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	This ontology document is licensed under the Creative Commons
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  <link><![CDATA[http://ebiquity.umbc.edu//tags/html/?t=inference]]></link>
  <description><![CDATA[UMBC ebiquity RSS Tag Search for inference]]></description>
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      <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/474/TABEL-A-Domain-Independent-and-Extensible-Framework-for-Inferring-the-Semantics-of-Tables"/>
      <rdf:li resource="http://ebiquity.umbc.edu/event/html/id/447/Generating-Linked-Data-from-Tables-"/>
      <rdf:li resource="http://ebiquity.umbc.edu/event/html/id/394/Privacy-Preservation-in-Context-Aware-Systems"/>
      <rdf:li resource="http://ebiquity.umbc.edu/event/html/id/387/Intelligent-Management-of-Distributed-Rack-Based-Blade-Operations"/>
      <rdf:li resource="http://ebiquity.umbc.edu/event/html/id/339/Trust-and-Reputation-in-Social-Networks"/>
      <rdf:li resource="http://ebiquity.umbc.edu/event/html/id/325/We-KnowItAll-lessons-from-a-Quarter-Century-of-Web-Extraction-Research"/>
      <rdf:li resource="http://ebiquity.umbc.edu/event/html/id/273/Constraint-Generation-and-Reasoning-in-OWL"/>
      <rdf:li resource="http://ebiquity.umbc.edu/event/html/id/246/Grammatical-Inference-Some-of-the-Questions-Out-There"/>
      <rdf:li resource="http://ebiquity.umbc.edu/event/html/id/217/Database-Infrastructure-for-the-Semantic-Web"/>
      <rdf:li resource="http://ebiquity.umbc.edu/getnews/html/id/38/Looking-back-at-the-ebiquity-research-group-s-2006"/>
      <rdf:li resource="http://ebiquity.umbc.edu/getnews/html/id/32/Zhongli-Ding-defends-dissertation"/>
      <rdf:li resource="http://ebiquity.umbc.edu/project/html/id/59/Bayes-OWL"/>
      <rdf:li resource="http://ebiquity.umbc.edu/project/html/id/20/OWLIR-Information-Retrieval-On-The-Semantic-Web"/>
      <rdf:li resource="http://ebiquity.umbc.edu/project/html/id/40/Presence-Monitoring-System"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/1203/DUNE-A-Machine-Learning-Deep-UNET-based-ensemble-Approach-to-Monthly-Seasonal-and-Annual-Climate-Forecasting"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/1064/A-General-Framework-for-Auditing-Differentially-Private-Machine-Learning"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/1044/The-SemIoTic-Ecosystem-A-Semantic-Bridge-between-IoT-Devices-and-Smart-Spaces"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/1015/CAPD-A-Context-Aware-Policy-Driven-Framework-for-Secure-and-Resilient-IoBT-Operations"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/1035/Interpretable-Explanations-for-Probabilistic-Inference-in-Markov-Logic"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/981/Understanding-Cybersecurity-Threat-Trends-through-Dynamic-Topic-Modeling"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/970/Locality-Preserving-Loss-Neighbors-that-Live-together-Align-together"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/956/A-Discrete-Variational-Recurrent-Topic-Model-without-the-Reparametrization-Trick"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/943/Knowledge-Graph-Inference-using-Tensor-Embedding"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/888/Temporal-Understanding-of-Cybersecurity-Threats"/>
      <rdf:li resource="http://ebiquity.umbc.edu/resource/html/id/75/An-Intelligent-Broker-for-Pervasive-Context-Aware-Systems"/>
      <rdf:li resource="http://ebiquity.umbc.edu/resource/html/id/162/BayesOWL-A-Probabilistic-Framework-for-Uncertainty-in-Semantic-Web"/>
      <rdf:li resource="http://ebiquity.umbc.edu/resource/html/id/163/BayesOWL-A-Probabilistic-Framework-for-Uncertainty-in-Semantic-Web-pdf-"/>
      <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/312/Privacy-Preservation-in-Context-Aware-Systems"/>
      <rdf:li resource="http://ebiquity.umbc.edu/resource/html/id/11/Resource-Guide-for-Trust-on-the-Semantic-Web"/>
      <rdf:li resource="http://ebiquity.umbc.edu/resource/html/id/188/Semantic-Web-Technologies-A-Tutorial"/>
      <rdf:li resource="http://ebiquity.umbc.edu/resource/html/id/194/Semantically-Linked-Bayesian-Networks"/>
      <rdf:li resource="http://ebiquity.umbc.edu/resource/html/id/103/SEMDIS-poster-March-2005-"/>
      <rdf:li resource="http://ebiquity.umbc.edu/resource/html/id/293/Trust-and-Reputation-in-Social-Networks"/>
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 </channel>
 <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/474/TABEL-A-Domain-Independent-and-Extensible-Framework-for-Inferring-the-Semantics-of-Tables">
  <title><![CDATA[TABEL -- A Domain Independent and Extensible Framework for Inferring the Semantics of Tables]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/474/TABEL-A-Domain-Independent-and-Extensible-Framework-for-Inferring-the-Semantics-of-Tables</link>
  <description><![CDATA[ 

