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<title><string language="fre"><![CDATA[Resolving Entities in the Web of Data]]></string></title>
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<string language="fre"><![CDATA[Over the past decade, numerous knowledge bases (KBs) have been built 
to power a new generation of Web applications that provide 
entity-centric search and recommendation services. These KBs offer 
comprehensive, machine-readable descriptions of a large variety of 
real-world entities (e.g., persons, places, products, events) published 
on the Web as Linked Data (LD). Even when derived from the same data 
source (e.g., a Wikipedia entry), KBs such as DBpedia, YAGO2, or 
Freebase may provide multiple, non-identical descriptions for the same 
real-world entities. This is due to the different information extraction
tools and curation policies employed by KBs, resulting to complementary
and sometimes conflicting entity descriptions. Entity resolution (ER) 
aims to identify different descriptions that refer to the same 
real-world entity, and emerges as a central data-processing task for an 
entity-centric organization of Web data. ER is needed to enrich 
interlinking of data elements describing entities, even by 
third-parties, so that the Web of data can be accessed by machines as a 
global data space using standard languages, such as SPARQL. ER can also 
facilitate an automated KB construction by integrating entity 
descriptions from legacy
 
KBs with Web content published as HTML documents.
ER has attracted significant attention from many researchers in 
information systems, database and machine-learning communities. The 
objective of this lecture is to present the new ER challenges stemming 
from the Web openness in describing, by an unbounded number of KBs, a 
multitude of entity types across domains, as well as the high 
heterogeneity (semantic and structural) of descriptions, even for the 
same types of entities. The scale, diversity and graph structuring of 
entity descriptions published according to the LD paradigm challenge the
core ER tasks, namely, (i) how descriptions can be effectively compared
for similarity and (ii) how resolution algorithms can efficiently 
filter the candidate pairs of descriptions that need to be compared.
In a multi-type and large-scale entity resolution, we need to examine
whether two entity descriptions are somehow (or near) similar without 
resorting to domain- specific similarity functions and/or mapping rules.
Furthermore, the resolution of some entity descriptions might influence
the resolution of other neighbourhood descriptions. This setting 
clearly goes beyond deduplication (or record linkage) of collections of 
descriptions usually referring to a single entity type that slightly 
differ only in their attribute values. It essentially requires 
leveraging similarity of descriptions both on their content and 
structure. It also forces us to revisit traditional ER workfows 
consisting of separate indexing (for pruning the number of candidate 
pairs) and matching (for resolving entity descriptions) phases.
In this talk we intend to provide a starting point for researchers, 
students and developers who are interested in a global view of the ER 
problem in the Web of data.]]></string></description>
<keyword><string language="fre"><![CDATA[web de données]]></string></keyword><keyword><string language="fre"><![CDATA[résolution entitées]]></string></keyword>
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<date><dateTime>2015-11-05</dateTime></date>
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<entity><![CDATA[BEGIN:VCARD
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© Inria Paris - Rocquencourt]]></string>
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