<rdf:RDF
 xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"
 xmlns="http://purl.org/rss/1.0/"
 xmlns:dc="http://purl.org/dc/elements/1.1/"
 xmlns:cc="http://web.resource.org/cc/"
 >
<!--
	This ontology document is licensed under the Creative Commons
	Attribution License. To view a copy of this license, visit
	http://creativecommons.org/licenses/by/2.0/ or send a letter to
	Creative Commons, 559 Nathan Abbott Way, Stanford, California
	94305, USA.
-->
 <channel rdf:about="http://ebiquity.umbc.edu//tags/html/?t=clustering">
  <cc:license rdf:resource="http://creativecommons.org/licenses/by/2.0/" />
  <title><![CDATA[UMBC ebiquity RSS Tag Search]]></title>
  <link><![CDATA[http://ebiquity.umbc.edu//tags/html/?t=clustering]]></link>
  <description><![CDATA[UMBC ebiquity RSS Tag Search for clustering]]></description>
  <items>
    <rdf:Seq>
      <rdf:li resource="http://ebiquity.umbc.edu/event/html/id/478/Topic-Modeling-for-RDF-Graphs"/>
      <rdf:li resource="http://ebiquity.umbc.edu/event/html/id/397/Community-Detection-in-Twitter"/>
      <rdf:li resource="http://ebiquity.umbc.edu/event/html/id/372/Social-media-analytics"/>
      <rdf:li resource="http://ebiquity.umbc.edu/event/html/id/355/Clustering-short-status-messages-a-topic-model-based-approach"/>
      <rdf:li resource="http://ebiquity.umbc.edu/event/html/id/323/A-Hybrid-Approach-to-Unsupervised-Relation-Discovery-via-Linguistic-Analysis-Entropy-based-Label-Ranking-and-Semantic-Typing"/>
      <rdf:li resource="http://ebiquity.umbc.edu/event/html/id/292/Stochastic-and-Iterative-Techniques-for-Relational-Data-Clustering"/>
      <rdf:li resource="http://ebiquity.umbc.edu/event/html/id/265/Mining-Social-Media-Communities-and-Content"/>
      <rdf:li resource="http://ebiquity.umbc.edu/event/html/id/256/Information-Extraction-via-Automatic-Generation-of-Semantic-Classifiers"/>
      <rdf:li resource="http://ebiquity.umbc.edu/event/html/id/249/Using-Automatic-Word-Sense-Discrimination-to-generate-a-Semantic-Lexicon"/>
      <rdf:li resource="http://ebiquity.umbc.edu/event/html/id/238/Probabilistic-Approximate-Algorithms-for-Distributed-Data-Mining-in-Peer-to-Peer-Networks"/>
      <rdf:li resource="http://ebiquity.umbc.edu/project/html/id/68/memeta"/>
      <rdf:li resource="http://ebiquity.umbc.edu/project/html/id/73/Mobile-Peer-to-Peer-Traffic-Monitoring"/>
      <rdf:li resource="http://ebiquity.umbc.edu/project/html/id/17/Web-Data-Mining-and-Personalization"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/1187/Evaluating-Causal-AI-Techniques-for-Health-Misinformation-Detection"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/1068/Neural-Bregman-Divergences-for-Distance-Learning"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/902/Affinity-Propagation-Initialisation-Based-Proximity-Clustering-For-Labeling-in-Natural-Language-Based-Big-Data-Systems"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/770/Modeling-the-Evolution-of-Climate-Change-Assessment-Research-Using-Dynamic-Topic-Models-and-Cross-Domain-Divergence-Maps"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/742/Clinico-genomic-Data-Analytics-for-Precision-Diagnosis-and-Disease-Management"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/743/Collaborative-data-mining-for-clinical-trial-analytics"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/714/Topic-Modeling-for-RDF-Graphs"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/717/Entity-Disambiguation-for-Wild-Big-Data-Using-Multi-Level-Clustering"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/697/Hot-Stuff-at-Cold-Start-HLTCOE-participation-at-TAC-2014"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/633/Online-unsupervised-coreference-resolution-for-semi-structured-heterogeneous-data"/>
      <rdf:li resource="http://ebiquity.umbc.edu/resource/html/id/222/Generative-Model-To-Construct-Blog-and-Post-Networks-In-Blogosphere-Masters-Thesis-Presentation-"/>
      <rdf:li resource="http://ebiquity.umbc.edu/resource/html/id/221/Generative-Model-To-Construct-Blog-and-Post-Networks-In-Blogosphere-Poster-"/>
      <rdf:li resource="http://ebiquity.umbc.edu/resource/html/id/374/Structural-Metadata-from-ArXiv-Articles"/>
    </rdf:Seq>
  </items>
 </channel>
 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/478/Topic-Modeling-for-RDF-Graphs">
  <title><![CDATA[Topic Modeling for RDF Graphs]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/478/Topic-Modeling-for-RDF-Graphs</link>
  <description><![CDATA[Topic models are widely used to thematically describe a collection of
