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	This ontology document is licensed under the Creative Commons
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 <channel rdf:about="http://ebiquity.umbc.edu//tags/html/?t=similarity">
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  <description><![CDATA[UMBC ebiquity RSS Tag Search for similarity]]></description>
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    <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/466/PhD-defense-Lushan-Han-Schema-Free-Querying-of-Semantic-Data"/>
      <rdf:li resource="http://ebiquity.umbc.edu/event/html/id/459/MS-defense-Social-Media-Analytics-Digital-Footprints"/>
      <rdf:li resource="http://ebiquity.umbc.edu/event/html/id/453/Social-Media-Analytics-Digital-Footprints"/>
      <rdf:li resource="http://ebiquity.umbc.edu/event/html/id/438/Masters-Thesis-Research-Update-Sandhya-Krishnan"/>
      <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/393/PowerRelations-A-Question-Answering-System-for-DBPedia"/>
      <rdf:li resource="http://ebiquity.umbc.edu/event/html/id/368/Text-Based-Similarity-Metrics-and-Deltas-for-Semantic-Web-Graphs"/>
      <rdf:li resource="http://ebiquity.umbc.edu/event/html/id/350/Text-Based-Similarity-Metrics-and-Delta-for-Semantic-Web-Graphs"/>
      <rdf:li resource="http://ebiquity.umbc.edu/event/html/id/344/A-New-Approach-for-Automatic-Thesaurus-Generation"/>
      <rdf:li resource="http://ebiquity.umbc.edu/project/html/id/95/Graph-of-Relations"/>
      <rdf:li resource="http://ebiquity.umbc.edu/project/html/id/29/UMBC-OntoMapper-A-Tool-For-Mapping-Between-Two-Ontologies"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/1069/ProKnow-Process-knowledge-for-safety-constrained-and-explainable-question-generation-for-mental-health-diagnostic-assistance"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/1009/Cybersecurity-Knowledge-Graph-Improvement-with-Graph-Neural-Networks"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/998/A-BERT-Based-Approach-to-Measure-Web-Services-Policies-Compliance-With-GDPR"/>
      <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/961/Measuring-Semantic-Similarity-across-EU-GDPR-Regulation-and-Cloud-Privacy-Policies"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/957/Fine-and-Ultra-Fine-Entity-Type-Embeddings-for-Question-Answering"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/877/Two-Tier-Analysis-of-Social-Media-Collaboration-for-Student-Migration"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/846/Knowledge-Graph-Fact-Prediction-via-Knowledge-Enriched-Tensor-Factorization"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/925/Energy-theft-detection-for-AMI-using-principal-component-analysis-based-reconstructed-data"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/782/Thinking-Fast-and-Slow-Combining-Vector-Spaces-and-Knowledge-Graphs"/>
      <rdf:li resource="http://ebiquity.umbc.edu/resource/html/id/21/Swoogle-presentation-April-04-"/>
      <rdf:li resource="http://ebiquity.umbc.edu/resource/html/id/300/Text-Based-Similarity-Metrics-and-Delta-for-Semantic-Web-Graphs"/>
      <rdf:li resource="http://ebiquity.umbc.edu/resource/html/id/351/UMBC-webbase-corpus"/>
      <rdf:li resource="http://ebiquity.umbc.edu/resource/html/id/348/Word-and-phrase-similarity"/>
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 </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/466/PhD-defense-Lushan-Han-Schema-Free-Querying-of-Semantic-Data">
  <title><![CDATA[PhD defense: Lushan Han, Schema Free Querying of Semantic Data]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/466/PhD-defense-Lushan-Han-Schema-Free-Querying-of-Semantic-Data</link>
  <description><![CDATA[Schema Free Querying of Semantic Data

Lushan Han

Developing interfaces to enable casual, non-expert users to query complex structured data has been the subject of much research over the past forty years. We refer to them as as schema-free query interfaces, since they allow users to freely query data without understanding its schema, knowing how to refer to objects, or mastering the appropriate formal query language. Schema-free query interfaces address fundamental problems in natural la...]]></description>
  <dc:date>2014-05-23</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/459/MS-defense-Social-Media-Analytics-Digital-Footprints">
  <title><![CDATA[MS defense: Social Media Analytics: Digital Footprints]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/459/MS-defense-Social-Media-Analytics-Digital-Footprints</link>
