In this Webinar Lorenz Bühmann presents the ontology repair and enrichment tool ORE and also the DL-Learner , a machine learning tool to solve supervised learnings tasks and support knowledge engineers in constructing knowledge. Those two beneighbored tools in the LOD2 Stack are for classification and the following quality analysis of Linked Data.
LOD2 Webinar Series Classification and Quality Analysis with DL Learner and ORE
1. Creating Knowledge out of Interlinked Data
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2. Creating Knowledge out of Interlinked Data
LOD2 is a large-scale integrating project co-funded by the European
Commission within the FP7 Information and Communication Technologies
Work Programme. This 4-year project comprises leading Linked Open
Data technology researchers, companies, and service providers. Coming
from across 12 countries the partners are coordinated by the Agile
Knowledge Engineering and Semantic Web Research Group at the
University of Leipzig, Germany.
LOD2 will integrate and syndicate Linked Data with existing large-scale
applications. The project shows the benefits in the scenarios of Media and
Publishing, Corporate Data intranets and eGovernment.
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3. Creating Knowledge out of Interlinked Data
Once per month the LOD2 webinar series offer a free webinar about
tools and services along the Linked Open Data Life Cycle.
Stay with us and learn more about acquisition, editing, composing,
connected applications – and finally publishing Linked Open Data.
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About me
Lorenz Bühmann
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Member of Agile Knowledge Engineering and Semantic Web research group
(AKSW) since 2009
PhD Student at the University of Leipzig since 2011
Research Interests:
– Machine Learning in Semantic Web
– Ontology Debugging
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Agile Knowledge Engineering and Semantic Web Research Group
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Founded in 2006
30+ Researchers
3 Sub groups
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Goals
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Contributing to the advancement of science in Semantic Web, Knowledge Engineering,
Software Engineering
Cost efficient, high-impact R&D, which proves usefulness at an early stage
Bridge the gap between research results and applications
Committed to Open Source, Open Access, and Open Knowledge movements
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Agile Knowledge Engineering and Semantic Web Research Group
EU Funded Projects:
– Linked Open Data 2 (LOD2)
– GeoKnow
– BIG
– BioASQ
– LinkingLOD
– Open Data Portal (ODP)
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Further descriptions about
projects can be found here:
http://aksw.org/Projects
Past EU funded Projects
– LOD Around the Clock (LATC)
– Semantic Content Management Systems for Enterprise Knowledge
Management and News Mining (SCMS)
– OntoWiki - Semantic Collaboration for Knowledge Management, ELearning and E-Tourism
– ...
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Project Page: http://aksw.org/Projects/ORE
Source Code: https://github.com/AKSW/ORE
Demo Page: http://ore.aksw.org/
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Agenda
Introduction and Motivation
Linked Data life cycle and role of ORE within LOD2
Knowledge Base Enrichment
Knowledge Base Repair
Demo
Future Work
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Introduction
• the quantity and size of RDF knowledge bases has significantly
increased
• many of those knowledge bases lack sophisticated schemata and
instance data adhering to those schemata
• for content extracted from legacy sources, crowdsourced content, but
also manually curated content, it is challenging to ensure a coevolution of schemata and data, in particular for large knowledge
bases
• ontology modeling can be difficult and introduce unintended errors
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Linked Data Life Cycle
Manual
revision
authoring
Interlinking
Fusing
Quality
Analysis
Storage
Querying
Extraction
Search
Browsing
Exploration
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Classification
Enrichment
Evolution
Repair
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Knowledge Base Schema Enrichment
Goal:
Learn schema axioms in knowledge bases based on the instance data.
Apply methods of Inductive Logic Programming(ILP):
Positive examples + Negative examples + Background knowledge
Hypothesis
ILP in Semantic Web:
OWL Axiom
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Knowledge Base Schema Enrichment
“How to describe a class MusicalArtist?”
Positive Examples:
Eric Clapton a Person; instrument Guitar; birthDate 1945-03-30.
Elton John a Person; instrument Piano; birthDate 1947-03-25.
Whitney Houston a Actor; instrument Vocals; birthDate 1963-08-09.
