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 <channel rdf:about="http://ebiquity.umbc.edu//tags/html/?t=linked+open+data">
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  <title><![CDATA[UMBC ebiquity RSS Tag Search]]></title>
  <link><![CDATA[http://ebiquity.umbc.edu//tags/html/?t=linked+open+data]]></link>
  <description><![CDATA[UMBC ebiquity RSS Tag Search for linked open data]]></description>
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    <rdf:Seq>
      <rdf:li resource="http://ebiquity.umbc.edu/event/html/id/417/Semantic-Web-Meetup"/>
      <rdf:li resource="http://ebiquity.umbc.edu/event/html/id/400/Generating-Linked-Data-by-inferring-the-semantics-of-tables"/>
      <rdf:li resource="http://ebiquity.umbc.edu/event/html/id/351/T2LD-An-automatic-framework-for-extracting-interpreting-and-representing-tables-as-linked-data"/>
      <rdf:li resource="http://ebiquity.umbc.edu/project/html/id/95/Graph-of-Relations"/>
      <rdf:li resource="http://ebiquity.umbc.edu/project/html/id/96/Tables-to-Linked-Data"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/722/UCO-A-Unified-Cybersecurity-Ontology"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/720/Supporting-Situationally-Aware-Cybersecurity-Systems"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/701/Querying-RDF-Data-with-Text-Annotated-Graphs"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/628/Semantic-Message-Passing-for-Generating-Linked-Data-from-Tables"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/596/A-Domain-Independent-Framework-for-Extracting-Linked-Semantic-Data-from-Tables"/>
      <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/551/DC-Proposal-Graphical-Models-and-Probabilistic-Reasoning-for-Generating-Linked-Data-from-Tables"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/547/Generating-Linked-Data-by-Inferring-the-Semantics-of-Tables"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/521/Integrating-Linked-Open-Data-with-Unstructured-Text-for-Intelligence-Gathering-Tasks"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/480/T2LD-An-automatic-framework-for-extracting-interpreting-and-representing-tables-as-Linked-Data"/>
      <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/316/Generating-Linked-Data-by-inferring-the-semantics-of-tables"/>
      <rdf:li resource="http://ebiquity.umbc.edu/resource/html/id/321/Generating-Linked-Data-by-inferring-the-semantics-of-tables"/>
      <rdf:li resource="http://ebiquity.umbc.edu/resource/html/id/331/GoRelations-an-Intuitive-Query-System-for-DBPedia-and-LOD-"/>
      <rdf:li resource="http://ebiquity.umbc.edu/resource/html/id/339/Making-the-Semantic-Web-Easier-to-Use"/>
      <rdf:li resource="http://ebiquity.umbc.edu/resource/html/id/327/situational-awareness-for-cybersecurity"/>
      <rdf:li resource="http://ebiquity.umbc.edu/resource/html/id/348/Word-and-phrase-similarity"/>
    </rdf:Seq>
  </items>
 </channel>
 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/417/Semantic-Web-Meetup">
  <title><![CDATA[Semantic Web Meetup]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/417/Semantic-Web-Meetup</link>
  <description><![CDATA[The UMBC Ebiquity Lab is hosting the November meeting of the Lotico Central Maryland Semantic Web Meetup from 6:00-8:00 pm in room 456 of the ITE building (directions).  All are welcome.  If you want to attend, please join the  Central MD Semantic Web Meetup group and RSVP.  The meeting will start with a pizza social from 6:00pm to 6:45pm and then continue with a series of short presentations of current Semantic Web research being done in our lab.