  <description><![CDATA[In this work we describe an approach to distinguish real and impostor/ compromised accounts on social media. Compromising a user's social media account is not only a breach of security, but can also lead to dissemination of misinformation at a fast pace on social media. There have been several such high profile attacks recently, including on Twitter feeds of AP, CBS, and Delta Airlines. A fake account for the Prime Minister's Office in India was used to spread malicious rumors last year. Our ...]]></description>
  <dc:date>2013-05-13</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/453/Social-Media-Analytics-Digital-Footprints">
  <title><![CDATA[Social Media Analytics: Digital Footprints]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/453/Social-Media-Analytics-Digital-Footprints</link>
  <description><![CDATA[Social media has greatly impacted the way we communicate today. With approximately 3000 tweets/sec and 55 million FB status updates a day, it is a great way to disseminate information to users across the world.  However such a tool can also be used to disseminate misinformation in a quick and efficient manner which can have a harmful impact in multiple scenarios like national security cases, or business/marketing cases and hence needs to be curbed and kept in check. Our approach involves crea...]]></description>
  <dc:date>2013-04-15</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/438/Masters-Thesis-Research-Update-Sandhya-Krishnan">
  <title><![CDATA[Masters Thesis Research Update: Sandhya Krishnan]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/438/Masters-Thesis-Research-Update-Sandhya-Krishnan</link>
  <description><![CDATA[In this week's lab meeting Sandhya Krishnan will present her proposed thesis topic:

Abstract: Content available on social media, can be used to understand the profile, behavior and interests of a user. Text mining and information retrieval tools can be used to extract words and topics which represent the content of the particular user in a given time frame. The goal if this thesis work is to analyze content of prominent user accounts on Social media [example: social media accounts of polit...]]></description>
  <dc:date>2012-10-08</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/393/PowerRelations-A-Question-Answering-System-for-DBPedia">
  <title><![CDATA[PowerRelations: A Question Answering System for DBPedia]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/393/PowerRelations-A-Question-Answering-System-for-DBPedia</link>
  <description><![CDATA[Large amounts of structured and semi-structured semantic data are available on the Web. A well-known example is DBpedia, which extracts data from Wikipedia, encodes it in the Semantic Web language RDF, and stores it in a triplestore. Although a formal query language, SPARQL, is available for accessing such data, it remains challenging for users to query the knowledge unless they are familiar with SPARQL and the particular ontologies used. We have developed an intuitive system for users to ex...]]></description>
  <dc:date>2011-04-26</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/368/Text-Based-Similarity-Metrics-and-Deltas-for-Semantic-Web-Graphs">
  <title><![CDATA[Text Based Similarity Metrics and Deltas for Semantic Web Graphs]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/368/Text-Based-Similarity-Metrics-and-Deltas-for-Semantic-Web-Graphs</link>
  <description><![CDATA[Recognizing that two Semantic Web documents or graphs are similar and characterizing their differences is useful in many tasks, including retrieval, updating, version control and knowledge base editing. I will describe several text-based similarity metrics that characterize the relation between Semantic Web graphs and evaluate these metrics for three specific cases of similarity: similarity in classes and properties, similarity disregarding differences in base-URIs, and versioning relationshi...]]></description>
  <dc:date>2010-10-05</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/350/Text-Based-Similarity-Metrics-and-Delta-for-Semantic-Web-Graphs">
  <title><![CDATA[Text Based Similarity Metrics and Delta for Semantic Web Graphs]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/350/Text-Based-Similarity-Metrics-and-Delta-for-Semantic-Web-Graphs</link>
  <description><![CDATA[Recognizing that two semantic web documents or graphs are similar, and
characterizing their differences is useful in many tasks, including
retrieval, updating, version control and knowledge base editing.  We
describe a number of text based similarity metrics that characterize
the relation between semantic web graphs and evaluate these metrics
for three specific cases of similarity that we have identified:
similarity in classes and properties used while differing only in
literal content...]]></description>
  <dc:date>2010-06-28</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/344/A-New-Approach-for-Automatic-Thesaurus-Generation">