 Dissertation Defense

Tables are an integral part of documents, reports and Web pages in many scientific and technical domains, compactly encoding important information that can be difficult to express in text. Table-like structures outside documents, such as spreadsheets, CSV files, log files and databases, are widely used to represent and share information. However, tables remain beyond the scope of regular text processing systems which often treat them like free text.

This ...]]></description>
  <dc:date>2015-01-08</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/447/Generating-Linked-Data-from-Tables-">
  <title><![CDATA[Generating Linked Data from Tables.]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/447/Generating-Linked-Data-from-Tables-</link>
  <description><![CDATA[Large amounts of information is stored in tables, spreadsheets, CSV files and databases for a number of domains, including the Web, healthcare, e-science and public policy. The tables' structure facilitates human understanding, yet this very structure makes it difficult for machine understanding. This talk will focus on describing our work on making the intended meaning of tabular data explicit by representing it as RDF linked data, potentially making large amounts of scientific and medical d...]]></description>
  <dc:date>2013-03-24</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/394/Privacy-Preservation-in-Context-Aware-Systems">
  <title><![CDATA[Privacy Preservation in Context-Aware Systems]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/394/Privacy-Preservation-in-Context-Aware-Systems</link>
  <description><![CDATA[Pramod Jagtap will defend his MS thesis titled "Privacy Preservation in Context-Aware Systems". 

Abstract: 
 
Recent years have seen a confluence of two major trends – the increase of mobile devices such as smart phones as the primary access point to networked information and the rise of social media platforms that connect people. Their convergence supports the emergence of a new class of context-aware geosocial networking applications. While existing systems focus mostly on location...]]></description>
  <dc:date>2011-04-27</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/387/Intelligent-Management-of-Distributed-Rack-Based-Blade-Operations">
  <title><![CDATA[Intelligent Management of Distributed Rack-Based Blade Operations]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/387/Intelligent-Management-of-Distributed-Rack-Based-Blade-Operations</link>
  <description><![CDATA[This presentation will cover research addressing management and operations problems in distributed rack-based blade systems. Specifically, the focus will be on handling distributed configuration management of system-level parameters and optimizing job placement in a distributed system using thermal exhaust characteristics. I will cover not only why these particular issues are important to the community, but how statistical inference models can be used to determine correctness of configuration...]]></description>
  <dc:date>2011-03-29</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/339/Trust-and-Reputation-in-Social-Networks">
  <title><![CDATA[Trust and Reputation in Social Networks]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/339/Trust-and-Reputation-in-Social-Networks</link>
  <description><![CDATA[Trust is a statement (or prediction of reliance) about what is otherwise unknown or uncertain -- for example, because it is far away, cannot be verified, or is in the future. Trust is pervasive and beneficial in complex social systems. It can be built from direct interactions between the source party (truster) and the target (trustee). However, in large open systems, it is infeasible for each party to have a direct basis for trusting another party. Therefore, the participants in an open syste...]]></description>
  <dc:date>2010-03-30</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/325/We-KnowItAll-lessons-from-a-Quarter-Century-of-Web-Extraction-Research">
  <title><![CDATA[We KnowItAll: lessons from a Quarter Century  of Web Extraction Research]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/325/We-KnowItAll-lessons-from-a-Quarter-Century-of-Web-Extraction-Research</link>
  <description><![CDATA[For the last quarter century (measured in person years), the KnowItAll project has investigated information extraction at Web scale. If successful, this effort will begin to address the long-standing "Knowledge Acquisition Bottleneck" in Artificial Intelligence, and will enable a new generation of search engines that extract and synthesize information from text to answer complex user queries. To date, we have generalized information extraction methods to process arbitrary Web text, to handle ...]]></description>
  <dc:date>2009-11-10</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/273/Constraint-Generation-and-Reasoning-in-OWL">
  <title><![CDATA[Constraint Generation and Reasoning in OWL]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/273/Constraint-Generation-and-Reasoning-in-OWL</link>
  <description><![CDATA[The majority of OWL ontologies in the emerging Semantic Web are constructed from properties that lack domain and range constraints. Constraints in OWL are different from the familiar uses in programming languages and databases, and are actually type assertions that are made about the individuals which are connected by the property. These assertions can add vital information to the model because they are assertions of type on the individuals involved, and they can also give information on how ...]]></description>
  <dc:date>2008-11-17</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/246/Grammatical-Inference-Some-of-the-Questions-Out-There">
  <title><![CDATA[Grammatical Inference: Some of the Questions Out There]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/246/Grammatical-Inference-Some-of-the-Questions-Out-There</link>
  <description><![CDATA[Grammatical Inference is a field concerned with learning
grammars given data about a language.  In this talk we
survey some of the questions being addressed by researchers
in the field.  Some of these are now classical and have been
looked into for some time, others are more recent:

understanding the models and the paradigms:
what does polynomial language learning mean?

learning more complex families of languages

scaling up and using grammatical inference in applications]]></description>
  <dc:date>2008-06-10</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/217/Database-Infrastructure-for-the-Semantic-Web">
  <title><![CDATA[Database Infrastructure for the Semantic Web]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/217/Database-Infrastructure-for-the-Semantic-Web</link>
  <description><![CDATA[Oracle Database has support for native storage, querying and inference of semantic datasets containing hundreds of millions to billions of triples. This scalable and secure infrastructure can be used to build applications for data integration, metadata (knowledge) representation, ontology usage and management, ontology enhanced search, and so on. Several new features have been added to the database infrastructure support:

    Native inferencing for a part of OWL (basic constructs, property...]]></description>
  <dc:date>2007-09-14</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/getnews/html/id/38/Looking-back-at-the-ebiquity-research-group-s-2006">
  <title><![CDATA[Looking back at the ebiquity research group's 2006]]></title>
  <link>http://ebiquity.umbc.edu/getnews/html/id/38/Looking-back-at-the-ebiquity-research-group-s-2006</link>
  <description><![CDATA[Maybe it's a bit of a cliché, but this is the traditional time to look back on the past year and reflect on how things are going.  It has been an active productive year.  Here's a rundown of our past year by the numbers.

205,000 is the number of visits to the Ebiquity web site.  Our monthly page visits increased five fold over the year and we currently receive about 25,000 visits a month.


   



