LOD2 Webinar Series Classification and Quality Analysis with DL Learner and ORE

Creating Knowledge out of Interlinked Data

LOD2 Webinar . 29.11.2011 . Page 1

http://lod2.eu


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.


http://lod2.eu
http://lod2.eu


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.


http://lod2.eu
http://lod2.eu


About me
Lorenz Bühmann
•
•
•

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
•
•
•

Founded in 2006
30+ Researchers
3 Sub groups

•

Goals
–
–
–

•

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


http://lod2.eu


Agile Knowledge Engineering and Semantic Web Research Group

EU Funded Projects:
– Linked Open Data 2 (LOD2)
– GeoKnow
– BIG
– BioASQ
– LinkingLOD
– Open Data Portal (ODP)

•

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
– ...


http://lod2.eu


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


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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“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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“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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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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Paper

Accepted

Rejected

Paper

AcceptedPaper

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

http://lod2.eu


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)
}
}


http://lod2.eu


Knowledge Base Repair by Validation of Constraints in ORE


http://lod2.eu


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


http://lod2.eu


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/


http://lod2.eu


Contact Details

Lorenz Bühmann
Address Work: Augustusplatz 10, Room P618, 04109 Leipzig

Email: buehmann@informatik.uni-leipzig.de
Skype ID: lorenz.buehmann.uni


http://lod2.eu


Credits
Jingle
Coordination

R.E.M., Martin Kaltenböck, Florian Kondert
Thomas Thurner
Martin Kaltenböck
Moderation
Martin Kaltenböck
Presented by Lorenz Bühmann


http://lod2.eu


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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http://lod2.eu



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