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 <channel rdf:about="http://ebiquity.umbc.edu/tag/learning/">
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    <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/75/Feeds-that-matter"/>
    <rdf:li resource="http://ebiquity.umbc.edu/project/html/id/42/flipix"/>
    <rdf:li resource="http://ebiquity.umbc.edu/project/html/id/68/memeta"/>
    <rdf:li resource="http://ebiquity.umbc.edu/project/html/id/96/Tables-to-Linked-Data"/>
    <rdf:li resource="http://ebiquity.umbc.edu/project/html/id/57/Text-Mining-Approach-to-Ontology-Enrichment"/>
    <rdf:li resource="http://ebiquity.umbc.edu/project/html/id/80/XPod"/>
    <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/552/Automatically-Generating-Government-Linked-Data-from-Tables"/>
    <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/546/SAT-an-SVM-based-Automated-Trust-Management-System-for-Mobile-Ad-hoc-Networks"/>
    <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/551/DC-Proposal-Graphical-Models-and-Probabilistic-Reasoning-for-Generating-Linked-Data-from-Tables"/>
    <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/548/Content-based-prediction-of-temporal-boundaries-for-events-in-Twitter"/>
    <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/539/Mobile-Collaborative-Context-Aware-Systems"/>
    <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/541/Context-Aware-Middleware-for-Activity-Recognition"/>
    <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/503/Learning-Co-reference-Relations-for-FOAF-Instances"/>
    <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/506/Computing-FOAF-Co-reference-Relations-with-Rules-and-Machine-Learning"/>
    <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/477/A-Hybrid-Approach-to-Unsupervised-Relation-Discovery-Based-on-Linguistic-Analysis-and-Semantic-Typing"/>
    <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/475/Unsupervised-techniques-for-discovering-ontology-elements-from-Wikipedia-article-links"/>
    <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/478/Automatic-Discovery-of-Semantic-Relations-using-MindNet"/>
    <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/471/A-Machine-Learning-Approach-to-Linking-FOAF-Instances"/>
    <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/461/Ensembles-in-Adversarial-Classification-for-Spam"/>
    <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/460/Improving-Binary-Classification-on-Text-Problems-using-Differential-Word-Features"/>
    <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/448/Delta-TFIDF-An-Improved-Feature-Space-for-Sentiment-Analysis"/>
    <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/377/Learning-the-Semantic-Meaning-of-a-Concept-from-the-Web"/>
    <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/335/XPod-A-Human-Activity-Aware-Learning-Mobile-Music-Player"/>
    <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/296/Detecting-Spam-Blogs-A-Machine-Learning-Approach"/>
    <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/269/SVMs-for-the-Blogosphere-Blog-Identification-and-Splog-Detection"/>
    <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/236/Modifying-Bayesian-Networks-by-Probability-Constraints"/>
    <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/171/Mining-Domain-Specific-Texts-and-Glossaries-to-Evaluate-and-Enrich-Domain-Ontologies"/>
    <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/234/Communicating-neural-network-knowledge-between-agents-in-a-simulated-aerial-reconnaissance-system"/>
    <rdf:li resource="http://ebiquity.umbc.edu/resource/html/id/333/Automatically-Generating-Linked-Data-from-Tables"/>
    <rdf:li resource="http://ebiquity.umbc.edu/resource/html/id/306/Detecting-Domain-Shift"/>
    <rdf:li resource="http://ebiquity.umbc.edu/resource/html/id/140/Recognizing-Activities-using-RFID"/>
    <rdf:li resource="http://ebiquity.umbc.edu/resource/html/id/141/Recognizing-Activities-using-RFID"/>
    <rdf:li resource="http://ebiquity.umbc.edu/resource/html/id/208/XPod-IJCAI-Presentation"/>
    <rdf:li resource="http://ebiquity.umbc.edu/event/html/id/403/Mid-Atlantic-Student-Colloquium-on-Speech-Language-and-Learning"/>
    <rdf:li resource="http://ebiquity.umbc.edu/event/html/id/381/Domain-Independent-Sentiment-Analysis"/>
    <rdf:li resource="http://ebiquity.umbc.edu/event/html/id/357/Detecting-Domain-Shift"/>
    <rdf:li resource="http://ebiquity.umbc.edu/event/html/id/352/Prediction-of-Oscar-Award-Nominations-Based-on-Movie-Scripts"/>
    <rdf:li resource="http://ebiquity.umbc.edu/event/html/id/342/Cost-Sensitive-Information-Acquisition-for-Prediction-"/>
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    <rdf:li resource="http://ebiquity.umbc.edu/event/html/id/237/Research-Challenges-In-Data-Mining"/>
    <rdf:li resource="http://ebiquity.umbc.edu/event/html/id/215/Empowering-Scientific-Discovery-by-Distributed-Data-Mining-on-the-Grid-Infrastructure"/>
    <rdf:li resource="http://ebiquity.umbc.edu/event/html/id/204/Transfer-in-the-Context-of-Reinforcement-Learning-by-Mapping-Q-Tables"/>
    <rdf:li resource="http://ebiquity.umbc.edu/event/html/id/205/Data-Clustering-with-a-Relational-Push-Pull-Model"/>
    <rdf:li resource="http://ebiquity.umbc.edu/event/html/id/193/Knowledge-Transfer-using-Multiresolution-Learning"/>
    <rdf:li resource="http://ebiquity.umbc.edu/event/html/id/188/Real-Time-Identification-of-Operating-Room-State-from-Video"/>
    <rdf:li resource="http://ebiquity.umbc.edu/event/html/id/171/Cost-Sensitive-Classifier-Evaluation-Using-Cost-Curves"/>