  <title><![CDATA[A New Approach for Automatic Thesaurus Generation]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/344/A-New-Approach-for-Automatic-Thesaurus-Generation</link>
  <description><![CDATA[Distributional similarity has long been used to determine  how similar two words are and has been used in automatic thesaurus generation. Such distribution similarity measures, however, do not always work well for finding synonyms in a text corpus because synonyms may not necessarily have the most similar contexts. We have developed a novel alternative approach in automatic thesaurus generation using pointwise mutual information (PMI) and by exploiting co-occurrence patterns of synonyms, whic...]]></description>
  <dc:date>2010-05-04</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/project/html/id/95/Graph-of-Relations">
  <title><![CDATA[Graph of Relations]]></title>
  <link>http://ebiquity.umbc.edu/project/html/id/95/Graph-of-Relations</link>
  <description><![CDATA[ 
Users need better ways to explore linked open data collections and obtain information from it. Using SPARQL requires not only mastering its syntax and semantics but also understanding the RDF data model, the ontology used by the DBpedia, and URIs for entities of interest.  Natural language question answering systems solve the problem, but these are still subjects of research. We are developing a compromise approach in which non-experts specify a graphical ``skeleton'' for a query and anno...]]></description>
  <dc:date>2010-01-01</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/project/html/id/29/UMBC-OntoMapper-A-Tool-For-Mapping-Between-Two-Ontologies">
  <title><![CDATA[UMBC OntoMapper: A Tool For Mapping Between Two Ontologies]]></title>
  <link>http://ebiquity.umbc.edu/project/html/id/29/UMBC-OntoMapper-A-Tool-For-Mapping-Between-Two-Ontologies</link>
  <description><![CDATA[Forcing all communicating agents to share a common ontology is infeasible. A group of
people with similar interests usually has its own organizational schemes for documents. This
organization may be in the form of an ontology. Different agents may define very different
ontologies, and the semantics for the same terms may be very different in their ontologies. A
mapping from one agent's ontology to another agent's ontology is required to facilitate
communication between agents.
   Thi...]]></description>
  <dc:date>2001-09-01</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/1069/ProKnow-Process-knowledge-for-safety-constrained-and-explainable-question-generation-for-mental-health-diagnostic-assistance">
  <title><![CDATA[ProKnow: Process knowledge for safety constrained and explainable question generation for mental health diagnostic assistance]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/1069/ProKnow-Process-knowledge-for-safety-constrained-and-explainable-question-generation-for-mental-health-diagnostic-assistance</link>
  <description><![CDATA[Virtual Mental Health Assistants (VMHAs) are utilized in health care to provide patient services such as counseling and suggestive care. They are not used for patient diagnostic assistance because they cannot adhere to safety constraints and specialized clinical process knowledge (ProKnow) used to obtain clinical diagnoses. In this work, we define ProKnow as an ordered set of information that maps to evidence-based guidelines or categories of conceptual understanding to experts in a domain. W...]]></description>
  <dc:date>2023-01-09</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/1009/Cybersecurity-Knowledge-Graph-Improvement-with-Graph-Neural-Networks">
  <title><![CDATA[Cybersecurity Knowledge Graph Improvement with Graph Neural Networks]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/1009/Cybersecurity-Knowledge-Graph-Improvement-with-Graph-Neural-Networks</link>
  <description><![CDATA[Cybersecurity Knowledge Graphs (CKGs) help in
aggregating information about cyber-events. CKGs combined
with reasoning and querying systems such as SPARQL enable
security researchers to look up information about past cyberevents
that is helpful in understanding future cyber-events or
drawing similarity with a known cyber-event recorded in a
CKG. CKGs have assertions in the form of semantic triples. The
triples describe a relationship between a subject and object, both
of which are cyb...]]></description>
  <dc:date>2021-12-15</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/998/A-BERT-Based-Approach-to-Measure-Web-Services-Policies-Compliance-With-GDPR">
  <title><![CDATA[A BERT Based Approach to Measure Web Services Policies Compliance With GDPR]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/998/A-BERT-Based-Approach-to-Measure-Web-Services-Policies-Compliance-With-GDPR</link>
