explaining data patterns using background knowledge from linked data
TRANSCRIPT
Explaining data patterns using background knowledgefrom Linked Data
Ilaria Tiddi
Knowledge Media Institute, The Open University
Oct 22nd, 2013
Problem description
Regularities in data areI
clusters of bookscalled patterns borrowed by students
Each pattern has itsI
each cluster represents aown interpretation faculty the students enrol
Interpretations requireI
why is there a relation betweenan explanation certain books and a faculty?
Explanations come fromI
books and faculties are likelydata information in some
to have the same topicsbackground knowledge
Problem
Where and how to find the background knowledge B to explain somepatterns?
B usually comes from domain experts
Can the expert be supported in finding B?
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Semantics in Knowledge Discovery
Data Warehouse
Cleaned Data
Extracted Knowledge (Patterns)
Assimilated Knowledge
Data PreprocessingFiltering, organising, data preprocessing
using semantics
Data MiningSemantics to improve
Machine Learning Algorithms
Results Interpretation
Generating hypotheses using semantics
Linked Dataontologiesknowledge bases
Semantics
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Research hypothesis and questions
Hypothesis: Linked Data can be exploited as backgroundknowledge to explain patterns of data
Raised questions
Q1Data
How to select a dataset?
SelectionHow to detect data in a
selected dataset?
Q2Hypotheses
Which process
Generationgenerates explanations
using B?
Q3Hypotheses How to know the
Evaluation explanation is valid?
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An Inductive Logic Programming approach
1 Consider items in aset of patterns 2 Get information from
Linked Data
3 Build the backgroundknowledge
4 Analyse a single patterna time
5 Generate hypothesesdiscriminant for it
6 Validate the hypotheses
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Preliminary results
Pattern B Hypotheses Wracc% F%
MusicB1 books History of popular music
topic0.2 14.5
B2 books Musicbroader topic
0.3 21.2
EnglishB1 books English phonetics
topic0.03 0.5
B2 books Linguisticsbroader topic
1.2 17.2
PoliticsB1 books UK politics and government
topic0.05 3.7
B2 books International Relationsbroader topic
0.2 10.1
N N N
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Evaluation Plan
1 – Automatic Evaluation
The more Linked Data knowledge is added to B, the better theexplanation is
British Library books History of popular musictopic
14.5%
British Library +books Music
broader topic21.2%
Library of Congress
2 – Manual Evaluation
Cross-validation of a domain expert and our ILPprocess
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Reflections
Preliminary work – Hypotheses Generation
ILP and Linked Data = promising results
<owl:sameAs> retrieve connected knowledge from LD
Work in progress – Automatic data selection
Dynamic traversal of LD to collect data (AnytimeAlgorithm)
Simultaneous B building and hypotheses evaluation
Guiding the search in Linked Data (Properties Evaluation)
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Thank you
Questions?
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