743 people have
registered as users of the Swoogle
semantic web search system.  By ...]]></description>
  <dc:date>2007-01-01</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/getnews/html/id/32/Zhongli-Ding-defends-dissertation">
  <title><![CDATA[Zhongli Ding defends dissertation]]></title>
  <link>http://ebiquity.umbc.edu/getnews/html/id/32/Zhongli-Ding-defends-dissertation</link>
  <description><![CDATA[Zhongli Ding successfully defended her Ph.D. dissertation
entitled "BayesOWL: A Probabilistic Framework for Uncertainty in
Semantic Web" on December 5, 2005.  Dr. Ding came to UMBC in the Fall
of 1999 after receiving her undergraduate degree from the University
of Science and Technology of China in Hefei.  She joined the ebquity
lab in 2000 and has worked closely with Professor Yun Peng, who was
her mentor and dissertation supervisor.  She received a Masters degree in
Computer Scie...]]></description>
  <dc:date>2005-12-05</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/project/html/id/59/Bayes-OWL">
  <title><![CDATA[Bayes OWL]]></title>
  <link>http://ebiquity.umbc.edu/project/html/id/59/Bayes-OWL</link>
  <description><![CDATA[Dealing with uncertainty is crucial in ontology engineering tasks such
as domain modeling, ontology reasoning, and concept mapping between
ontologies. The Bayes OWL project addresses this problem by exploring
how uncertainty can be modeled in ontologies using Bayesian networks
(BN). Our approach involves extending OWL to allow additional
probabilistic markups for attaching probability information.  Having
done so, we can directly convert a probabilistically annotated OWL
ontology into ...]]></description>
  <dc:date>2003-09-01</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/project/html/id/20/OWLIR-Information-Retrieval-On-The-Semantic-Web">
  <title><![CDATA[OWLIR: Information Retrieval On The Semantic Web]]></title>
  <link>http://ebiquity.umbc.edu/project/html/id/20/OWLIR-Information-Retrieval-On-The-Semantic-Web</link>
  <description><![CDATA[Information in the web is presented in human understandable form
  .Current search engines operate on huge databases and the
  information retrieval techniques are keyword based indexes on
  Textual Data.So,not all retrieved documents answer a user's query
  and is limited in its automated inference capability.
We envision the future web as pages containing both text and
semantic markup.We describe an approach for information retrieval over
documents that consist of both free text an...]]></description>
  <dc:date>2000-12-01</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/project/html/id/40/Presence-Monitoring-System">
  <title><![CDATA[Presence Monitoring System]]></title>
  <link>http://ebiquity.umbc.edu/project/html/id/40/Presence-Monitoring-System</link>
  <description><![CDATA[Objective. The objective is to develop a distributed system that is capable of monitoring user presence in an office environment and reporting the updated information to the registered context brokers. This monitoring system will use different sensing techniques to detect user presence, for example, detecting the network connections made by Bluetooth devices, monitoring user logins on the UNIX servers and desktop computers, reading the RFID tags attached to the devices that a user carries, an...]]></description>
  <dc:date>2003-11-01</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/1203/DUNE-A-Machine-Learning-Deep-UNET-based-ensemble-Approach-to-Monthly-Seasonal-and-Annual-Climate-Forecasting">
  <title><![CDATA[DUNE: A Machine Learning Deep UNET++ based ensemble Approach to Monthly, Seasonal and Annual Climate Forecasting]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/1203/DUNE-A-Machine-Learning-Deep-UNET-based-ensemble-Approach-to-Monthly-Seasonal-and-Annual-Climate-Forecasting</link>
  <description><![CDATA[Capitalizing on the recent availability of ERA5 monthly averaged, long-term data records of mean atmospheric and climate fields derived from the high-resolution reanalysis, deep learning architectures provide an alternative to physics-based daily numerical weather predictions for subseasonal to seasonal (S2S) and annual forecasts. A novel deep U-Net++-based ensemble (DUNE) neural architecture is introduced, incorporating encoder–decoder structures with residual blocks. When initialized with...]]></description>
  <dc:date>2025-10-01</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/1064/A-General-Framework-for-Auditing-Differentially-Private-Machine-Learning">
  <title><![CDATA[A General Framework for Auditing Differentially Private Machine Learning]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/1064/A-General-Framework-for-Auditing-Differentially-Private-Machine-Learning</link>
  <description><![CDATA[We present a framework to statistically audit the privacy guarantee conferred by a differentially private machine learner in practice. While previous works have taken steps toward evaluating privacy loss through poisoning attacks or membership inference, they have been tailored to specific models or have demonstrated low statistical power. Our work develops a general methodology to empirically evaluate the privacy of differentially private machine learning implementations, combining improved ...]]></description>
  <dc:date>2022-11-28</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/1044/The-SemIoTic-Ecosystem-A-Semantic-Bridge-between-IoT-Devices-and-Smart-Spaces">
  <title><![CDATA[The SemIoTic Ecosystem: A Semantic Bridge between IoT Devices and Smart Spaces]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/1044/The-SemIoTic-Ecosystem-A-Semantic-Bridge-between-IoT-Devices-and-Smart-Spaces</link>
  <description><![CDATA[Smart space administration and application development is challenging in part due to the semantic gap that exists between the high-level requirements of users and the low-level capabilities of IoT devices. The stakeholders in a smart space are required to deal with communicating with specific IoT devices, capturing data, processing it, and abstracting it out to generate useful inferences. Additionally, this makes reusability of smart space applications difficult since they are developed for s...]]></description>
  <dc:date>2022-08-01</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/1015/CAPD-A-Context-Aware-Policy-Driven-Framework-for-Secure-and-Resilient-IoBT-Operations">
