Creating Knowledge from Interlinked Data

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


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


LIMES
- Link Discovery Framework for Metric Spaces -



Overview

• LIMES in LOD2
• Main Ideas
• Technical Details
• Using LIMES
– The „Geeky“ Approach
– LIMES Interface
– Assisted Linking


Inter-
linking/
Fusing

Linked Data Lifecycle
Manual Classifi-
revision/ cation/
authoring Enrichment

Storage/
Linked Data Quality
Querying
Lifecycle Analysis

Evolution /
Extraction Repair

Search/
Browsing/
Exploration


LOD2 Services LOD2 Stack UI components
Sindice Sig.ma Browse &
LOD2 Stack Semantic
Structure

Authoring
GovData.eu
eGovernment LOD2 Stack APIs
Portal and components Interlinking API

Exalead Enterprise Search DXX LIMES SILK
Web
Search Enrichment and Repair API
Multi-Domain
Ontology
DL-Learner ORE

LOD2 STACK API Knowledge Base Fusion API
Create
Wolters Kluwer Deutschland Structure SemMF WIQA
LOD2 applied to Media and Publishing Link
Fuse
Knowledge Store API
LOD Cloud hosted on OpenLink's Virtuoso
Query and Browsing capability Virtuoso + MonetDB

LOD Cloud: Access interfaces:
Knowledge Storage Layer

Linked Data SPARQL DUMPS Triplify, D2R

Central LOD2 Services Distributed/Local LOD2 Components


Link Discovery

• Characteristics
– Very large data sets
– Complex data sets
• Problems
– Runtime
– Complex Specifications
• Solutions
– Time-efficient computation
– Assistance during configuration
– Machine learning for creating link specifications


LIMES

• Declarative Link Discovery Framework
• Tuned towards efficiency and extensibility
• Set-theoretical grammar for specifying links
• Time-efficient mappers for single data types
• Machine learning for detecting link specs



Architecture

Machine Learning



Workflow



Workflow

Hybrid
approach

Time-efficient
Rich grammar mappers



LIMES Link Specifications

1. Metadata
2. Source and Target
3. Similarity Measure
4. Acceptance Conditions
5. Review Conditions
6. Execution Mode
7. Output Format




1. Metadata
6. Execution Mode
7. Output Format




• Preprocessing functions
– Strings, numerical values
– Data converters
• Similarity Measures Trigram
– String
– Numerical values
lowerCase

label label




• Operators
– Measure operators
– Spec operators
MAX

Trigram Trigram

label label label name




• Operators
– Measure operators
OR
– Spec operators

Filter Filter

Trigram Trigram

label label label name




1. Metadata
6. Execution Mode
7. Output Format



Geeky approach: XML

• Task: Link drugs
and ingredients
– Source: Dailymed
– Target: Drugbank
– Features
• Definition of source, target, measures
• Using property chains for linking
• Using preprocessing



LIMES Native Interface

• Task: Link drugs
across knowledge bases
– Source: DBpedia
– Target: Drugbank
– Features
• Definition of complex measures



LIMES Native Interface

• OR(trigram(x.rdfs:label, y.drugbank:genericName)|0.8,
trigram(x.rdfs:label, y.rdfs:label)|0.8)
OR

Filter Filter

Trigram Trigram

label label label genericName


COLANUT

• Task: Link diseases
across knowledge bases
– Source: Diseasome
– Target: Sider
– Features
• Assisted linking
• Definition of complex measures



Further Information

• Technical Details
– Requirements: Java 1.6
– License: http://creativecommons.org/licenses/
by-nc-sa/3.0/
• Technical papers
– Axel-Cyrille Ngonga Ngomo: A Time-Efficient Hybrid Approach to Link
Discovery. In: Proceedings of the sixth international workshop on
Ontology Matching, 2011
– Axel-Cyrille Ngonga Ngomo und Klaus Lyko: EAGLE: Efficient Active
Learning of Link Specifications using Genetic Programming. In:
Proceedings of ESWC 2012
– Axel-Cyrille Ngonga Ngomo, Jens Lehmann, Sören Auer und Konrad
Höffner: RAVEN -- Active Learning of Link Specifications. In: Proceedings
of OM@ISWC



Credits

Jingle Axel Ngonga
Coordination Thomas Thurner
Martin Kaltenböck
Moderation Martin Kaltenböck
Presented by Axel Ngonga
http://bis.uni-leipzig.de/AxelNgonga
ngonga@informatik.uni-leipzig.de



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

24.04.2012 – D2R (University of Leipzig)

http://lod2.eu

Creating Knowledge from Interlinked Data

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