dataset",
  "name": "Structural Metadata from ArXiv Articles",
  "version": "1.0",
  "license": "https://creativecommons.org/licenses/by-sa/4.0/",
  "description": "The dataset contains metadata encoded in JSON and extracted from more than one million arXiv articles that were put online before the end of 2016. The metadata includes the arXiv id, category names, title, author names, abstract, link to article, publication date and table ...]]></description>
  <dc:date>2017-09-01</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/resource/html/id/164/Text-Understanding-Agents-and-the-Semantic-Web-HICSS06-">
  <title><![CDATA[Text Understanding  Agents and the Semantic Web  (HICSS06)]]></title>
  <link>http://ebiquity.umbc.edu/resource/html/id/164/Text-Understanding-Agents-and-the-Semantic-Web-HICSS06-</link>
  <description><![CDATA[Presentation given at HICSS 2006.

We discuss the challenges involved in adapting the OntoSem natural language processing system to the Web. One set of tasks involves processing Web documents, translating their computed meaning representations from the OntoSem's native KR language into the Semantic Web language OWL, and publishing the results as Web pages and RSS feeds. Another set of tasks works in reverse -- querying the Web for facts needed by OntoSem, translating them from OWL into Onto...]]></description>
  <dc:date>2005-01-04</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/resource/html/id/348/Word-and-phrase-similarity">
  <title><![CDATA[Word and phrase similarity]]></title>
  <link>http://ebiquity.umbc.edu/resource/html/id/348/Word-and-phrase-similarity</link>
  <description><![CDATA[Computing semantic similarity between words/phrases has important applications in natural language processing, information retrieval, and artificial intelligence. There are two prevailing approaches to computing word similarity, based on either using of a thesaurus (e.g., WordNet ) or statistics from a large corpus. We provide a hybrid approach combining the two methods that is demonstrated on a web site through two services: one that returns a similarity score for two words or phrases and an...]]></description>
  <dc:date>2013-01-09</dc:date>
 </item>
</rdf:RDF>