    <rdf:li resource="http://ebiquity.umbc.edu/event/html/id/166/Learning-the-Semantic-Meaning-of-a-Concept-from-the-Web"/>
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 <image rdf:about="http://ebiquity.umbc.edu/img/logo.jpg">
  <title>UMBC ebiquity research group</title>
  <link>http://ebiquity.umbc.edu</link>
  <url>http://ebiquity.umbc.edu/img/logo.jpg</url>
 </image>
 <item rdf:about="http://ebiquity.umbc.edu/research/area/id/14/Data-Mining">
  <title><![CDATA[Data Mining]]></title>
  <link>http://ebiquity.umbc.edu/research/area/id/14/Data-Mining</link>
 </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/75/Feeds-that-matter">
  <title><![CDATA[Feeds that matter]]></title>
  <link>http://ebiquity.umbc.edu/project/html/id/75/Feeds-that-matter</link>
  <description><![CDATA[Finding good feeds is getting harder as the Blogosphere grows. We analyze the Bloglines public feed subscriptions and describe techniques to induce an intuitive set of feed topics. The FTM! prototype service uses a ranked list of the "feeds that matter" for each topic to allow users to browse the catalog and subscribe to interesting feeds.]]></description>
  <dc:date>2006-05-01</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/project/html/id/42/flipix">
  <title><![CDATA[flipix]]></title>
  <link>http://ebiquity.umbc.edu/project/html/id/42/flipix</link>
  <description><![CDATA[Create a program that  can automatically rotate an image from a digital camera so that it is oriented correctly.   A machine learning approach will be used to develop a model that can predict the proper orientation from low level image features.  If interested, contact Tim Finin.]]></description>
  <dc:date>2003-12-01</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/project/html/id/68/memeta">
  <title><![CDATA[memeta]]></title>
  <link>http://ebiquity.umbc.edu/project/html/id/68/memeta</link>
  <description><![CDATA[Weblogs, or blogs, have become an important new way to publish information, engage in discussions and form communities. The memeta project is developing a framework for representing and studying the structure and content of communities of blogs. We are particularly interested in how metadata about blogs can be extracted, discovered and computed and how that metadata can be used in the analysis of blogs and to provide new blog related services.  Examples of concrete problems we hope to be able...]]></description>
  <dc:date>2005-03-01</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/project/html/id/96/Tables-to-Linked-Data">
  <title><![CDATA[Tables to Linked Data]]></title>
  <link>http://ebiquity.umbc.edu/project/html/id/96/Tables-to-Linked-Data</link>
  <description><![CDATA[Vast amounts of information is encoded in tables found in documents, on the Web, and in spreadsheets or databases. Integrating or searching over this information benefits from understanding its intended meaning and making it explicit in a semantic representation language like RDF. Most current approaches to generating Semantic Web representations from tables requires human input to create schemas and often results in graphs that do not follow best practices for linked data. Evidence for a tab...]]></description>
  <dc:date>2010-09-01</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/project/html/id/57/Text-Mining-Approach-to-Ontology-Enrichment">
  <title><![CDATA[Text Mining Approach to Ontology Enrichment]]></title>
  <link>http://ebiquity.umbc.edu/project/html/id/57/Text-Mining-Approach-to-Ontology-Enrichment</link>
  <description><![CDATA[Ontologies have been widely accepted as the most advanced knowledge representation model. They are among the most important building blocks of semantic web, hence, very crucial for the success of semantic web. Huge effort is needed from the domain expert in order to construct ontologies manually. There is a need for semi-automatic approach in ontology building which will help the domain expert in constructing extensive domain ontologies efficiently. We propose the use of text mining technique...]]></description>
  <dc:date>2003-10-01</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/project/html/id/80/XPod">
  <title><![CDATA[XPod]]></title>
  <link>http://ebiquity.umbc.edu/project/html/id/80/XPod</link>
  <description><![CDATA[The XPod system aims
to integrate awareness of human activity and musical
preferences to produce an adaptive system
that plays the contextually correct music. The
XPod project introduces a “smart” music player
that learns its user’s preferences and activity, and
tailors its music selections accordingly. We are using
a BodyMedia device that has been shown to accurately
measure a user’s physiological state. The
device is able to monitor a number of variables to
determine its u...]]></description>
  <dc:date>2004-01-01</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/552/Automatically-Generating-Government-Linked-Data-from-Tables">
  <title><![CDATA[Automatically Generating Government Linked Data from Tables]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/552/Automatically-Generating-Government-Linked-Data-from-Tables</link>
  <description><![CDATA[Most open government data is encoded and published
in structured tables found in reports, on the Web, and in
spreadsheets or databases. Current approaches to generating
Semantic Web representations from such data requires
human input to create schemas and often results
in graphs that do not follow best practices for linked
data. Evidence for a table’s meaning can be found in its
column headers, cell values, implicit relations between
