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Chapter ♥ Copyright 2008 Digital Enterprise Research Institute. All rights reserved.
Digital Enterprise Research Institute www.deri.ie
Semantic Search for Enterprise 2.0
Alexandre Passant1, Philippe Laublet2, John Breslin1, Stefan Decker1
1 Digital Enterprise Research Institute, NUI Galway 2 LaLIC, Université Paris-Sorbonne, France
SemSearch09, WWW09, Madrid
21th April 2009
Digital Enterprise Research Institute www.deri.ie
Enterprise 2.0
Social media in a corporate context “The use of emergent social software platforms within
companies, or between companies and their partners or customers”
The SLATES paradigm Search
Links
Authoring
Tagging
Extension
Signals
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Main issues
Enterprise 2.0 can be used to foster collaboration and social intelligence, but raises various issues
Information fragmentation Description of a project on a wiki, minutes of meetings on
blog posts, information about partners on RSS feeds, etc .
The gap between documents and data Valuable information in wikis, but hard to efficiently get it,
e.g “list all companies involved in project X since 2008”
Tagging issues Ambiguity, heterogeneity, lack of organization and gap of
tagging behaviors depending on expertise
Digital Enterprise Research Institute www.deri.ie
Proposed solution
Considering Enterprise 2.0 at the level of semantics The SemSLATES approach: middleware for Enterprise 2.0
Following the RDF bus approach Add-ons for existing applications
RDF(S)/OWL and SPARQL
User-interfaces and applications
on the top of it
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Use-case
EDF R&D: Blogs, wikis, RSS feeds Extensions for data integration, enabling semantic mash-
ups and semantic search
Common semantics for various applications SIOC and related vocabularies to model the social
interactions within the user communities
From documents to structured and interlinked data Lightweight ontologies (SKOS, FOAF extensions …)
Extending the Wiki platform to a Semantic Wiki system
Tagging issues Semantic tagging with MOAT, i.e. “Tag with URIs”
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Exposing SIOC data
Enable common semantics for user-generated content From various applications, completely automated
Inline macro
Simple autocomplete
field
Complex instance field
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From documents to RDF data
UfoWiki Wiki interface including forms mapped to ontologies for
collaborative instances management
Live SPARQL-autocompletion to reuse URIs between wikis
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Semantic tagging
MOAT - Meaning Of A Tag A lightweight model and framework to bridge the gap
between tagging and semantic indexing
Tags mapped to ontology instances created from the Wikis (via their label)
User-interface for validation / disambiguation (if needed)
Ability to link new tags to existing instances
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A complete interlinked graph
Wiki page 2Blog Post 1
hyperlink
a:EDF
f:Company
g:3017382/
g:Featurea:Energy
rdf:type rdf:typea:produces
g:locatedIn
:bpost_1
moat:topic
:wikipage_2sioc:links_to
:wiki_A
sioc:container_of
:alex
sioc:has_creator
creates contains
Wiki A
Sem
antic W
eb layer
Ente
rprise 2
.0 s
erv
ices
Social interactionswth SIOC
Semantic tagging with MOAT
Ontology populationwith semantic wikis
Digital Enterprise Research Institute www.deri.ie
Enabling search
How to integrate and query all this data ? 17000 instances of sioc:Post linked to 300 domain
ontology instances, on various applications
A ping-based architecture with a central RDF store Each component pings the store when creating /
updating / deleting RDF data
REST-ful interactions using SPARQL / SPARUL
Semantic Middleware
RDF Store
SPARU
L
inte
rface
SPARQ
Lin
terf
ace
Ping system to store
new or updated data
External services using
stored data
Digital Enterprise Research Institute www.deri.ie
Search interface
End-user interface Identifying relevant instance and retrieving information
from various sources, as well as related entities
Hiding RDF(S)/OWL and SPARQL to the users
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Enabling Semantic mash-ups
Re-using RDF data from the LOD cloud internally Low-cost Semantic mash-ups
E.g. Geolocation of wiki instances thanks to Geonames
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Conclusion
Enterprise 2.0 enables social interactions and ease content-generation But introduces new issues / emphasizes existing ones
Semantic integration can help Without having to rebuild the information system
Lightweight add-ons, transparency for end-users
Compared to existing information integration approaches Use lightweight semantics (FOAF, SIOC, SKOS …)
Consider the social aspect of Enterprise 2.0 both when creating and using RDF data
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Thank you !
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