text documents and have become an important technique for systems that
measure document similarity for classification, clustering,
segmentation, entity linking and more.  While they have been applied
to some non-text domains, their use for semi-structured graph data,
such as RDF, has been less explored.  We present a framework for
applying topic modeling to RDF graph data and describe how it can be
used in a number o...]]></description>
  <dc:date>2015-09-21</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/397/Community-Detection-in-Twitter">
  <title><![CDATA[Community Detection in Twitter]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/397/Community-Detection-in-Twitter</link>
  <description><![CDATA[Mohit Kewalramani will defend his MS thesis titled "Community Detection in Twitter".
 
Twitter has evolved into a source of social, political and real time information in addition to being a means of mass-communication and marketing. Monitoring and analyzing information on Twitter can lead to invaluable insights, which might otherwise be hard to get using conventional media resources. An important task in analyzing highly networked information sources like twitter is to identify communities...]]></description>
  <dc:date>2011-05-16</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/372/Social-media-analytics">
  <title><![CDATA[Social media analytics]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/372/Social-media-analytics</link>
  <description><![CDATA[This week's ebiquity meeting will focus on social media and two research efforts that are part of our Relief Social Media project.

Mohit Kewalramani will present the topic that he is addressing in his MS research.  An important task in analyzing highly networked information sources like Twitter is to identify communities that are formed. A community can be defined as a group of nodes that have more links within the set than outside it. We plan to present a technique for detecting communiti...]]></description>
  <dc:date>2010-10-12</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/event/html/id/323/A-Hybrid-Approach-to-Unsupervised-Relation-Discovery-via-Linguistic-Analysis-Entropy-based-Label-Ranking-and-Semantic-Typing">
  <title><![CDATA[A Hybrid Approach to Unsupervised Relation Discovery via Linguistic Analysis, Entropy-based Label Ranking and Semantic Typing]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/323/A-Hybrid-Approach-to-Unsupervised-Relation-Discovery-via-Linguistic-Analysis-Entropy-based-Label-Ranking-and-Semantic-Typing</link>
  <description><![CDATA[Zareen Syed will talk about "A Hybrid Approach to Unsupervised Relation Discovery via Linguistic Analysis, Entropy-based Label Ranking and Semantic Typing" 

ABSTRACT:
There are today two main approaches in Information Extraction systems to extract entities and relations between them from text: a knowledge engineering approach which requires grammars to be hand crafted to express the rules for the system, a quite laborious process; an automatic training approach which requires the hand ann...]]></description>
  <dc:date>2009-10-27</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/265/Mining-Social-Media-Communities-and-Content">
  <title><![CDATA[Mining Social Media Communities and Content]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/265/Mining-Social-Media-Communities-and-Content</link>
  <description><![CDATA[Ph.D. Dissertation Defense


Social Media is changing the way we find information, share knowledge and
communicate with each other. The important factor contributing to the growth
of these technologies is the ability to easily produce "user-generated
content". Blogs, Twitter, Wikipedia, Flickr and YouTube are just a few
examples of Web 2.0 tools that are drastically changing the Internet landscape
today. These platforms allow users to produce, annotate and share information
with thei...]]></description>
  <dc:date>2008-10-16</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/256/Information-Extraction-via-Automatic-Generation-of-Semantic-Classifiers">
  <title><![CDATA[Information Extraction via Automatic Generation of Semantic Classifiers]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/256/Information-Extraction-via-Automatic-Generation-of-Semantic-Classifiers</link>
  <description><![CDATA[Information extraction is an important unsolved problem of natural
language processing (NLP). It is the problem of 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 expensive.