columns, caption and surrounding text but also re...]]></description>
  <dc:date>2011-11-04</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/546/SAT-an-SVM-based-Automated-Trust-Management-System-for-Mobile-Ad-hoc-Networks">
  <title><![CDATA[SAT: an SVM-based Automated Trust Management System for Mobile Ad-hoc Networks]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/546/SAT-an-SVM-based-Automated-Trust-Management-System-for-Mobile-Ad-hoc-Networks</link>
  <description><![CDATA[Mobile Ad-hoc Networks (MANETs) are extremely vulnerable to a variety of misbehaviors because of their basic features, including lack of communication infrastructure, short transmission range, and dynamic network topology. To detect and mitigate those misbehaviors, many trust management schemes have been proposed for MANETs. Most rely on pre-defined weights to determine how each apparent misbehavior contributes to an overall measure of trustworthiness. The extremely dynamic nature of MANETs m...]]></description>
  <dc:date>2011-11-01</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/551/DC-Proposal-Graphical-Models-and-Probabilistic-Reasoning-for-Generating-Linked-Data-from-Tables">
  <title><![CDATA[DC Proposal: Graphical Models and Probabilistic Reasoning for Generating Linked Data from Tables]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/551/DC-Proposal-Graphical-Models-and-Probabilistic-Reasoning-for-Generating-Linked-Data-from-Tables</link>
  <description><![CDATA[Vast amounts of information is encoded in tables found in
documents, on the Web, and in spreadsheets or databases. Integrating or
searching over this information benefits from understanding its intended
meaning and making it explicit in a semantic representation language
like RDF. Most current approaches to generating Semantic Web representations
from tables requires human input to create schemas and
often results in graphs that do not follow best practices for linked data.
Evidence fo...]]></description>
  <dc:date>2011-10-24</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/548/Content-based-prediction-of-temporal-boundaries-for-events-in-Twitter">
  <title><![CDATA[Content-based prediction of temporal boundaries for events in Twitter]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/548/Content-based-prediction-of-temporal-boundaries-for-events-in-Twitter</link>
  <description><![CDATA[Social media services like Twitter, Flickr and YouTube publish high volumes of user generated content as a major event occurs, making them a potential data source for event analysis. The large volume and noisy content of social media makes automatic preprocessing essential. Intuitively, the eventrelated data falls into three major phases: the buildup to the event, the event itself, and the post-event effects and repercussions.  We describe an approach to automatically determine when an antici...]]></description>
  <dc:date>2011-10-09</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/539/Mobile-Collaborative-Context-Aware-Systems">
  <title><![CDATA[Mobile, Collaborative, Context-Aware Systems]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/539/Mobile-Collaborative-Context-Aware-Systems</link>
  <description><![CDATA[We describe work on representing and using a rich notion of context that goes beyond current networking applications focusing mostly on location.  Our context model includes location and surroundings, the presence of people and devices, inferred activities and the roles people fill in them. A key element of our work is the use of collaborative information sharing where devices share and integrate knowledge about their context. This introduces a requirement that users can set appropriate level...]]></description>
  <dc:date>2011-08-07</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/541/Context-Aware-Middleware-for-Activity-Recognition">
  <title><![CDATA[Context-Aware Middleware for Activity Recognition]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/541/Context-Aware-Middleware-for-Activity-Recognition</link>
  <description><![CDATA[Smart phones and other mobile devices have a simple notion of context largely restricted to temporal and spatial coordinates. Service providers and enterprise administrators can deploy systems incorporating activity and relations context to enhance the user experience, but this raises considerable collaboration, trust and privacy issues between different service providers. Our work is an initial step toward enabling devices themselves to represent, acquire and use a richer notion of context t...]]></description>
  <dc:date>2011-05-30</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/503/Learning-Co-reference-Relations-for-FOAF-Instances">
  <title><![CDATA[Learning Co-reference Relations for FOAF Instances]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/503/Learning-Co-reference-Relations-for-FOAF-Instances</link>
  <description><![CDATA[FOAF is widely used on the Web to describe people, groups and organizations and their properties. Since FOAF does not require unique IDs, it is often unclear when two FOAF instances are co-referent, i.e., denote the same entity in the world. We describe a prototype system that identifies sets of co-referent FOAF instances using logical constraints (e.g., IFPs), strong heuristics (e.g., FOAF agents described in the same file are not co-referent), and a Support Vector Machine (SVM) generated cl...]]></description>
  <dc:date>2010-11-09</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/506/Computing-FOAF-Co-reference-Relations-with-Rules-and-Machine-Learning">