  <title><![CDATA[CAPD: A Context-Aware, Policy-Driven Framework for Secure and Resilient IoBT Operations]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/1015/CAPD-A-Context-Aware-Policy-Driven-Framework-for-Secure-and-Resilient-IoBT-Operations</link>
  <description><![CDATA[The Internet of Battlefield Things (IoBT) will advance the operational effectiveness of infantry units. However, this requires autonomous assets such as sensors, drones, combat equipment, and uncrewed vehicles to collaborate, securely share information, and be resilient to adversary attacks in contested multi-domain operations. CAPD addresses this problem by providing a context-aware, policy-driven framework supporting data and knowledge exchange among autonomous entities in a battlespace. We...]]></description>
  <dc:date>2022-04-04</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/1035/Interpretable-Explanations-for-Probabilistic-Inference-in-Markov-Logic">
  <title><![CDATA[Interpretable Explanations for Probabilistic Inference in Markov Logic]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/1035/Interpretable-Explanations-for-Probabilistic-Inference-in-Markov-Logic</link>
  <description><![CDATA[Markov Logic Networks (MLNs) represent relational knowledge using a combination of first-order logic and probabilistic models. In this paper, we develop an approach to explain the results of probabilistic inference in MLNs. Unlike approaches such as LIME and SHAP that explain black-box classifiers, e explaining MLN inference is harder since the data is interconnected. We develop an explanation framework that computes importance weights for MLN formulas based on their influence on the marginal...]]></description>
  <dc:date>2021-12-15</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/981/Understanding-Cybersecurity-Threat-Trends-through-Dynamic-Topic-Modeling">
  <title><![CDATA[Understanding Cybersecurity Threat Trends through Dynamic Topic Modeling]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/981/Understanding-Cybersecurity-Threat-Trends-through-Dynamic-Topic-Modeling</link>
  <description><![CDATA[Cybersecurity threats continue to increase and are impacting almost all aspects of modern life. Being aware of how vulnerabilities and their exploits are changing gives helpful insights into combating new threats. Applying dynamic topic modeling to a timestamped cybersecurity document collection shows how the significance and details of concepts found in them are evolving.  We correlate two different temporal corpora, one with reports about specific exploits and another with research-oriented...]]></description>
  <dc:date>2021-06-01</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/970/Locality-Preserving-Loss-Neighbors-that-Live-together-Align-together">
  <title><![CDATA[Locality Preserving Loss: Neighbors that Live together, Align together]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/970/Locality-Preserving-Loss-Neighbors-that-Live-together-Align-together</link>
  <description><![CDATA[We present a locality preserving loss (LPL) that improves the alignment between vector space embeddings while separating uncorrelated representations. Given two pretrained embedding manifolds, LPL optimizes a model to project an embedding and maintain its local neighborhood while aligning one manifold to another. This reduces the overall size of the dataset required to align the two in tasks such as crosslingual word alignment. We show that the LPL-based alignment between input vector spaces ...]]></description>
  <dc:date>2021-04-19</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/956/A-Discrete-Variational-Recurrent-Topic-Model-without-the-Reparametrization-Trick">
  <title><![CDATA[A Discrete Variational Recurrent Topic Model without the Reparametrization Trick]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/956/A-Discrete-Variational-Recurrent-Topic-Model-without-the-Reparametrization-Trick</link>
  <description><![CDATA[We show how to learn a neural topic model with discrete random variables---one that explicitly models each word's assigned topic---using neural variational inference that does not rely on stochastic backpropagation to handle the discrete variables. The model we utilize combines the expressive power of neural methods for representing sequences of text with the topic model's ability to capture global, thematic coherence. Using neural variational inference, we show improved perplexity and docume...]]></description>
  <dc:date>2020-12-06</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/943/Knowledge-Graph-Inference-using-Tensor-Embedding">
  <title><![CDATA[Knowledge Graph Inference using Tensor Embedding]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/943/Knowledge-Graph-Inference-using-Tensor-Embedding</link>
  <description><![CDATA[Axiom based inference provides a  clear and consistent way of reasoning to add more information to a knowledge graph.  However, constructing a set of axioms is expensive and requires domain expertise, time, and money.  It is also difficult to reuse or adapt a set of axioms to a knowledge graph in a new domain or even in the same domain but using a slightly different representation approach. This work makes three main contributions,  it (1) provides a family of representation learning algorith...]]></description>
  <dc:date>2020-09-12</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/888/Temporal-Understanding-of-Cybersecurity-Threats">
  <title><![CDATA[Temporal Understanding of Cybersecurity Threats]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/888/Temporal-Understanding-of-Cybersecurity-Threats</link>
  <description><![CDATA[As cybersecurity-related threats continue to increase, understanding how the field is changing over time can give insight into combating new threats and understanding historical events. We show how to apply dynamic topic models to a set of cybersecurity documents to understand how the concepts found in them are changing over time.  We correlate two different data sets, the first relates to specific exploits, and the second relates to cybersecurity research. We use Wikipedia concepts to provid...]]></description>
  <dc:date>2020-05-25</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/resource/html/id/75/An-Intelligent-Broker-for-Pervasive-Context-Aware-Systems">
  <title><![CDATA[An Intelligent Broker for Pervasive Context-Aware Systems]]></title>
  <link>http://ebiquity.umbc.edu/resource/html/id/75/An-Intelligent-Broker-for-Pervasive-Context-Aware-Systems</link>
  <description><![CDATA[Presentation slides used in Harry Chen's PhD dissertation defense.