This talk introduces a new "model" to generate training data with
le...]]></description>
  <dc:date>2008-09-16</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/249/Using-Automatic-Word-Sense-Discrimination-to-generate-a-Semantic-Lexicon">
  <title><![CDATA[Using Automatic Word Sense Discrimination to generate a  Semantic Lexicon]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/249/Using-Automatic-Word-Sense-Discrimination-to-generate-a-Semantic-Lexicon</link>
  <description><![CDATA[Automatic word sense discrimination is the process of distinguishing the 
number of unique senses of a target word in a given corpus. This work 
approaches word sense discrimination as an unsupervised clustering 
problem on the context of the target word in web documents.
Using the features from the computed clusters, the system constructs a 
new lexicon entry for the target word which includes the semantic and 
syntactic constraints for each discriminated sense. The lexicon entries 
a...]]></description>
  <dc:date>2008-07-07</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/238/Probabilistic-Approximate-Algorithms-for-Distributed-Data-Mining-in-Peer-to-Peer-Networks">
  <title><![CDATA[Probabilistic Approximate Algorithms for Distributed Data Mining in Peer-to-Peer Networks]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/238/Probabilistic-Approximate-Algorithms-for-Distributed-Data-Mining-in-Peer-to-Peer-Networks</link>
  <description><![CDATA[Peer-to-peer(P2P) computing is emerging as a new distributed computing 
paradigm for novel applications that involves exchange of information 
among  peers with little centralized coordination. Analyzing data 
distributed in P2P networks requires peer-to-peer data mining algorithms 
that can mine the data without data centralization. However, 
replicating  result of centralized data mining in an exact fashion is 
often communication-wise expensive. Approximate algorithms can be a 
real...]]></description>
  <dc:date>2008-04-28</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/73/Mobile-Peer-to-Peer-Traffic-Monitoring">
  <title><![CDATA[Mobile Peer-to-Peer Traffic Monitoring]]></title>
  <link>http://ebiquity.umbc.edu/project/html/id/73/Mobile-Peer-to-Peer-Traffic-Monitoring</link>
  <description><![CDATA[Automobile traffic is a major problem in developed societies.  We collectively waste huge amounts of time and resources traveling through traffic congestion.  Drivers choose the route that they believe will be the fastest; however traffic congestion can significantly change the length of a trip.  Drivers that know where clusters of slow traffic exist can choose other, more efficient routes.  We could save significant amount of wasted time if traffic congestion patterns could be effectively d...]]></description>
  <dc:date>2006-01-01</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/project/html/id/17/Web-Data-Mining-and-Personalization">
  <title><![CDATA[Web/Data Mining and Personalization]]></title>
  <link>http://ebiquity.umbc.edu/project/html/id/17/Web-Data-Mining-and-Personalization</link>
  <description><![CDATA[The evolution of the Internet into the Global Information Infrastructure, coupled with the immense
    popularity of the Web, has also enabled the ordinary citizen to become not just a consumer of information, but also its
    disseminator. The Web, then, is becoming the apocryphal Vox Populi. Given that there is this vast and ever growing
    amount of information, how does the average user quickly find what s/he is looking for -- a task in which the present
    day search engines don'...]]></description>
  <dc:date>1999-09-01</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/1068/Neural-Bregman-Divergences-for-Distance-Learning">
  <title><![CDATA[Neural Bregman Divergences for Distance Learning]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/1068/Neural-Bregman-Divergences-for-Distance-Learning</link>
  <description><![CDATA[Many metric learning tasks, such as triplet learning, nearest neighbor retrieval, and visualization, are treated primarily as embedding tasks where the ultimate metric is some variant of the Euclidean distance (e.g., cosine or Mahalanobis), and the algorithm must learn to embed points into the pre-chosen space. The study of non-Euclidean geometries is often not explored, which we believe is due to a lack of tools for learning non-Euclidean measures of distance. Recent work has shown that Breg...]]></description>
  <dc:date>2023-05-01</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/902/Affinity-Propagation-Initialisation-Based-Proximity-Clustering-For-Labeling-in-Natural-Language-Based-Big-Data-Systems">
