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      <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"/>
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      <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/1057/Towards-Semantic-Exploration-of-Tables-in-Scientific-Documents"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/1053/Offline-RL-CKG-A-hybrid-AI-model-for-cybersecurity-tasks"/>
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 <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/482/Kelvin-Information-Extraction-System">
  <title><![CDATA[Kelvin Information Extraction System]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/482/Kelvin-Information-Extraction-System</link>
  <description><![CDATA[I'll describe recent work on the Kelvin information extraction system and its performance in two tasks in the 2015 NIST Text Analysis Conference.  Kelvin has been under development at the JHU Human Language Center of Excellence for several years.  Kelvin reads documents in several languages and extracts entities and relations between them. This year it was used for the Coldstart Knowledge Base Population and Trilingual Entity Discovery and Linking tasks.  Key components in the tasks are a sys...]]></description>
  <dc:date>2015-11-02</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/480/Beyond-NER-Towards-Semantics-in-Clinical-Text">
  <title><![CDATA[Beyond NER: Towards Semantics in Clinical Text]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/480/Beyond-NER-Towards-Semantics-in-Clinical-Text</link>
  <description><![CDATA[While clinical text NLP systems have become very effective in recognizing named entities in clinical text and mapping them to standardized terminologies in the normalization process, there remains a gap in the ability of extractors to combine entities together into a complete semantic representation of medical concepts that contain multiple attributes each of which has its own set of allowed named entities or values. Furthermore, additional domain knowledge may be required to determine the se...]]></description>
  <dc:date>2015-10-05</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/381/Domain-Independent-Sentiment-Analysis">
  <title><![CDATA[Domain Independent Sentiment Analysis]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/381/Domain-Independent-Sentiment-Analysis</link>
  <description><![CDATA[Domain independent sentiment signals are words or word pairs that are present and have the same sentimental orientation in multiple domains. These words can be easily identified if you have an accurate representation of their in-domain sentimental orientation. If you also have an accurate representation of their sentimental strength then you can use them to correctly classify out of domain documents with reasonable accuracy. In this talk I will present a method to identify domain independent ...]]></description>
  <dc:date>2011-03-01</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/369/Machine-aided-human-translation-An-automated-system-for-transcribing-dictated-document-translations">
  <title><![CDATA[Machine aided human translation - An automated system for transcribing dictated document translations]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/369/Machine-aided-human-translation-An-automated-system-for-transcribing-dictated-document-translations</link>
  <description><![CDATA[A model is presented for machine aided human translation (MAHT) that integrates source language text and target language acoustic information to produce the text translation of source language document. It is evaluated on a scenario where a human translator dictates a first draft target language translation of a source language document. Information obtained from the source language document, including translation probabilities derived from statistical machine translation (SMT) and named enti...]]></description>
  <dc:date>2010-10-08</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/project/html/id/109/Accelerated-Cybersecurity">
  <title><![CDATA[Accelerated Cybersecurity]]></title>
  <link>http://ebiquity.umbc.edu/project/html/id/109/Accelerated-Cybersecurity</link>
  <description><![CDATA[We conduct cutting edge research to address the national and global challenges in Cybersecurity by leveraging Cognitive Computing approaches Accelerated to work in soft real time. We aim to create a platform for Academia, Industry and Government to collaborate seamlessly and to advance UMBCâ€™s position as a leading research university in cybersecurity-related disciplines. We are a member of IBMâ€™s AI Horizonâ€™s Network.]]></description>
  <dc:date>2016-09-01</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/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 how to leverage the emergent reasoning capabilities of large language models (LLMs) to detect inconsistencies between extracted facts and their provenance. With a focus on “open” LLMs t...]]></description>
  <dc:date>2024-03-25</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/1079/A-Study-of-the-Landscape-of-Privacy-Policies-of-Smart-Devices">
  <title><![CDATA[A Study of the Landscape of Privacy Policies of Smart Devices]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/1079/A-Study-of-the-Landscape-of-Privacy-Policies-of-Smart-Devices</link>
  <description><![CDATA[As the adoption of smart devices continues to permeate all aspects of our lives, concerns surrounding user privacy have become more pertinent than ever before. While privacy policies define the data management practices of their manufacturers, previous work has shown that they are rarely read and understood by users. Hence, automatic analysis of privacy policies has been shown to help provide users with appropriate insights. Previous research has extensively analyzed privacy policies of websi...]]></description>
  <dc:date>2023-12-23</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/1057/Towards-Semantic-Exploration-of-Tables-in-Scientific-Documents">
  <title><![CDATA[Towards Semantic Exploration of Tables in Scientific Documents]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/1057/Towards-Semantic-Exploration-of-Tables-in-Scientific-Documents</link>
  <description><![CDATA[Structured data artifacts such as tables are widely used in scientific literature to organize and concisely communicate important statistical information. Discovering relevant information in these tables remains a significant challenge owing to their structural heterogeneity, dense and often implicit semantics, and diffuse context. This paper describes how we leverage semantic technologies to enable technical experts to search and explore tabular data embedded within scientific documents. We ...]]></description>