Context-aware systems exploit the use of situational information, or context, to provide relevant information and services to users. A great challenge remains in defining an architecture that supports context-aware systems. Critical research issues include modeling and reasoning (how to represent contextual information for machine processing and reasoning), knowledge sharing (how to enable agents to acquire consistent knowl...]]></description>
  <dc:date>2004-12-07</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/resource/html/id/162/BayesOWL-A-Probabilistic-Framework-for-Uncertainty-in-Semantic-Web">
  <title><![CDATA[BayesOWL: A Probabilistic Framework for Uncertainty in Semantic Web]]></title>
  <link>http://ebiquity.umbc.edu/resource/html/id/162/BayesOWL-A-Probabilistic-Framework-for-Uncertainty-in-Semantic-Web</link>
  <description><![CDATA[Ph.D. Dissertation Defense
To address the difficult but important problem of modeling uncertainty in semantic web, this research has taken a probabilistic approach and developed a theoretical framework, named BayesOWL, that incorporates the Bayesian network (BN), a widely used graphic model for probabilistic interdependency, into the web ontology language OWL. This framework consists of three key components:

 a representation for encoding the probability distributions as OWL classes;
 a...]]></description>
  <dc:date>2005-12-05</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/resource/html/id/163/BayesOWL-A-Probabilistic-Framework-for-Uncertainty-in-Semantic-Web-pdf-">
  <title><![CDATA[BayesOWL: A Probabilistic Framework for Uncertainty in Semantic Web (pdf)]]></title>
  <link>http://ebiquity.umbc.edu/resource/html/id/163/BayesOWL-A-Probabilistic-Framework-for-Uncertainty-in-Semantic-Web-pdf-</link>
  <description><![CDATA[Ph.D. Dissertation Defense
To address the difficult but important problem of modeling uncertainty in semantic web, this research has taken a probabilistic approach and developed a theoretical framework, named BayesOWL, that incorporates the Bayesian network (BN), a widely used graphic model for probabilistic interdependency, into the web ontology language OWL. This framework consists of three key components:

 a representation for encoding the probability distributions as OWL classes;
 a...]]></description>
  <dc:date>2005-12-05</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/312/Privacy-Preservation-in-Context-Aware-Systems">
  <title><![CDATA[Privacy Preservation in Context-Aware Systems]]></title>
  <link>http://ebiquity.umbc.edu/resource/html/id/312/Privacy-Preservation-in-Context-Aware-Systems</link>
  <description><![CDATA[Recent years have seen a confluence of two major trends – the increase of mobile devices such as smart phones as the primary access point to networked information and the rise of social media platforms that connect people. Their convergence supports the emergence of a new class of context-aware geosocial networking applications. While existing systems focus mostly on location, our work centers on models for representing and reasoning about a more inclusive and higher-level notion of context...]]></description>
  <dc:date>2011-04-27</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/resource/html/id/11/Resource-Guide-for-Trust-on-the-Semantic-Web">
  <title><![CDATA[Resource Guide for Trust on the Semantic Web]]></title>
  <link>http://ebiquity.umbc.edu/resource/html/id/11/Resource-Guide-for-Trust-on-the-Semantic-Web</link>
  <description><![CDATA[This page is a collection of information and resources about trust research on the semantic web. Our interests includes but not limited in trust representation, trust inference and trust based application.  Trust representation includes trust ontology development. Trust inference includes trust learning (how to generate trust knowledge from our daily experience), trust network inference (how to derive trust knowledge from learned trust knowledge), and trust based inference (how to use trust k...]]></description>
  <dc:date>2003-11-04</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/resource/html/id/188/Semantic-Web-Technologies-A-Tutorial">
  <title><![CDATA[Semantic Web Technologies: A Tutorial]]></title>
  <link>http://ebiquity.umbc.edu/resource/html/id/188/Semantic-Web-Technologies-A-Tutorial</link>
  <description><![CDATA[People become "smarter" by using web search engines like Google to obtain relevant and useful information from the World Wide Web. The Semantic Web aims at making such knowledge more accessible to computer programs, i.e. building “a web of data, in some ways like a global database” as suggested by Tim Berners-Lee.  This tutorial gives an overview of Semantic Web technologies and their utility.

The Semantic Web technologies can be studied from two aspects. From the web aspect, Semantic ...]]></description>
  <dc:date>2006-07-18</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/resource/html/id/194/Semantically-Linked-Bayesian-Networks">
  <title><![CDATA[Semantically-Linked Bayesian Networks]]></title>
  <link>http://ebiquity.umbc.edu/resource/html/id/194/Semantically-Linked-Bayesian-Networks</link>
  <description><![CDATA[At the present time, Bayesian networks (BNs), presumably the most popular uncertainty inference framework, are still widely used as standalone systems. When the problem itself is distributed, domain knowledge has to be centralized and unified before a single BN can be created. Alternatively, separate BNs describing related sub-domains or different aspects of the same domain may be created, but it is difficult to combine them for problem solving even if the interdependent relations between var...]]></description>
  <dc:date>2006-08-02</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/resource/html/id/103/SEMDIS-poster-March-2005-">
  <title><![CDATA[SEMDIS poster (March 2005)]]></title>
  <link>http://ebiquity.umbc.edu/resource/html/id/103/SEMDIS-poster-March-2005-</link>
  <description><![CDATA[Discovering and evaluating interesting patterns and semantic
associations in vast amount of information provided by many different
sources is an important and time-consuming work for homeland security
analysts. By publishing or converting such information in semantic web
language, intelligent agents can automate the inference without
compromising the semantics. This paper describes how trust and
provenance can be represented/obtained in the SemanticWeb and then be
used to evaluate trus...]]></description>
  <dc:date>2005-03-17</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/resource/html/id/293/Trust-and-Reputation-in-Social-Networks">
  <title><![CDATA[Trust and Reputation in Social Networks]]></title>
  <link>http://ebiquity.umbc.edu/resource/html/id/293/Trust-and-Reputation-in-Social-Networks</link>
  <description><![CDATA[Trust is a statement (or prediction of reliance) about what is otherwise
unknown or uncertain -- for example, because it is far away, cannot be
verified, or is in the future. Trust is pervasive and beneficial in complex
social systems. It can be built from direct interactions between the source
party (truster) and the target (trustee). However, in large open systems, it
is infeasible for each party to have a direct basis for trusting another
party. Therefore, the participants in an open...]]></description>
  <dc:date>2010-03-30</dc:date>
 </item>
</rdf:RDF>