  <title><![CDATA[Affinity Propagation Initialisation Based Proximity Clustering For Labeling in Natural Language Based Big Data Systems]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/902/Affinity-Propagation-Initialisation-Based-Proximity-Clustering-For-Labeling-in-Natural-Language-Based-Big-Data-Systems</link>
  <description><![CDATA[A key challenge for natural language based large text data is automatically extracting knowledge, in terms of entities and relations, embedded in it. State of the art relation extraction systems requires large amounts of labeled data, which is costly and very difficult, especially in industrial settings, due to time constraints of subject matter experts. Techniques like distant supervision require the availability of a related knowledge base, which is rarely possible. We have developed a nove...]]></description>
  <dc:date>2020-05-26</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/770/Modeling-the-Evolution-of-Climate-Change-Assessment-Research-Using-Dynamic-Topic-Models-and-Cross-Domain-Divergence-Maps">
  <title><![CDATA[Modeling the Evolution of Climate Change Assessment Research Using Dynamic Topic Models and Cross-Domain Divergence Maps]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/770/Modeling-the-Evolution-of-Climate-Change-Assessment-Research-Using-Dynamic-Topic-Models-and-Cross-Domain-Divergence-Maps</link>
  <description><![CDATA[Climate change is an important social issue and the subject of much research, both to understand the history of the Earth's changing climate and to foresee what changes to expect in the future. Approximately every five years, starting in 1990, the Intergovernmental Panel on Climate Change (IPCC) publishes a set of reports that cover the current state of climate change research, how this research will impact the world, risks, and approaches to mitigate the effects of climate change. Each repor...]]></description>
  <dc:date>2017-03-27</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/742/Clinico-genomic-Data-Analytics-for-Precision-Diagnosis-and-Disease-Management">
  <title><![CDATA[Clinico-genomic Data Analytics for Precision Diagnosis and Disease Management]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/742/Clinico-genomic-Data-Analytics-for-Precision-Diagnosis-and-Disease-Management</link>
  <description><![CDATA[Patient data can be present in clinical notes, lab results, genomic data sources, environmental and geospatial data sources and tissue banks to name a few. A holistic view of the patient's health can be achieved when relevant data from multiple heterogeneous sources are extracted and analyzed in a personalized manner. Moreover, comparative analysis of patients can be performed when multiple patient records are viewed across these heterogeneous data sources. To address this need, we propose cl...]]></description>
  <dc:date>2015-11-30</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/743/Collaborative-data-mining-for-clinical-trial-analytics">
  <title><![CDATA[Collaborative data mining for clinical trial analytics]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/743/Collaborative-data-mining-for-clinical-trial-analytics</link>
  <description><![CDATA[his paper proposes a collaborative data mining technique to provide multi-level analysis from clinical trials data. Clinical trials for clinical research and drug development generate large amount of data. Due to dispersed nature of clinical trial data, it remains a challenge to harness this data for analytics. In this paper, we propose a novel method using master data management (MDM) for analyzing clinical trial data, scattered across multiple databases, through collaborative data mining. O...]]></description>
  <dc:date>2015-11-30</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/714/Topic-Modeling-for-RDF-Graphs">
  <title><![CDATA[Topic Modeling for RDF Graphs]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/714/Topic-Modeling-for-RDF-Graphs</link>
  <description><![CDATA[Topic models are widely used to thematically describe a collection of text documents and have become an important technique for systems that measure document similarity for classification, clustering, segmentation, entity linking, and more.  While they have been applied to some non-text domains, their use for semi-structured graph data, such as RDF, has been less explored.  We present a framework for applying topic modeling to RDF graph data and describe how it can be used in a number of link...]]></description>
  <dc:date>2015-10-12</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/717/Entity-Disambiguation-for-Wild-Big-Data-Using-Multi-Level-Clustering">