  Tim Finin: introduction and overview

...]]></description>
  <dc:date>2011-11-15</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/400/Generating-Linked-Data-by-inferring-the-semantics-of-tables">
  <title><![CDATA[Generating Linked Data by inferring the semantics of tables]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/400/Generating-Linked-Data-by-inferring-the-semantics-of-tables</link>
  <description><![CDATA[Ph.D. Preliminary Examination
A vast amount of information is encoded in tables on the web, spreadsheets and databases. Considerable work has been focused on exploiting unstructured free text; however techniques that are effective for documents and free text do not work well with tables. In this research we present techniques to generate high quality linked data from tables by jointly inferring the semantics of column headers, table cell values (e.g., strings and numbers), relations between ...]]></description>
  <dc:date>2011-05-25</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/351/T2LD-An-automatic-framework-for-extracting-interpreting-and-representing-tables-as-linked-data">
  <title><![CDATA[T2LD – An automatic framework for extracting,            interpreting and representing tables as linked data]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/351/T2LD-An-automatic-framework-for-extracting-interpreting-and-representing-tables-as-linked-data</link>
  <description><![CDATA[MS Thesis Defense


We present an automatic framework for extracting, interpreting and
generating linked data from tables. In the process of representing
tables as linked data, we assign every column header a class label
from an appropriate ontology, link table cells (if appropriate) to an
entity from the Linked Open Data cloud and identify relations between
various columns in the table, which helps us to build an overall
interpretation of the table. Using the limited evidence provid...]]></description>
  <dc:date>2010-06-29</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/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/paper/html/id/722/UCO-A-Unified-Cybersecurity-Ontology">
  <title><![CDATA[UCO: A Unified Cybersecurity Ontology]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/722/UCO-A-Unified-Cybersecurity-Ontology</link>
  <description><![CDATA[In this paper, we describe the Unified Cybersecurity Ontology (UCO) that is intended to support information integration and cyber situational awareness in cybersecurity systems. The ontology integrates heterogeneous data and knowledge schemas from different cybersecurity systems, as well as the most commonly used cybersecurity standards for information sharing and exchange. The UCO ontology has also been mapped to a number of existing cybersecurity ontologies as well as concepts in the Linked...]]></description>
  <dc:date>2016-02-12</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/720/Supporting-Situationally-Aware-Cybersecurity-Systems">
  <title><![CDATA[Supporting Situationally Aware Cybersecurity Systems]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/720/Supporting-Situationally-Aware-Cybersecurity-Systems</link>
  <description><![CDATA[In this report, we describe the Unified Cyber Security ontology (UCO) to support situational awareness in cyber security systems. The ontology is an effort to incorporate
and integrate heterogeneous information available from different cyber security systems and most commonly used cyber security standards for information sharing and exchange. The ontology has also been mapped to a number of existing cyber security
ontologies as well as concepts in the Linked Open Data cloud. Similar to DBpe...]]></description>
  <dc:date>2015-09-30</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/701/Querying-RDF-Data-with-Text-Annotated-Graphs">
  <title><![CDATA[Querying RDF Data with Text Annotated  Graphs]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/701/Querying-RDF-Data-with-Text-Annotated-Graphs</link>
  <description><![CDATA[Scientists and casual users need better ways to query RDF databases or Linked Open Data. Using the SPARQL query language requires not only mastering its syntax and semantics but also understanding the RDF data model, the ontology used, and URIs for entities of interest. Natural language query systems are a powerful approach, but current techniques are brittle in addressing the ambiguity and complexity of natural language and require expensive labor to supply the extensive domain knowledge the...]]></description>
  <dc:date>2015-06-29</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/628/Semantic-Message-Passing-for-Generating-Linked-Data-from-Tables">
  <title><![CDATA[Semantic Message Passing for Generating Linked Data from Tables]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/628/Semantic-Message-Passing-for-Generating-Linked-Data-from-Tables</link>
  <description><![CDATA[We describe work on automatically inferring the intended meaning of tables and representing it as RDF linked data, making it available for improving search, interoperability and integration.  We present implementation details of a joint inference module that uses knowledge from the linked open data (LOD) cloud to jointly infer the semantics of column headers, table cell values (e.g., strings and numbers) and relations between columns. The framework generates linked data by mapping column head...]]></description>
  <dc:date>2013-10-21</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/596/A-Domain-Independent-Framework-for-Extracting-Linked-Semantic-Data-from-Tables">
  <title><![CDATA[A Domain Independent Framework for Extracting Linked Semantic Data from Tables]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/596/A-Domain-Independent-Framework-for-Extracting-Linked-Semantic-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 require human input to create schemas and often result in graphs that do not follow best practices for linked data.  Evidence for a tab...]]></description>
  <dc:date>2012-07-05</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/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/547/Generating-Linked-Data-by-Inferring-the-Semantics-of-Tables">
  <title><![CDATA[Generating Linked Data by Inferring the Semantics of Tables]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/547/Generating-Linked-Data-by-Inferring-the-Semantics-of-Tables</link>
  <description><![CDATA[Vast amounts of information is encoded in structured tables found in
documents, on the Web, and in spreadsheets or databases. Integrating
or searching over this information benefits from understanding its
intended meaning.  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 requires general and
domain-specific background knowledge.  We represent a table's meaning
by mapping columns to...]]></description>
  <dc:date>2011-09-03</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/521/Integrating-Linked-Open-Data-with-Unstructured-Text-for-Intelligence-Gathering-Tasks">
  <title><![CDATA[Integrating Linked Open Data with Unstructured Text for Intelligence Gathering Tasks]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/521/Integrating-Linked-Open-Data-with-Unstructured-Text-for-Intelligence-Gathering-Tasks</link>
  <description><![CDATA[We present techniques for uncovering links between terror
incidents, organizations, and people involved with these incidents.
