This document discusses combining data mining and ontology engineering to enrich ontologies and linked data. It describes how the knowledge discovery process and ontology engineering process can evolve together, with data mining interpreting ontologies and mining data to discover new concepts and relationships to enrich ontologies. It also outlines major new issues that arise from mining linked and ontology-based data, such as ontology-guided data mining and versioning to track changes between models.

1. Combining Data Mining and Ontology
Engineering to enrich Ontologies and
Linked Data
Mathieu d’Aquin
Knowledge Media Institute (Kmi), The Open University, UK (@mdaquin)
Gabriel Kronberger
University of Applied Science Upper Austria, School for Informatics, Communications and Media
Mari Carmen Suárez-Figueroa
Ontology Engineering Group, Departamento de Inteligencia Artificial, Facultad de Informática,
Universidad Politécnica de Madrid

2. The Knowledge Discovery Process

3. The Knowledge Discovery Process
Ontology Patterns?
??
??
??
populatedBy/modelling/characterising/structuring?
Ontologies?

4. The Knowledge Discovery Process

5. The Knowledge Discovery Process

6. The Knowledge Discovery Process

7. The Ontology Engineering Process
Traditionally In Linked Data
competency through existing
Ellicitate
questions, key Ellicitate domain information
knowledge
concepts, etc. systems, etc
Model diagrams, etc. Reuse from find commonly
knowledge others used vocabularies
Represent align, fill the gaps,
OWL, RDFS, etc. Combine
knowledge etc.
In both cases, it is expected that the data will somehow fit
the ontology, that the ontology will support relevant
applications, and support the inference of new information

8. Knowledge Engineering and Knowledge
Discovery: a co-evolution process?
Ellicitate
knowledge/domain
Model
knowledge/Reuse
Represent
knowledge/Combine
Ontologies/
Knowledge
Interpret
Mine
Data
Data
Data Pre-process

9. Knowledge Engineering and Knowledge
Discovery: a co-evolution process?

10. Major (new) issues 1/4
Ontology-based filtering, checking and
interpretation of DM results
Zablith et al., Using Ontological Contexts
to Assess the Relevance of Statements in
Ontology Evolution, EKAW 2010
Data
Data
Data
Text
Docs Analysis
Ontologies
Mine
Relation
Discovery
New concepts
Results ??
Ontology
New relations

11. Major (new) issues 2/4
Mining from Linked and Ontology based data
Nikolov et al., Unsupervised Learning of
Link Discovery Configuration, ESWC 2012
Ontologies
Ontolo Ontolo
gy gy
Data Data
Data
Data Data
Genetic
Algorithm
Mine
Similarity Configuration
Link Discovery
Results
??
Links

12. Major (new) issues 3/4
Ontology-guided data mining
d’Aquin and Motta, Extracting Relevant
Questions to an RDF Dataset Using Formal
Ontologies Concept Analysis, K-CAP 2011
Ontolo Inference +
gy Formal
Context
Data Generation
RDF
Data
Mine Formal Context
Prominent
questions/qu
eries Formal
Lattice
?? Interpr
Concept
Results etation
Analysis

13. Major (new) issues 4/4
Versioning and consistency
Requires keeping track
of the different models
and their versions, the
Data
Data agreement and
Data
disagreement
between them, as well
Ontologies
Ontologies as the areas of
Mine Mine Ontologies
Mine ?? consensus and
controveries
(d’Aquin, Formally Measuring
Result Result
Agreement and Disagreement
s s Result in Ontologies, K-CAP 2009)
s Lead to the notion of
ontology
convergence

14. Conclusion
• Many existing works have considered the
connection between data mining and ontology
engineeing
• A large scale, web of linked data and ontologies
make the related challenges more prominent…
• … and need real interactions between the two
approaches, not as disconnected components.
• Need to investigate and exploit the colateral
benefits of ontology engineering and knowledge
discovery…
• … coming up with new techniques for enriching
knowledge from mined data, and guiding the
extraction of further data wit ontological knowledge

15. Thank you
m.daquin@open.ac.uk
http://people.kmi.open.ac.uk/mathieu
@mdaquin