  <title><![CDATA[Computing FOAF Co-reference Relations with Rules and Machine Learning]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/506/Computing-FOAF-Co-reference-Relations-with-Rules-and-Machine-Learning</link>
  <description><![CDATA[The friend of a friend (FOAF) vocabulary is widely used on the Web to describe ’agents’ (people, groups and organizations) and their properties. Since FOAF does not require unique ID for agents, it is not clear when two FOAF instances should be linked as co-referent, i.e., denote the entity in the world. One approach is to use logical constraints such as the presence of inverse functional properties as evidence that two individuals are the same. Another applies heuristics based on the str...]]></description>
  <dc:date>2010-11-01</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/477/A-Hybrid-Approach-to-Unsupervised-Relation-Discovery-Based-on-Linguistic-Analysis-and-Semantic-Typing">
  <title><![CDATA[A Hybrid Approach to Unsupervised Relation Discovery Based on Linguistic Analysis and Semantic Typing]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/477/A-Hybrid-Approach-to-Unsupervised-Relation-Discovery-Based-on-Linguistic-Analysis-and-Semantic-Typing</link>
  <description><![CDATA[This paper describes a hybrid approach for unsupervised and unrestricted relation discovery between entities using output from linguistic analysis and semantic typing information from a knowledge base. We use Factz (encoded as subject, predicate and object triples) produced by Powerset as a result of linguistic analysis. A particular relation may be expressed in a variety of ways in text and hence have multiple facts associated with it. We present an unsupervised approach for collapsing multi...]]></description>
  <dc:date>2010-06-06</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/475/Unsupervised-techniques-for-discovering-ontology-elements-from-Wikipedia-article-links">
  <title><![CDATA[Unsupervised techniques for discovering ontology elements from Wikipedia article links]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/475/Unsupervised-techniques-for-discovering-ontology-elements-from-Wikipedia-article-links</link>
  <description><![CDATA[We present an unsupervised and unrestricted approach to discovering an infobox like ontology by exploiting the inter-article links within Wikipedia. It discovers new slots and fillers that may not be available in the Wikipedia infoboxes. Our results demonstrate that there are certain types of properties that are evident in the link structure of resources like Wikipedia that can be predicted with high accuracy using little or no linguistic analysis.  The discovered properties can be further us...]]></description>
  <dc:date>2010-06-06</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/478/Automatic-Discovery-of-Semantic-Relations-using-MindNet">
  <title><![CDATA[Automatic Discovery of Semantic Relations using MindNet]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/478/Automatic-Discovery-of-Semantic-Relations-using-MindNet</link>
  <description><![CDATA[Information extraction deals with extracting entities (such as people,organizations or locations) and named relations between entities (such as "People born-in Country") from text documents. An important challenge in information extraction is the labeling of training data which is usually done manually and is therefore very laborious and in certain cases impractical. This paper introduces a new “model” to extract semantic relations fully automatically from text using the Encarta encyclope...]]></description>
  <dc:date>2010-05-19</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/471/A-Machine-Learning-Approach-to-Linking-FOAF-Instances">
  <title><![CDATA[A Machine Learning Approach to Linking FOAF Instances]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/471/A-Machine-Learning-Approach-to-Linking-FOAF-Instances</link>
  <description><![CDATA[The friend of a friend (FOAF) vocabulary is widely used on the Web to describe individual people and their properties.  Since FOAF does not require a unique ID for a person, it is not clear when two FOAF agents should be linked as coreferent, i.e., denote the same person in the world. One approach is to use the presence of inverse functional properties (e.g., foaf:mbox) as evidence that two individuals are the same. Another applies heuristics based on the string similarity of values of FOAF p...]]></description>
  <dc:date>2010-01-23</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/461/Ensembles-in-Adversarial-Classification-for-Spam">
  <title><![CDATA[Ensembles in Adversarial Classification for Spam]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/461/Ensembles-in-Adversarial-Classification-for-Spam</link>
  <description><![CDATA[The standard method for combating spam, either in email or on the web, is to train a classifier on manually labeled instances. As the spammers change their tactics, the performance of such classifiers tends to decrease over time. Gathering and labeling more data to periodically retrain the classifier is expensive. We present a method based on an ensemble of classifiers that can detect when its performance might be degrading and retrain itself, all without manual intervention.  Experiments wit...]]></description>
  <dc:date>2009-11-02</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/460/Improving-Binary-Classification-on-Text-Problems-using-Differential-Word-Features">
  <title><![CDATA[Improving Binary Classification on Text Problems using Differential Word Features]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/460/Improving-Binary-Classification-on-Text-Problems-using-Differential-Word-Features</link>