Background Knowledge:
Actor SubClassOf Person.
Learned Axiom:
MusicalArtist EquivalentTo Person and
birthDate some xsd:date and
instrument some Instrument
We can derive from
Tina Turner a Person; instrument Vocals; birthDate 1939-11-26.
Tina Turner a MusicalArtist.
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Knowledge Base Schema Enrichment
“How to describe a property birthDate?”
Positive Examples:
Eric Clapton
birthDate 1945-03-30.
Elton John birthDate 1947-03-25.
Whitney Houston birthDate 1963-08-09.
Paracelsus birthDate 1493-11-11, 1493-12-17.
Learned Axiom:
Functional(birthDate) (3 out of 4 = 75%)
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Knowledge Base Schema Enrichment
Why schema enrichment?
1. The axioms serve as documentation for the purpose and correct
usage of schema elements.
2. They improve the application debugging techniques.
3. Additional implicit information can be inferred.
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Knowledge Base Schema Enrichment in ORE
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Knowledge Base Repair
Goal:
Support in detection and repair of modelling errors and problems.
ORE in its current version supports the detection and repair of
• logical errors
• naming problems by integrating the PatOMat framework
• “missing data”
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Knowledge Base Repair of Logical Errors
OWL based on Description Logics
Common errors:
• unsatisfiable classes
• inconsistent ontology
cannot have any individuals
everything can be derived
Both cases are quite easy for a reasoner to detect and for a tool to
display when working on OWL ontologies.
“Why is the class unsatisfiable?”
“Why is the knowledge base inconsistent?”
Explanations
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Knowledge Base Repair of Logical Errors in ORE
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Knowledge Base Repair of Naming Problems
• analysis of the naming of entities across ontological structures
can reveal
• naming issues
• underlying conceptualization issues
• natural language structure influences the structure of formal
knowledge bases and vice versa
• content expressed in formal representation languages, such as
the semantic web ones, should be accessible not only to logical
reasoning machines but also to humans and NLP procedures,
and thus resemble the natural language as much as possible
Naming in ontologies matters
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Knowledge Base Repair of Naming Problems
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Knowledge Base Repair of Naming Problems
Paper
Accepted
Rejected
Paper
AcceptedPaper
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RejectedPaper
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Knowledge Base Repair of Naming Problems in ORE
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Knowledge Base Repair by Validation of Constraints
Constraints = OWL Axioms
Example:
Person SubClassOf birthDate some xsd:date
“Every person has a birth date”
OWL comes with the Open World Assumption(OWA), thus missing
information is no error.
We assume a Closed World Assumption, such that we can reuse
OWL axioms as constraints.
Basic Idea: Rewrite OWL axiom into SPARQL query
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Knowledge Base Repair by Validation of Constraints
Person SubClassOf birthDate some xsd:date
SELECT ?s WHERE {
?s a :Person.
FILTER NOT EXISTS { ?s :birthDate ?o.
FILTER(DATATYPE(?o)=xsd:date)
}
}
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Knowledge Base Repair by Validation of Constraints in ORE
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Demonstration
http://ore.aksw.org
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Future Work
Enrichment
batch mode
Logical repair
detection of unsatisfiable classes on SPARQL knowledge bases
Constraint Validation
add repair option
SPIN support
(better) explanation why resources violate a constraint + support
to add missing information
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Further Information
License: Apache License Version 2.0
Project page: http://aksw.org/Projects/ORE
Source code: https://github.com/AKSW/ORE
DL-Learner: http://dl-learner.org/
PatOMat: http://patomat.vse.cz/
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Credits
Jingle
Coordination
R.E.M., Martin Kaltenböck, Florian Kondert
Thomas Thurner
Martin Kaltenböck
Moderation
Martin Kaltenböck
Presented by Lorenz Bühmann
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Hope you enjoyed staying with us – if you need more detailed
information, visit us at www.lod2.eu and let us know how we can
improve to meet your expectations!
Don’t forget to register for our next webinar
20.12. 2011 - Virtuoso (Open Link Software)
24.01. 2012 - OntoWiki (University of Leipzig, Germany)
Have a great day and don’t forget ...
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