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

LOD2 Webinar . 29.11.2011 . Page 1 http://lod2.eu

Creating Knowledge out of Interlinked Data



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

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.



• 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

About me Lorenz Bühmann



• 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

Agile Knowledge Engineering and Semantic Web Research Group



EU Funded Projects:

– Linked Open Data 2 (LOD2)

– GeoKnow

– BIG

– BioASQ

– LinkingLOD

– Open Data Portal (ODP)

• 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, E-

Learning and E-Tourism

– ...

Agile Knowledge Engineering and Semantic Web Research Group

Further descriptions about

projects can be found here:

http://aksw.org/Projects





Project Page: http://aksw.org/Projects/ORE

Source Code: https://github.com/AKSW/ORE

Demo Page: http://ore.aksw.org/

http://aksw.org/Projects/CubeViz



https://github.com/AKSW/cubeviz.ontowiki



http://odp.cubeviz.aksw.org/






Agenda

Introduction and Motivation

Linked Data life cycle and role of ORE within LOD2

Knowledge Base Enrichment

Knowledge Base Repair

Demo

Future Work



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 co-

evolution of schemata and data, in particular for large knowledge

bases

• ontology modeling can be difficult and introduce unintended errors



Linked Data Life Cycle

Classification Enrichment

Quality Analysis

Evolution Repair

Search

Browsing

Exploration

Extraction

Storage Querying

Manual revision

authoring

Interlinking Fusing



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




“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.




“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%)




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.



Knowledge Base Schema Enrichment in ORE



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”



Knowledge Base Repair of Logical Errors

OWL based on Description Logics

Common errors:

• unsatisfiable classes cannot have any individuals

• inconsistent ontology 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



Knowledge Base Repair of Logical Errors in ORE



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







Paper

Accepted Rejected

Paper

AcceptedPaper RejectedPaper



Knowledge Base Repair of Naming Problems in ORE



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



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) } }



Knowledge Base Repair by Validation of Constraints in ORE



Demonstration http://ore.aksw.org



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



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/



https://github.com/AKSW/ORE

http://dl-learner.org/



http://patomat.vse.cz/



Lorenz Bühmann

Address Work: Augustusplatz 10, Room P618, 04109 Leipzig

Email: [email protected]

Skype ID: lorenz.buehmann.uni

Contact Details

mailto:[email protected]





Credits

Jingle R.E.M., Martin Kaltenböck, Florian Kondert

Coordination 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 ...



http://lod2.eu

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