  <description><![CDATA[We describe an efficient technique to weigh word-based features in binary classification tasks and show that it significantly improves classification accuracy on a range of problems. The most common text classification approach uses a document's ngrams (words and short phrases) as its features and assigns feature values equal to their frequency or TFIDF score relative to the training corpus. Our approach uses values computed as the product of an ngram's document frequency and the difference o...]]></description>
  <dc:date>2009-11-02</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/448/Delta-TFIDF-An-Improved-Feature-Space-for-Sentiment-Analysis">
  <title><![CDATA[Delta TFIDF: An Improved Feature Space for Sentiment Analysis]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/448/Delta-TFIDF-An-Improved-Feature-Space-for-Sentiment-Analysis</link>
  <description><![CDATA[Mining opinions and sentiment from social networking sites is a popular application for social media systems. Common approaches use a machine learning system with a bag of words feature set. We present Delta TFIDF, an intuitive general purpose technique to efficiently weight word scores before classification. Delta TFIDF is easy to compute, implement, and understand. We use Support Vector Machines to show that Delta TFIDF significantly improves accuracy for sentiment analysis problems using t...]]></description>
  <dc:date>2009-05-17</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/377/Learning-the-Semantic-Meaning-of-a-Concept-from-the-Web">
  <title><![CDATA[Learning the Semantic Meaning of a Concept from the Web]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/377/Learning-the-Semantic-Meaning-of-a-Concept-from-the-Web</link>
  <description><![CDATA[Many researchers have used text classification method in solving the ontology mapping problem. Their mapping results heavily depend on the availability of quality exemplars used as training data. However, manual preparation of exemplars is costly. In this work, we propose to automatically extract text from web pages returned by a search engine. Search queries are formed according to the semantic information given in the ontology. We have implemented a prototype system that automates the entir...]]></description>
  <dc:date>2007-05-28</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/335/XPod-A-Human-Activity-Aware-Learning-Mobile-Music-Player">
  <title><![CDATA[XPod: A Human Activity Aware Learning Mobile Music Player]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/335/XPod-A-Human-Activity-Aware-Learning-Mobile-Music-Player</link>
  <description><![CDATA[The XPod system, presented in this paper, aims
to integrate awareness of human activity and musical
preferences to produce an adaptive system
that plays the contextually correct music. The
XPod project introduces a “smart” music player
that learns its user’s preferences and activity, and
tailors its music selections accordingly. We are using
a BodyMedia device that has been shown to accurately
measure a user’s physiological state. The
device is able to monitor a number of var...]]></description>
  <dc:date>2007-01-08</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/296/Detecting-Spam-Blogs-A-Machine-Learning-Approach">
  <title><![CDATA[Detecting Spam Blogs: A Machine Learning Approach]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/296/Detecting-Spam-Blogs-A-Machine-Learning-Approach</link>
  <description><![CDATA[Weblogs or blogs are an important new way to publish
information, engage in discussions, and form communities
on the Internet. The Blogosphere has unfortunately
been infected by several varieties of spam-like
content. Blog search engines, for example, are inundated
by posts from splogs – false blogs with machine
generated or hijacked content whose sole purpose is to
host ads or raise the PageRank of target sites. We discuss
how SVM models based on local and link-based
features can ...]]></description>
  <dc:date>2006-07-16</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/269/SVMs-for-the-Blogosphere-Blog-Identification-and-Splog-Detection">
  <title><![CDATA[SVMs for the Blogosphere: Blog Identification and Splog Detection]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/269/SVMs-for-the-Blogosphere-Blog-Identification-and-Splog-Detection</link>
  <description><![CDATA[Weblogs, or blogs have become an important new way to publish
information, engage in discussions and form communities. The
increasing popularity of blogs has given rise to search and analysis
engines focusing on the 'blogosphere'.  A key requirement of such
systems is to identify blogs as they crawl the Web.
While this ensures that only blogs are indexed, blog search engines
are also often overwhelmed by spam blogs (splogs). Splogs not only
incur computational overheads but also reduce...]]></description>
  <dc:date>2006-03-27</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/236/Modifying-Bayesian-Networks-by-Probability-Constraints">
  <title><![CDATA[Modifying Bayesian Networks by Probability Constraints]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/236/Modifying-Bayesian-Networks-by-Probability-Constraints</link>
  <description><![CDATA[This paper deals with the following problem:
modify a Bayesian network to satisfy a given set
of probability constraints by only changeing its
conditional probability tables while keeping the probability
distribution of the resulting network  as
close as possible to that of the original.