  <dc:date>2023-05-28</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/1053/Offline-RL-CKG-A-hybrid-AI-model-for-cybersecurity-tasks">
  <title><![CDATA[Offline RL+CKG: A hybrid AI model for cybersecurity tasks]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/1053/Offline-RL-CKG-A-hybrid-AI-model-for-cybersecurity-tasks</link>
  <description><![CDATA[AI models for cybersecurity have to detect and defend against constantly evolving cyber threats. Much effort is spent building defenses for zero days and unseen variants of known cyber-attacks. Current AI models for cybersecurity struggle with these yet unseen threats due to the constantly evolving nature of threat vectors, vulnerabilities, and exploits. This paper shows that cybersecurity AI models will be improved and more general if we include semi-structured representations of background ...]]></description>
  <dc:date>2023-03-27</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/1122/Data-Quality-and-Linguistic-Cues-for-Domain-independent-Deception-Detection">
  <title><![CDATA[Data Quality and Linguistic Cues for Domain-independent Deception Detection]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/1122/Data-Quality-and-Linguistic-Cues-for-Domain-independent-Deception-Detection</link>
  <description><![CDATA[Deception is pervasive in today’s connected society and is being spread in a multitude of different forms with diverse goals, which we refer to as domains of deception. The most crucial research task in the field of deception is identification of deception, which in most cases involves a machine learning model making the binary classification of Deceptive or Not Deceptive. These classification models are very important as they can help protect the security of an organization by preventing p...]]></description>
  <dc:date>2022-12-06</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/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>2026-10-08</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/resource/html/id/382/A-Dynamic-Pipeline-for-Cybersecurity-Information-Extraction-and-Understanding">
  <title><![CDATA[A Dynamic Pipeline for Cybersecurity Information Extraction and Understanding]]></title>
  <link>http://ebiquity.umbc.edu/resource/html/id/382/A-Dynamic-Pipeline-for-Cybersecurity-Information-Extraction-and-Understanding</link>
  <description><![CDATA[This work was funded, in part, by a grant from the NSA through the On-Ramp program
and by the National Science Foundation under Grant Number 2114892.]]></description>
  <dc:date>2021-10-14</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/resource/html/id/355/Annotations-of-Cybersecurity-blogs-and-articles">
  <title><![CDATA[Annotations of Cybersecurity blogs and articles]]></title>
  <link>http://ebiquity.umbc.edu/resource/html/id/355/Annotations-of-Cybersecurity-blogs-and-articles</link>
  <description><![CDATA[This data is the result of a Master Thesis Project by Ravendar Lal under the supervision of Dr. Tim Finin. This dataset can be used for training technical systems. This dataset consists of manually data for cybersecurity domain where this data collection has the articles from CVES, Adobe Security Bulletins, Microsoft Security Bulletins and various blog posts. Total data has over 45,000 tokens and 5,000 tagged entities. Annotation was done by the Graduate Students of Computer Science Departmen...]]></description>
  <dc:date>2013-05-30</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/359/Extracting-Cybersecurity-related-entities-terms-and-concepts-from-text-">
  <title><![CDATA[Extracting Cybersecurity related entities, terms and concepts from text.]]></title>
  <link>http://ebiquity.umbc.edu/resource/html/id/359/Extracting-Cybersecurity-related-entities-terms-and-concepts-from-text-</link>
  <description><![CDATA[MS Thesis Presentation

Securing computers, data, cyber-physical systems and networks is a growing problem as society's dependence on them increases while they remain vulnerable to attacks by both criminals and rival nation states. Creating 'situationally aware' computer systems that defend against new "zero day" software vulnerabilities requires them to automatically integrate and use new security-related data from a wide variety of sources. One important source is information found in tex...]]></description>
  <dc:date>2013-05-30</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/resource/html/id/384/Extracting-Malware-Information-from-Cybersecurity-Text">
  <title><![CDATA[Extracting Malware Information from Cybersecurity Text]]></title>
  <link>http://ebiquity.umbc.edu/resource/html/id/384/Extracting-Malware-Information-from-Cybersecurity-Text</link>
  <description><![CDATA[Presentation given at the OnRamp II Symposium, 13-14 October 2021.

This work was funded, in part, by a grant from the NSA through the On-Ramp program and by the National Science Foundation under Grant Number 2114892.]]></description>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/resource/html/id/297/Improving-Accuracy-of-Named-Entity-Recognition-on-Social-Media-Data">
  <title><![CDATA[Improving Accuracy of Named Entity Recognition on Social Media Data]]></title>
  <link>http://ebiquity.umbc.edu/resource/html/id/297/Improving-Accuracy-of-Named-Entity-Recognition-on-Social-Media-Data</link>
  <description><![CDATA[We present a system for improving the accuracy of one NLP technique, Named Entity Recognition or NER, on Twitter data by training a recognizer specifically for this type of data.  This training data is obtained from the Amazon Mechanical Turk crowdsourcing platform.]]></description>
  <dc:date>2010-05-08</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/resource/html/id/156/Integrating-Language-Understanding-Agents-into-the-Semantic-Web">
  <title><![CDATA[Integrating Language Understanding Agents into the Semantic Web]]></title>
  <link>http://ebiquity.umbc.edu/resource/html/id/156/Integrating-Language-Understanding-Agents-into-the-Semantic-Web</link>
  <description><![CDATA[AAAI Fall Symposium session on Agents and Semantic Web presentation at Arlington Virginia Nov 4 2005]]></description>
  <dc:date>2005-11-05</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/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>
</rdf:RDF>