  <title><![CDATA[Entity Disambiguation for Wild Big Data Using Multi-Level Clustering]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/717/Entity-Disambiguation-for-Wild-Big-Data-Using-Multi-Level-Clustering</link>
  <description><![CDATA[When RDF instances represent the same entity they are said
to corefer. For example, two nodes from different RDF graphs 1 both refer
to same individual, musical artist James Brown. Disambiguating entities
is essential for knowledge base population and other tasks that result
in integration or linking of data. Often however, entity instance data
originates from different sources and can be represented using differ-
ent schemas or ontologies. In the age of Big Data, data can have other
c...]]></description>
  <dc:date>2015-10-12</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/697/Hot-Stuff-at-Cold-Start-HLTCOE-participation-at-TAC-2014">
  <title><![CDATA[Hot Stuff at Cold Start: HLTCOE participation at TAC 2014]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/697/Hot-Stuff-at-Cold-Start-HLTCOE-participation-at-TAC-2014</link>
  <description><![CDATA[The JHU HLTCOE participated in the Cold Start task in this year’s Text Analysis Conference Knowledge Base Population evaluation.  This is our third year of participation in the task, and we continued our research with the KELVIN system. We submitted experimental variants that explore use of forward-chaining inference, slightly more aggressive entity clustering, refined multiple within-document conference, and prioritization of relations extracted from news sources.]]></description>
  <dc:date>2014-10-31</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/633/Online-unsupervised-coreference-resolution-for-semi-structured-heterogeneous-data">
  <title><![CDATA[Online unsupervised coreference resolution for semi-structured heterogeneous data]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/633/Online-unsupervised-coreference-resolution-for-semi-structured-heterogeneous-data</link>
  <description><![CDATA[A pair of RDF instances are said to corefer when they are intended to denote the same
thing in the world, for example, when two nodes of type foaf:Person describe the same
individual. This problem is central to integrating and inter-linking semi-structured
datasets. We are developing an online, unsupervised coreference resolution framework
for heterogeneous, semi-structured data. The online aspect requires us to process
new instances as they appear and not as a batch. The instances are h...]]></description>
  <dc:date>2012-11-30</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/resource/html/id/222/Generative-Model-To-Construct-Blog-and-Post-Networks-In-Blogosphere-Masters-Thesis-Presentation-">
  <title><![CDATA[Generative Model To Construct Blog and Post Networks In Blogosphere (Masters Thesis Presentation)]]></title>
  <link>http://ebiquity.umbc.edu/resource/html/id/222/Generative-Model-To-Construct-Blog-and-Post-Networks-In-Blogosphere-Masters-Thesis-Presentation-</link>
  <description><![CDATA[Web graphs have been very useful in the structural and statistical analysis of the web.
Various models have been proposed to simulate web graphs that generate degree distributions similar to the web. Real world blog networks resemble many properties of web graphs. But the dynamic nature of the blogosphere and the link structure evolving due to
blog readership and social interactions is not well expressed by the existing models.

In this research we propose a model for a blogger to constru...]]></description>
  <dc:date>2007-05-01</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/resource/html/id/221/Generative-Model-To-Construct-Blog-and-Post-Networks-In-Blogosphere-Poster-">
  <title><![CDATA[Generative Model To Construct Blog and Post Networks In Blogosphere (Poster)]]></title>
  <link>http://ebiquity.umbc.edu/resource/html/id/221/Generative-Model-To-Construct-Blog-and-Post-Networks-In-Blogosphere-Poster-</link>
  <description><![CDATA[Web graphs have been very useful in the structural and statistical analysis of the web.
Various models have been proposed to simulate web graphs that generate degree distributions
similar to the web. Real world blog networks resemble many properties of web
graphs. But the dynamic nature of the blogosphere and the link structure evolving due to
blog readership and social interactions is not well expressed by the existing models.
In this research we propose a model for a blogger to constru...]]></description>
  <dc:date>2007-05-01</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>