We solve this problem by extending
IPFP (iterative proportional fitting procedure) to
probability distributions represented by Bayesian
networks. The resulting algorithm, E-IPFP is further
developed...]]></description>
  <dc:date>2005-07-26</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/171/Mining-Domain-Specific-Texts-and-Glossaries-to-Evaluate-and-Enrich-Domain-Ontologies">
  <title><![CDATA[Mining Domain Specific Texts and Glossaries to Evaluate and Enrich Domain Ontologies]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/171/Mining-Domain-Specific-Texts-and-Glossaries-to-Evaluate-and-Enrich-Domain-Ontologies</link>
  <description><![CDATA[Ontologies have been widely accepted as the most advanced knowledge representation model. They are among the most important building blocks of semantic web, hence, very crucial for the success of semantic web. This paper discusses a fast and efficient method to facilitate the evaluation and enrichment of domain ontologies using a text-mining approach. We exploit domain specific texts and glossaries or dictionaries in order to automatically generate g-groups and f-groups. These groups are sets...]]></description>
  <dc:date>2004-06-21</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/234/Communicating-neural-network-knowledge-between-agents-in-a-simulated-aerial-reconnaissance-system">
  <title><![CDATA[Communicating neural network knowledge between agents in a simulated aerial reconnaissance system]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/234/Communicating-neural-network-knowledge-between-agents-in-a-simulated-aerial-reconnaissance-system</link>
  <description><![CDATA[In order to maintain their performance in a dynamic environment,
agents may be required to modify their learning
behavior during run-time. If an agent utilizes a rule-based
system for learning, new rules may be easily communicated
to the agent in order to modify the way in which it learns.
However, if an agent utilizes a connectionist-based system
for learning, the way in which the agent learns typically
remains static. This is due, in part, to a lack of research
in communicating subs...]]></description>
  <dc:date>1999-10-03</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/resource/html/id/333/Automatically-Generating-Linked-Data-from-Tables">
  <title><![CDATA[Automatically Generating Linked Data from Tables]]></title>
  <link>http://ebiquity.umbc.edu/resource/html/id/333/Automatically-Generating-Linked-Data-from-Tables</link>
  <description><![CDATA[Evidence for a table’s meaning can be found in its metadata but currently requires human interpretation. We describe techniques grounded in graphical models and probabilistic reasoning to infer meaning associated with a table. Using background knowledge from the Linked Open Data cloud, we automatically infer the semantics of column headers, table cell values (e.g., strings and numbers) and relations between columns and represent the inferred meaning as graph of RDF triples.]]></description>
  <dc:date>2011-11-15</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/140/Recognizing-Activities-using-RFID">
  <title><![CDATA[Recognizing Activities using RFID]]></title>
  <link>http://ebiquity.umbc.edu/resource/html/id/140/Recognizing-Activities-using-RFID</link>
  <description><![CDATA[Discovering the activites of daily life through a wearable RFID tag reader.]]></description>
  <dc:date>2005-09-27</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/resource/html/id/141/Recognizing-Activities-using-RFID">
  <title><![CDATA[Recognizing Activities using RFID]]></title>
  <link>http://ebiquity.umbc.edu/resource/html/id/141/Recognizing-Activities-using-RFID</link>
  <dc:date>2005-09-27</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/resource/html/id/208/XPod-IJCAI-Presentation">
  <title><![CDATA[XPod IJCAI Presentation]]></title>
  <link>http://ebiquity.umbc.edu/resource/html/id/208/XPod-IJCAI-Presentation</link>
  <dc:date>2006-10-25</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/403/Mid-Atlantic-Student-Colloquium-on-Speech-Language-and-Learning">
  <title><![CDATA[Mid-Atlantic Student Colloquium on Speech, Language and Learning]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/403/Mid-Atlantic-Student-Colloquium-on-Speech-Language-and-Learning</link>
  <description><![CDATA[The First Mid-Atlantic Student Colloquium on Speech, Language and Learning is a one-day event to be held at the Johns Hopkins University in Baltimore on Friday, 23 September 2011.  Its goal is to bring together students taking computational approaches to speech, language, and learning, so that they can introduce their research to the local student community, give and receive feedback, and engage each other in collaborative discussion.  Attendance is open to all and free but space is limited, ...]]></description>
  <dc:date>2011-09-23</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/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/352/Prediction-of-Oscar-Award-Nominations-Based-on-Movie-Scripts">
  <title><![CDATA[Prediction of Oscar Award Nominations Based on Movie Scripts]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/352/Prediction-of-Oscar-Award-Nominations-Based-on-Movie-Scripts</link>
  <description><![CDATA[According to The Numbers, the gross revenue for the Hollywood movie industry was over USD 10 billion in 2009. With annual revenues at this scale, it is critical for a movie to be successful at the box office. High revenue is closely linked to Oscar Award nominations and hence to winning of the award. Building predictive models for Oscar nominations can provide useful insights into predicting Oscar awards.  Using a movie script as a movie representation, we retrieve and weight individual movie...]]></description>
  <dc:date>2010-07-06</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/342/Cost-Sensitive-Information-Acquisition-for-Prediction-">
  <title><![CDATA[Cost-Sensitive Information Acquisition for Prediction]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/342/Cost-Sensitive-Information-Acquisition-for-Prediction-</link>
  <description><![CDATA[Machine learning systems have been increasingly used in our day-to-day
activities now. Just a few examples include handwritten character
recognition systems, product recommendation systems, face detection
features of cameras, speech recognition in hands-free devices, document
ranking by search engines, fraudulent activity detection for credit card
transactions, spam detection, and medical diagnosis. A critical
component of a machine learning system is the "information" needed to
develo...]]></description>
  <dc:date>2010-04-21</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/313/Dynamic-Domain-Adapting-Sentiment-Classifiers">
  <title><![CDATA[Dynamic Domain Adapting Sentiment Classifiers]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/313/Dynamic-Domain-Adapting-Sentiment-Classifiers</link>
  <description><![CDATA[Justin Martineau will give us a
preview of his dissertation proposal.