  <description><![CDATA[Data confidentiality is an issue of increasing importance. Several authorities and regulatory bodies are creating new laws that control how web services data is handled and shared. With the rapid increase of such regulations, web service providers face challenges in complying with these evolving regulations across jurisdictions. Providers must update their service policies regularly to address the new regulations.  The challenge is that regulatory documents are large text documents and requir...]]></description>
  <dc:date>2021-11-11</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/961/Measuring-Semantic-Similarity-across-EU-GDPR-Regulation-and-Cloud-Privacy-Policies">
  <title><![CDATA[Measuring Semantic Similarity across EU GDPR Regulation and Cloud Privacy Policies]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/961/Measuring-Semantic-Similarity-across-EU-GDPR-Regulation-and-Cloud-Privacy-Policies</link>
  <description><![CDATA[Data protection authorities formulate policies and rules which the service providers have to comply with to ensure security and privacy when they perform Big Data analytics using users Personally Identifiable Information (PII). The knowledge contained in the data regulations and organizational privacy policies are typically maintained as short unstructured text in HTML or PDF formats. Hence it is an open challenge to determine the specific regulation rules that are being addressed by a provid...]]></description>
  <dc:date>2020-12-13</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/957/Fine-and-Ultra-Fine-Entity-Type-Embeddings-for-Question-Answering">
  <title><![CDATA[Fine and Ultra-Fine Entity Type Embeddings  for Question Answering]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/957/Fine-and-Ultra-Fine-Entity-Type-Embeddings-for-Question-Answering</link>
  <description><![CDATA[We describe our system for the SeMantic AnsweR (SMART) Type prediction task 2020 for both the DBpedia and Wikidata Question Answer Type datasets. The SMART task challenge introduced fine-grained and ultra-fine entity typing to question answering by releasing two datasets for question classification using DBpedia and Wikidata classes. We propose a flexible framework for both entity types using paragraph vectors and word embeddings to obtain high-quality contextualized question representations....]]></description>
  <dc:date>2020-11-02</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/877/Two-Tier-Analysis-of-Social-Media-Collaboration-for-Student-Migration">
  <title><![CDATA[Two Tier Analysis of Social Media Collaboration for Student Migration]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/877/Two-Tier-Analysis-of-Social-Media-Collaboration-for-Student-Migration</link>
  <description><![CDATA[Global adoption of Social Media as the preferred medium for collaboration and information exchange is increasingly reshaping social realities and facilitating new research methodologies in various disciplines. Social Media applications are collecting a large amount of User-Generated Content (UGC) and web data that contains knowledge about novel approaches of global collaboration between people. We have done a detailed study of the factors that lead to student migration, as espoused by social ...]]></description>
  <dc:date>2019-12-14</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/846/Knowledge-Graph-Fact-Prediction-via-Knowledge-Enriched-Tensor-Factorization">
  <title><![CDATA[Knowledge Graph Fact Prediction via Knowledge-Enriched Tensor Factorization]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/846/Knowledge-Graph-Fact-Prediction-via-Knowledge-Enriched-Tensor-Factorization</link>
  <description><![CDATA[We present a family of novel methods for embedding knowledge graphs into real-valued tensors. These tensor-based embeddings capture the ordered relations that are typical in the knowledge graphs represented by semantic web languages like RDF. Unlike many previous models, our methods can easily use prior background knowledge provided by users or extracted automatically from existing knowledge graphs. In addition to providing more robust methods for knowledge graph embedding, we provide a prova...]]></description>
  <dc:date>2019-12-01</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/925/Energy-theft-detection-for-AMI-using-principal-component-analysis-based-reconstructed-data">
  <title><![CDATA[Energy theft detection for AMI using principal component analysis based reconstructed data]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/925/Energy-theft-detection-for-AMI-using-principal-component-analysis-based-reconstructed-data</link>
