<?xml version="1.0"?>

<!DOCTYPE owl [
	<!ENTITY rdf "http://www.w3.org/1999/02/22-rdf-syntax-ns#">
	<!ENTITY rdfs "http://www.w3.org/2000/01/rdf-schema#">
	<!ENTITY xsd "http://www.w3.org/2001/XMLSchema#">
	<!ENTITY owl "http://www.w3.org/2002/07/owl#">
	<!ENTITY cc "http://web.resource.org/cc/#">
	<!ENTITY project "http://ebiquity.umbc.edu/ontology/project.owl#">
	<!ENTITY person "http://ebiquity.umbc.edu/ontology/person.owl#">
	<!ENTITY pub "http://ebiquity.umbc.edu/ontology/publication.owl#">
	<!ENTITY assert "http://ebiquity.umbc.edu/ontology/assertion.owl#">
]>

<!--

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.

-->

<rdf:RDF 
		xmlns:rdf = "&rdf;"
		xmlns:rdfs = "&rdfs;"
		xmlns:xsd = "&xsd;"
		xmlns:owl = "&owl;"
		xmlns:cc = "&cc;"
		xmlns:project = "&project;"
		xmlns:person = "&person;"
		xmlns:pub = "&pub;"
		xmlns:assert = "&assert;">
	<pub:InProceedings rdf:about="http://ebiquity.umbc.edu/paper/html/id/816/Cleaning-Noisy-Knowledge-Graphs">
		<rdfs:label><![CDATA[Cleaning Noisy Knowledge Graphs]]></rdfs:label>
		<pub:title><![CDATA[Cleaning Noisy Knowledge Graphs]]></pub:title>
		<pub:publishedOn rdf:datatype="&xsd;dateTime">2017-10-22T00:00:00-05:00</pub:publishedOn>
		<pub:abstract><![CDATA[My dissertation research is developing an approach to identify
and explain errors in a knowledge graph constructed by extracting
entities and relations from text. Information extraction systems can automatically
construct knowledge graphs from a large collection of documents,
which might be drawn from news articles, Web pages, social
media posts or discussion forums. The language understanding task is
challenging and current extraction systems introduce many kinds of errors.
Previous work on improving the quality of knowledge graphs uses
additional evidence from background knowledge bases or Web searches.
Such approaches are dicult to apply when emerging entities are present
and/or only one knowledge graph is available. In order to address the
problem I am using multiple complementary techniques including entitylinking,
common sense reasoning, and linguistic analysis.]]></pub:abstract>
		<pub:volume><![CDATA[1962]]></pub:volume>
		<pub:counter>1623</pub:counter>
		<pub:tag><![CDATA[knowledge graph]]></pub:tag>
		<pub:tag><![CDATA[learning]]></pub:tag>
		<pub:tag><![CDATA[natural language processing]]></pub:tag>
		<pub:booktitle><![CDATA[Proceedings of the Doctoral Consortium at the 16th International Semantic Web Conference]]></pub:booktitle>
		<pub:publisher><![CDATA[CEUR Workshop Proceedings]]></pub:publisher>
		<pub:author>
			<rdf:List>
				<rdf:first>
					<person:Person rdf:about="http://ebiquity.umbc.edu/person/html/Ankur/Padia"><person:name><![CDATA[Ankur Padia]]></person:name><rdfs:label><![CDATA[Ankur Padia]]></rdfs:label></person:Person>
				</rdf:first>
				<rdf:rest rdf:resource="&rdf;nil" />
			</rdf:List>
		</pub:author>
		<pub:firstAuthor>
<person:Person rdf:about="http://ebiquity.umbc.edu/person/html/Ankur/Padia"><person:name><![CDATA[Ankur Padia]]></person:name><rdfs:label><![CDATA[Ankur Padia]]></rdfs:label></person:Person>
		</pub:firstAuthor>
		<pub:relatedProject><project:PastProject rdf:about="http://ebiquity.umbc.edu/project/html/id/109/Accelerated-Cybersecurity"><project:title><![CDATA[Accelerated Cybersecurity]]></project:title><rdfs:label><![CDATA[Accelerated Cybersecurity]]></rdfs:label></project:PastProject></pub:relatedProject>
		<pub:softCopy><pub:SoftCopy>
			<pub:softCopyFormat><![CDATA[PDF Document]]></pub:softCopyFormat>
			<pub:softCopyURI><![CDATA[http://ebiquity.umbc.edu/get/a/publication/879.pdf]]></pub:softCopyURI>
			<pub:softCopySize>220013</pub:softCopySize>
			</pub:SoftCopy>
			</pub:softCopy>
	</pub:InProceedings>

<rdf:Description rdf:about="">
	<cc:License rdf:resource="http://creativecommons.org/licenses/by/2.0/" />
</rdf:Description>

</rdf:RDF>