Sentiment analysis is the automatic detection and measurement of sentiment
in text segments by machines. However, sentiment is highly domain dependent.
This is particularly troubling given the scale and variety of topics seen on
the web. Providing sentiment search on the web requires more than the
standard machine learning approach. In this talk I describe a plan to
overcome domain dependence by breaking down docum...]]></description>
  <dc:date>2009-09-22</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/296/Adversarial-Classification-An-Ensemble-based-approach">
  <title><![CDATA[Adversarial Classification: An Ensemble-based approach]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/296/Adversarial-Classification-An-Ensemble-based-approach</link>
  <description><![CDATA[Master's Thesis Defense Announcement

Spam has been studied and dealt with extensively in the email, web, and, recently, blog domains. Recent work has addressed the problem of non-stationarity of the data using ensemble-based approaches. Adversarial classification has been handled by retraining base classifiers using labeled samples obtained from the ensemble. However, frequent retraining is expensive. There is a need is to dynamically determine when the classifiers should be retrained and ...]]></description>
  <dc:date>2009-04-27</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/292/Stochastic-and-Iterative-Techniques-for-Relational-Data-Clustering-">
  <title><![CDATA[Stochastic and Iterative Techniques for Relational Data Clustering]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/292/Stochastic-and-Iterative-Techniques-for-Relational-Data-Clustering-</link>
  <description><![CDATA[Dissertation Defense


This research focuses on the topic of relational data clustering,
which is the task of organizing objects into logical groups, or
clusters, taking into account the relational links between objects. As
a research area, relational clustering has received a great deal of
attention recently, because of the large variety of social media
applications and other modern relational data sources that have become
popular, such as weblogs, protein interaction networks, soci...]]></description>
  <dc:date>2009-04-13</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/237/Research-Challenges-In-Data-Mining">
  <title><![CDATA[Research Challenges In Data Mining]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/237/Research-Challenges-In-Data-Mining</link>
  <description><![CDATA[Research in data mining has led to advanced knowledge discovery
technologies and applications. In this talk, we will discuss some
emerging research issues for advanced technologies and
applications in data mining and discuss some recent progress in
this direction, including (1) exploration of the power of pattern
mining, (2) analysis of multidimensional, heterogeneous and
evolving information network, (3) mining of fast changing data
streams, (4) mining of moving object data, RFID data...]]></description>
  <dc:date>2008-04-22</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/215/Empowering-Scientific-Discovery-by-Distributed-Data-Mining-on-the-Grid-Infrastructure">
  <title><![CDATA[Empowering Scientific Discovery by Distributed Data Mining on the Grid Infrastructure]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/215/Empowering-Scientific-Discovery-by-Distributed-Data-Mining-on-the-Grid-Infrastructure</link>
  <description><![CDATA[The grid-based computing paradigm has attracted much attention in recent years. The sharing of distributed computing resources (such as software, hardware, data, sensors, etc) is an important aspect of grid computing. Computational Grids focus on methods for handling compute intensive tasks while Data Grids are geared toward data-intensive computing. Grid-based computing has been put to use in several scientific disciplines such as astronomy, engineering, climate studies, ecology, biology and...]]></description>
  <dc:date>2007-09-28</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/204/Transfer-in-the-Context-of-Reinforcement-Learning-by-Mapping-Q-Tables">
  <title><![CDATA[Transfer in the Context of Reinforcement Learning by Mapping Q-Tables]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/204/Transfer-in-the-Context-of-Reinforcement-Learning-by-Mapping-Q-Tables</link>
  <description><![CDATA[Transfer in machine learning is the process of using knowledge
learned in a source domain to speed learning in one or more related
target domains. In human learning, transfer is a ubiquitous
phenomenon. In machine learning, transfer is far less common. In
this thesis we present a transfer method for reinforcement learning
when the learner does not have access to a model of either the
source or the target domains (i.e., the transition and reward
probabilities are unknown) and there is n...]]></description>
  <dc:date>2007-05-02</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/205/Data-Clustering-with-a-Relational-Push-Pull-Model">
  <title><![CDATA[Data Clustering with a Relational Push-Pull Model]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/205/Data-Clustering-with-a-Relational-Push-Pull-Model</link>
  <description><![CDATA[Relational data clustering is the task of grouping data objects together when both features and relations between objects are present. I present a new generative model for relational data in which relations between objects can have either a binding or separating effect. For example, with a group of students separated into gender clusters, a "dating" relation would appear most frequently between the clusters, but a "roommate" relation would appear more often within clusters. In visualizing the...]]></description>
  <dc:date>2007-04-30</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/193/Knowledge-Transfer-using-Multiresolution-Learning">
  <title><![CDATA[Knowledge Transfer using Multiresolution Learning]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/193/Knowledge-Transfer-using-Multiresolution-Learning</link>
  <description><![CDATA[For my dissertation research, I propose to explore the transfer of knowledge at multiple levels of abstraction to improve learning. These multiple levels of abstraction will be created using multiresolution analysis, providing a principled and formal mechanism for abstracting knowledge.  I claim that by exploiting the similarities between objects at various levels of detail, learning at multiple resolutions can facilitate transfer between related tasks.