  <description><![CDATA[To detect energy theft attacks in advanced metering infrastructure (AMI), we propose a detection method based on principal component analysis (PCA) approximation. PCA approximation is introduced by dimensionality reduction of high dimensional AMI data and the authors extract the underlying consumption trends of a consumer that repeat on a daily or weekly basis. AMI data is reconstructed using principal components and used for computing relative entropy. In the proposed method, relative entrop...]]></description>
  <dc:date>2019-06-01</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/782/Thinking-Fast-and-Slow-Combining-Vector-Spaces-and-Knowledge-Graphs">
  <title><![CDATA[Thinking, Fast and Slow: Combining Vector Spaces and Knowledge Graphs]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/782/Thinking-Fast-and-Slow-Combining-Vector-Spaces-and-Knowledge-Graphs</link>
  <description><![CDATA[Knowledge graphs and vector space models are robust knowledge representation techniques with individual strengths and weaknesses. Vector space models excel at determining similarity between concepts, but are severely constrained when evaluating complex dependency relations and other logic-based operations that are a strength of knowledge graphs. We describe the VKG structure that helps unify knowledge graphs and vector representation of entities, and enables powerful inference methods and sea...]]></description>
  <dc:date>2017-08-30</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/resource/html/id/21/Swoogle-presentation-April-04-">
  <title><![CDATA[Swoogle presentation (April 04)]]></title>
  <link>http://ebiquity.umbc.edu/resource/html/id/21/Swoogle-presentation-April-04-</link>
  <description><![CDATA[Swoogle is a crawler-based indexing and retrieval system the semantic web -- i.e., RDF or OWL documents. The documents are indexed using SIRE, an ngram based information retrieval system which can return documents matching a user's query or compute the similarity among a set of documents. Swoogle is able to reason about the documents it discovers and compute useful metadata properties about them. We also describe an approach to computing the rank of a semantic web document based loosely on th...]]></description>
  <dc:date>2004-04-07</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/resource/html/id/300/Text-Based-Similarity-Metrics-and-Delta-for-Semantic-Web-Graphs">
  <title><![CDATA[Text Based Similarity Metrics and Delta for Semantic Web Graphs]]></title>
  <link>http://ebiquity.umbc.edu/resource/html/id/300/Text-Based-Similarity-Metrics-and-Delta-for-Semantic-Web-Graphs</link>
  <description><![CDATA[Recognizing that two semantic web documents or graphs are similar, and characterizing their differences is useful in many tasks, including retrieval, updating, version control and knowledge base editing. We describe a number of text based similarity metrics that characterize the relation between semantic web graphs and evaluate these metrics for three specific cases of similarity that we have identified: similarity in classes and properties used while differing only in literal content, differ...]]></description>
  <dc:date>1999-11-30</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/resource/html/id/351/UMBC-webbase-corpus">
  <title><![CDATA[UMBC webbase corpus]]></title>
  <link>http://ebiquity.umbc.edu/resource/html/id/351/UMBC-webbase-corpus</link>
  <description><![CDATA[The UMBC webBase corpus (http://ebiq.org/r/351) is a dataset containing a collection of English paragraphs with over  three billion words processed from the February 2007 crawl from the  Stanford WebBase project (http://bit.ly/WebBase).  Compressed, it is about 13GB in size.

It was derived from  the February 2007 crawl, which is one of the
largest collections and contains 100 million web
pages from more than 50,000 websites. The Stanford WebBase project did an excellent job in extrac...]]></description>
  <dc:date>2013-04-09</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/resource/html/id/348/Word-and-phrase-similarity">
  <title><![CDATA[Word and phrase similarity]]></title>
  <link>http://ebiquity.umbc.edu/resource/html/id/348/Word-and-phrase-similarity</link>
  <description><![CDATA[Computing semantic similarity between words/phrases has important applications in natural language processing, information retrieval, and artificial intelligence. There are two prevailing approaches to computing word similarity, based on either using of a thesaurus (e.g., WordNet ) or statistics from a large corpus. We provide a hybrid approach combining the two methods that is demonstrated on a web site through two services: one that returns a similarity score for two words or phrases and an...]]></description>
  <dc:date>2013-01-09</dc:date>
 </item>
</rdf:RDF>
