Presentation at JIST 2012 -- I forgot to add a link to http://en.wikipedia.org/wiki/Knowledge_extraction I mentioned it during the presentation, because some of their output would be compatible with SPARQL

1. Creating Knowledge out of Interlinked Data
JIST 2012 – Page 1 http://lod2.eu
Improving the Performance of the
DL-Learner SPARQL Component for
Semantic Web Applications
Didier Cherix, Sebastian Hellmann, Jens Lehmann
http://slideshare.net/kurzum
http://dl-learner.org
http://lod2.eu
AKSW, Universität Leipzig
LOD2 Presentation . 02.09.2010 . Page http://lod2.eu

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Motivation: 2007 - 2012
DL-Learner was developed in parallel to DBpedia at University Leipzig since 2007
DL-Learner is a tool for learning concepts in Description Logics (DLs) from user-
provided examples.
Worked very well for small to medium sized data sets, e.g. Carcinogenesis an other
ML problems from the UCI ML repository
Limit is the capacity of current OWL-DL reasoners
Challenge was (and is) to do reasoning-based, supervized Machine Learning on
the DBpedia Dataset (> 200 Mio triples) or larger datasets

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Introduction DL-Learner

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Introduction DL-Learner
Very large search space
Reasoner instance checks

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Introduction DL-Learner

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Introduction DL-Learner
DL-Learner heavily relies on instance checks for machine learning, so the OWL
Reasoner is the bottle neck
Underlying idea:
Only select relevant data for the Machine Learning Problem based on user-given
examples
→ Reduces the amount of triples that have to be given to a reasoner
→ Reduces complexity and size of the OWL schema
Brute-force approach:
Load all data into the OWL Reasoner, then do instance checks
→ infeasible for Dbpedia
Iterative approach (old component):
Iterate over all instances and fetch the data recursively
→ inefficient even with caching

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Introduction DL-Learner

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Introduction DL-Learner

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Introduction DL-Learner

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Introduction DL-Learner

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Introduction DL-Learner

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Introduction DL-Learner
Challenge:
What is the most efficient way to retrieve such a fragment?

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Improvements of the New Component
• Step 1: Indexing the T-Box:
• Download the OWL Schema and index it in memory
• either via SPARQL or OWL file

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Improvements of the New Component
• Step 2: A-Box Queries
Parameter recursion depth:
Retrieve newly discovered bindings to ?o until a certain depth is reached.

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Improvements of the New Component
• Step 3: Typing the retrieved instances

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Improvements of the New Component
• Step 4: T-Box Index:
All “relevant” T-Box information is added via the index to the fragment.
For each class already in the fragment. all superclasses and their
equivalentClass axioms are added

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Benchmarking - Speed
For each class in DBpedia Ontology:
- 30 instances as positives
- 30 negatives from a sister class

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Benchmarking – F-Measure on the training data
70% of the results for each class
had an F-measure of 90-100% on the training data

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SPARQL Retrieval Component Impact
• DL-Learner – http://dl-learner.org
• DBpedia Navigator
• Tiger Corpus Navigator
• AutoSPARQL - http://autosparql.dl-learner.org/
• HANNE – http://hanne.aksw.org
• ORE - http://aksw.org/Projects/ORE
Sebastian Hellmann, Jens Lehmann und Sören Auer:
Learning of OWL Class Descriptions on Very Large Knowledge Bases
In: International Journal on Semantic Web and Information Systems, 2009
Web Applications
Active Learning → User Interaction and Feedback

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Future Work
• Research Paper in Session 4b (tomorrow at 15:10)
Navigation-induced Knowledge Engineering by Example
• Caching + more sophisticated options
• Large scale learning problems
http://slideshare.net/kurzum
Homepage: http://dl-learner.org
Source code:
http://sourceforge.net/projects/dl-learner/

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Example
Sebastian Hellmann, Jens Lehmann, Jörg Unbehauen, Claus Stadler, Thanh Nghia Lam und Markus
Strohmaier: Navigation-induced Knowledge Engineering by Example
In: JIST 2012

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Example
Sebastian Hellmann, Jens Lehmann und Sören Auer:
Learning of OWL Class Descriptions on Very Large Knowledge Bases
In: International Journal on Semantic Web and Information Systems, 2009