The use of multiple resolutions allo...]]></description>
  <dc:date>2007-03-07</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/188/Real-Time-Identification-of-Operating-Room-State-from-Video">
  <title><![CDATA[Real-Time Identification of Operating Room State from Video]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/188/Real-Time-Identification-of-Operating-Room-State-from-Video</link>
  <description><![CDATA[Managers of operating rooms (ORs) and of units upstream and downstream of
the OR (e.g., postanesthesia care) seek real-time information about OR
occupancy to make decisions about managing OR workflow and coordinating
resources.  Nursing and anesthesia staff typically record patient in/out
times by hand, and OR managers spend time walking about the OR suite to
estimate the time each case will finish.  This thesis describes a system
for using real-time video to automatically identify the ...]]></description>
  <dc:date>2006-11-20</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/171/Cost-Sensitive-Classifier-Evaluation-Using-Cost-Curves">
  <title><![CDATA[Cost-Sensitive Classifier Evaluation Using Cost Curves]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/171/Cost-Sensitive-Classifier-Evaluation-Using-Cost-Curves</link>
  <description><![CDATA[The evaluation of classifier performance in a cost-sensitive setting is 
straightforward if the operating conditions (misclassification costs and 
class distributions) are fixed and known. When this is not the case, 
evaluation requires a method of visualizing classifier performance 
across the full range of possible operating conditions. This talk argues 
that the classic technique for classifier performance visualization -- 
the ROC curve – is inadequate for the needs of researchers...]]></description>
  <dc:date>2006-09-28</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/166/Learning-the-Semantic-Meaning-of-a-Concept-from-the-Web">
  <title><![CDATA[Learning the Semantic Meaning of a Concept from the Web]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/166/Learning-the-Semantic-Meaning-of-a-Concept-from-the-Web</link>
  <description><![CDATA[Many researchers have applied text classification techniques to the ontology mapping problem. The mapping results in these researches heavily depend on the availability of highly relevant text exemplars associated with individual concepts. However, manual preparation of exemplars is costly. In this work, we propose to automatically collect text exemplars by downloading and processing web pages listed in the search results obtained by querying a search engine. Search queries are formed for eac...]]></description>
  <dc:date>2006-08-03</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/147/Using-Information-Extraction-to-Automatically-Generate-Probabilistic-Ontologies">
  <title><![CDATA[Using Information Extraction to Automatically Generate Probabilistic Ontologies]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/147/Using-Information-Extraction-to-Automatically-Generate-Probabilistic-Ontologies</link>
  <description><![CDATA[The Semantic Web is a rapidly developing research area that promises
to deliver Tim Berners-Lee's vision of a world where agents can
communicate, reason, and act to complete complex tasks for their
users.  Ontology languages have evolved as the de facto presentation
language for the Semantic Web.  Today there are over one million
Semantic Web Documents indexed in the Swoogle database collection.
This seemingly impressive number is dwarfed by the more than nine
billion pages in the Goog...]]></description>
  <dc:date>2006-04-25</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/124/Organizational-Learning-and-Network-Adaptation-in-Multi-Agent-Systems">
  <title><![CDATA[Organizational Learning and Network Adaptation in Multi-Agent Systems]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/124/Organizational-Learning-and-Network-Adaptation-in-Multi-Agent-Systems</link>
  <description><![CDATA[In both real and artificial societies, successful organizations are
highly dependent upon a structure that fosters effective and efficient
behavior at both the individual and the organizational levels. In
multi-agent systems, groups of agents must coordinate effectively in
order to solve problems, allocate tasks across a distributed
organization, collectively distribute knowledge and information, and
achieve collective goals.  The organizational structure of a
multi-agent system dictat...]]></description>
  <dc:date>2005-11-14</dc:date>
 </item>
</rdf:RDF>

