Navigation-induced Knowledge Engineering by Example
1. Creating Knowledge out of Interlinked Data
JIST 2012 – Page 1 http://lod2.eu
Navigation-induced Knowledge Engineering
by Example (NKE)
Sebastian Hellmann, Jens Lehmann, Jörg Unbehauen, Claus Stadler,
Thanh Nghia Lam, Markus Strohmaier
http://slideshare.net/kurzum
http://aksw.org/Projects/NKE
http://lod2.eu
AKSW, Universität Leipzig
LOD2 Presentation . 02.09.2010 . Page http://lod2.eu
2. JIST 2012 – Page 2 http://lod2.eu
Problem description
Why is there a Knowledge Acquisition Bottleneck?
Questions you might ask an Ontology Engineer:
• What is the purpose of my Ontology?
• For which application is it created?
• What are sensible categories?
• How do I design the concept hierarchy to be useful for browsing?
• How do I use my resources efficiently, yet still produce a reasonable
good result?
• With how many Domain experts do I have to communicate to reach
consensus?
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How many Ontology Engineers are
necessary to structure 31 Billion Facts?
Who will guard the guards?
Does their schema fit my use case?
What kind of schemas do we need to effectively query and
browse this data?
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NKE
Navigation-induced Knowledge Engineering by Example
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NKE Methodology
Based on the idea that each information need of a user might be a
potential ontological concept (set of instances)
Search <=> Ontological Concept
There are three steps involved:
I. Navigation: NKE starts by interpreting navigational behavior of users to
infer an initial (seed) set of positive and negative examples.
II. Iterative Feedback: NKE supports users in interactively refining the seed
set of examples such that the final set of objects satisfies the users’
intent
III.Retention: NKE allows users to retain previously explored sets of objects
by grouping them and saving them for later retrieval.
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Introduction DL-Learner
Good properties for active learning:
- Biased towards high recall
- Scales well: Number of training examples is
more important than the size of the
background knowledge
Didier Cherix, Sebastian Hellmann und Jens Lehmann:
Improving the Performance of a SPARQL Component for Semantic Web Applications
In: JIST 2012
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GUIs
With only 2 positives and 4 negatives,
it is possible to find 13 more instances, which are
football clubs situated close to Saxony, Germany
Possible to add more positives and complete the
list
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Vision
Integrate NKE processes seamlessly into existing applications
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GUIs
dbo:President and dbo:geoRelated value United_States
and dbo:spouse some Thing
Retrieves 42 of 44 instances → acceptable intensional definition
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Geizhals
Softer criteria: Retention / “Saving” is replaced by a hit count on the concept, which is a
navigation suggestion (popularity)
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Evaluation
• Based on Wikipedia Categories
(1) the categories can be considered a hierarchical structure to more effectively
group and browse Wikipedia articles
(2) the categories are maintained manually (which is very tedious and time-
consuming)
(3) they do not enforce a strict is-a relation to their member articles, which
means that the data contains errors from a supervised learning point of
view.
• list of 98 categories from DBpedia, which contained exactly 100 members
that had an infobox as well as an abstract property
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Keyword search vs. DL-Learner
Keyword search
• Find all “Wrestlers at the 1938 British Empire Games”
{
{Wrestler, 1938, British, Empire, Game},
{Wrestler, 1938, British, Empire},
{Wrestler, 1938, British, Game},
{Wrestler, 1938, Empire, Game},
…
}
• Total of 31 searches for five words (Power set minus the empty word)
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Keyword search vs. DL-Learner
Keyword search
Limit = Based on the assumption that a user
only looks at the first 20, 100, 200 examples
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Keyword search vs. DL-Learner
DL-Learner
• Used same metrics
• 5 randomly selected positive seed instances from the category (navigation
history, string search or facet-based browsing )
• 5 negatives from parallel sister categories (with same predecessor)
• 5 iterations (with a total of 25 positives and negatives)
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Qualitative Results - Examples
• Single feature concepts
• Easy to learn
• If added as intensional definition, e.g. by an admin, they can
• help to identify errors and missing values in the database
• Automatically classify new instances
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Qualitative Results - Examples
• Overly specific concepts
• Partially correct, Defoe is in Bay City, Michigan
• 53 of 100 matched
• Data inspection showed URIs as well as literals as objects
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Qualitative Results - Examples
• Indirect solution concepts
• Read like paraphrases
• no feature (e.g. champion value US_Open)
• SubdividisonName is more frequently used by US cities in DBpedia
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Qualitative Results - Examples
• Zero member concepts
• Northland region is not a clear is-a relation, but rather a tag
• Second one does not have any good features in the data
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Conclusions
• Definition of the NKE paradigm
• Proof of concept implementation
• Technical feasibility
• Web Demo: http://hanne.aksw.org
• We have made progress to bridge the gap between user interaction and
knowledge engineering
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Future Work & Open Questions
• For which purpose can concepts created by users be exploited:
• Improve Navigation via suggestions or hierarchial browsing
• Create domain ontologies
• Create a GUI for different target groups:
• End-users
• Domain experts with some technical skill
• Further evaluation necessary, please contact us for collaborations
• Project page is http://aksw.org/Projects/NKE
http://slideshare.net/kurzum