Our methods involve performing shallow NLP tasks
to extract entities of interest from documents and using linguistic
pattern matching and filtering techniques to assign
specific relations to the entities discovered. We also gather
more information about these entities from the Linked Open
Data Cloud, and further allow human analysts to add intelligent
inference...]]></description>
  <dc:date>2011-03-28</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/480/T2LD-An-automatic-framework-for-extracting-interpreting-and-representing-tables-as-Linked-Data">
  <title><![CDATA[T2LD - An automatic framework for extracting, interpreting and representing tables as Linked Data]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/480/T2LD-An-automatic-framework-for-extracting-interpreting-and-representing-tables-as-Linked-Data</link>
  <description><![CDATA[We present an automatic framework for extracting, interpreting and generating linked data from tables. In the process of representing tables as linked data, we assign every column header a class label from an appropriate ontology, link table cells (if appropriate) to an entity from the Linked Open Data cloud and identify relations between various columns in the table, which helps us to build an overall interpretation of the table. Using the limited evidence provided by a table in the form of ...]]></description>
  <dc:date>2010-08-02</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/316/Generating-Linked-Data-by-inferring-the-semantics-of-tables">
  <title><![CDATA[Generating Linked Data by inferring the semantics of tables]]></title>
  <link>http://ebiquity.umbc.edu/resource/html/id/316/Generating-Linked-Data-by-inferring-the-semantics-of-tables</link>
  <description><![CDATA[A vast amount of information is encoded in tables on the web, spreadsheets and databases. Considerable work has been focused on exploiting unstructured free text; however techniques that are effective for documents and free text do not work well with tables. Early work in table interpretation in the field of document analysis and later on the Web, focused mainly on understanding and extracting tables from scanned documents and html web pages. Relatively little work has addressed the understan...]]></description>
  <dc:date>2011-05-06</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/resource/html/id/321/Generating-Linked-Data-by-inferring-the-semantics-of-tables">
  <title><![CDATA[Generating Linked Data by inferring the semantics of tables]]></title>
  <link>http://ebiquity.umbc.edu/resource/html/id/321/Generating-Linked-Data-by-inferring-the-semantics-of-tables</link>
  <description><![CDATA[Vast amounts of information is encoded in structured tables found in documents, on the Web, and in spreadsheets or databases. Integrating or searching over this information benefits from understanding its intended meaning. 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 requires general and domain-specific background knowledge. We represent a table's meaning by mapping columns to classes ...]]></description>
  <dc:date>2011-09-02</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/resource/html/id/331/GoRelations-an-Intuitive-Query-System-for-DBPedia-and-LOD-">
  <title><![CDATA[GoRelations: an Intuitive Query System for DBPedia (and LOD)]]></title>
  <link>http://ebiquity.umbc.edu/resource/html/id/331/GoRelations-an-Intuitive-Query-System-for-DBPedia-and-LOD-</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 annotat...]]></description>
  <dc:date>2011-11-15</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/resource/html/id/339/Making-the-Semantic-Web-Easier-to-Use">
  <title><![CDATA[Making the Semantic Web Easier to Use]]></title>
  <link>http://ebiquity.umbc.edu/resource/html/id/339/Making-the-Semantic-Web-Easier-to-Use</link>
  <description><![CDATA[Semantic Web technologies have the potential to support many activities by providing a Web-based data representation that ties data to semantics models, facilitates data sharing and linking, supports provenance annotations, and can exploit a large and growing collection of background knowledge on the Web. While the concepts and technologies are mature and supported by sound standards, their use within most application communities remains relatively low. This talk will discuss current research...]]></description>
  <dc:date>2012-04-01</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/resource/html/id/327/situational-awareness-for-cybersecurity">
  <title><![CDATA[situational awareness for cybersecurity]]></title>
  <link>http://ebiquity.umbc.edu/resource/html/id/327/situational-awareness-for-cybersecurity</link>
  <description><![CDATA[We describe a current project    aimed at developing a situational awareness framework to (1) detect potential new vulnerabilities from Web descriptions and discussions, extract information and map to IDS knowledge base, (2) recognize potential attacks and intrusions in data from low level intrusion detection systems and map to IDS knowledge base, and (3) integrate and reason over results of (1) and (2) to identify actual attacks.]]></description>
  <dc:date>2011-10-21</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>
