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 <channel rdf:about="http://ebiquity.umbc.edu//tags/html/?t=semantic+similarity">
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  <title><![CDATA[UMBC ebiquity RSS Tag Search]]></title>
  <link><![CDATA[http://ebiquity.umbc.edu//tags/html/?t=semantic+similarity]]></link>
  <description><![CDATA[UMBC ebiquity RSS Tag Search for semantic similarity]]></description>
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      <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/393/PowerRelations-A-Question-Answering-System-for-DBPedia"/>
      <rdf:li resource="http://ebiquity.umbc.edu/project/html/id/95/Graph-of-Relations"/>
      <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/719/Robust-Semantic-Text-Similarity-Using-LSA-Machine-Learning-and-Linguistic-Resources"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/729/Event-Nugget-Detection-using-Thresholding-and-Classification-Techniques"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/693/Ebiquity-Paraphrase-and-Semantic-Similarity-in-Twitter-using-Skipgram"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/700/UMBC_Ebiquity-SFQ-Schema-Free-Querying-System"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/659/Meerkat-Mafia-Multilingual-and-Cross-Level-Semantic-Textual-Similarity-systems"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/658/Schema-Free-Querying-of-Semantic-Data"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/577/Improving-Word-Similarity-by-Augmenting-PMI-with-Estimates-of-Word-Polysemy"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/598/Schema-Free-Structured-Querying-of-DBpedia-Data"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/578/GoRelations-Towards-an-Intuitive-Query-System-for-RDF-Data"/>
      <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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 <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/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/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/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/719/Robust-Semantic-Text-Similarity-Using-LSA-Machine-Learning-and-Linguistic-Resources">
  <title><![CDATA[Robust Semantic Text Similarity Using LSA, Machine Learning and Linguistic Resources]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/719/Robust-Semantic-Text-Similarity-Using-LSA-Machine-Learning-and-Linguistic-Resources</link>
  <description><![CDATA[Semantic textual similarity is a measure of the degree of semantic equivalence between two pieces of text. We describe the SemSim system and its performance in the *SEM~2013~and SemEval-2014~tasks on semantic textual similarity. At the core of our system lies a robust distributional word similarity component that combines Latent Semantic Analysis and machine learning augmented with data from several linguistic resources. We used a simple term alignment algorithm to handle longer pieces of tex...]]></description>
  <dc:date>2016-03-01</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/729/Event-Nugget-Detection-using-Thresholding-and-Classification-Techniques">
  <title><![CDATA[Event Nugget Detection using Thresholding and Classification Techniques]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/729/Event-Nugget-Detection-using-Thresholding-and-Classification-Techniques</link>
  <description><![CDATA[This paper describes our Event Nugget Detection system that we submitted to the TAC KBP 2015. We considered the problem as document classification problem. We used confidence scores from classifier to detect the event by thresholding. Our feature vectors consist of event nugget context, part of speech tags of event nugget context and semantic similarity score between event nugget and event subtypes.  Our performance was low; we got F1 measure of 0.33 for both event nugget detection task and c...]]></description>
  <dc:date>2015-11-16</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/693/Ebiquity-Paraphrase-and-Semantic-Similarity-in-Twitter-using-Skipgram">
  <title><![CDATA[Ebiquity: Paraphrase and Semantic Similarity in Twitter using Skipgram]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/693/Ebiquity-Paraphrase-and-Semantic-Similarity-in-Twitter-using-Skipgram</link>
  <description><![CDATA[We describe the system we developed to participate in SemEval 2015 Task 1, Paraphrase and Semantic Similarity in Twitter. We create similarity vectors from two-skip trigrams of preprocessed tweets and measure their semantic similarity using our UMBC-STS system. We submitted two runs. The best result is ranked eleventh out of eighteen teams with F1 score of 0.599.]]></description>
  <dc:date>2015-06-04</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/700/UMBC_Ebiquity-SFQ-Schema-Free-Querying-System">
  <title><![CDATA[UMBC_Ebiquity-SFQ: Schema Free Querying System]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/700/UMBC_Ebiquity-SFQ-Schema-Free-Querying-System</link>
  <description><![CDATA[Users need better ways to explore large complex linked data resources.  Using SPARQL requires not only mastering its syntax and semantics but also understanding the RDF data model, the ontology and URIs for entities of interest. Natural language question answering systems solve the problem, but these are still subjects of research. The Schema agnostic SPARQL queries task defined in SAQ-2015 challenge consists of schema-agnostic queries following the syntax of the SPARQL standard, where the s...]]></description>
  <dc:date>2015-06-01</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/659/Meerkat-Mafia-Multilingual-and-Cross-Level-Semantic-Textual-Similarity-systems">
  <title><![CDATA[Meerkat Mafia: Multilingual and Cross-Level Semantic Textual Similarity systems]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/659/Meerkat-Mafia-Multilingual-and-Cross-Level-Semantic-Textual-Similarity-systems</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 TF-IDF score relative to the training corpus. Our approach uses values computed as the product of an ngram's document frequency and the difference ...]]></description>
  <dc:date>2014-08-23</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/658/Schema-Free-Querying-of-Semantic-Data">
  <title><![CDATA[Schema Free Querying of Semantic Data]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/658/Schema-Free-Querying-of-Semantic-Data</link>
  <description><![CDATA[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. Since such interfaces allow users to freely query data without understanding its schema, knowing how to refer to objects, or mastering the appropriate formal query language, we call them as schema-free query interfaces. Schema-free query interface systems address a fundamental problem in NLP, Database and AI - to bridge the user conceptual ...]]></description>
  <dc:date>2014-08-01</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/577/Improving-Word-Similarity-by-Augmenting-PMI-with-Estimates-of-Word-Polysemy">
  <title><![CDATA[Improving Word Similarity by Augmenting PMI with Estimates of Word Polysemy]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/577/Improving-Word-Similarity-by-Augmenting-PMI-with-Estimates-of-Word-Polysemy</link>
  <description><![CDATA[Pointwise mutual information (PMI) is a widely used word similarity measure, but it lacks a clear explanation of how it works. We explore how PMI differs from distributional similarity, and we introduce a novel metric, PMImax, that augments PMI with information about a word's number of senses. The coefficients of PMImax are determined empirically by maximizing a utility function based on the performance of automatic thesaurus generation. We show that it outperforms traditional PMI in the appl...]]></description>
  <dc:date>2013-06-01</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/598/Schema-Free-Structured-Querying-of-DBpedia-Data">
  <title><![CDATA[Schema-Free Structured Querying of DBpedia Data]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/598/Schema-Free-Structured-Querying-of-DBpedia-Data</link>
  <description><![CDATA[We need better ways to query large linked data collections such as DBpedia. Using the SPARQL query language requires not only mastering its syntax but also understanding the RDF data model, large ontology vocabularies and URIs for denoting entities.  Natural language interface systems address the problem, but are still subjects of research. We describe a compromise in which non-experts specify a graphical query ``skeleton'' and annotate it with freely chosen words, phrases and entity names. T...]]></description>
  <dc:date>2012-08-11</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/578/GoRelations-Towards-an-Intuitive-Query-System-for-RDF-Data">
  <title><![CDATA[GoRelations: Towards an Intuitive Query System for RDF Data]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/578/GoRelations-Towards-an-Intuitive-Query-System-for-RDF-Data</link>
  <description><![CDATA[Users need better ways to explore DBpedia 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 describe a compromise in which
non-experts specify a graphical “skeleton” for a query and annotate it with freely
chosen words,...]]></description>
  <dc:date>2012-02-01</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>
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