Explaining clusters with inductive logic programming and linked data

CEUR Workshop Proceedings

Volumn 1035, Issue , 2013, Pages 257-260

  Tiddi, Ilaria ^a   D'Aquin, Mathieu ^a   Motta, Enrico ^a  

^a OPEN UNIVERSITY   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

DATA HANDLING; DATA MINING; SEMANTIC WEB; SOCIAL NETWORKING (ONLINE);

BACK-GROUND KNOWLEDGE; CROSS-DISCIPLINARY; INDUCTIVE LOGIC; LARGE AMOUNTS OF DATA; LINKED DATUM;

INDUCTIVE LOGIC PROGRAMMING (ILP);

EID: 84924688877     PISSN: 16130073     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (8)

1. d'Aquin, M., & Jay, N. (2013). Interpreting Data Mining Results with Linked Data for Learning Analytics: Motivation, Case Study and Directions. In Third Conference in Learning Analytics and Knowledge (LAK), Leuven, Belgium.
2. Fayyad, U., Piatetsky-Shapiro, G., & Smyth, P. (1996). From data mining to knowledge discovery in databases. AI magazine, 17(3), 37.
3. Muggleton, S., & De Raedt, L. (1994). Inductive logic programming: Theory and methods. The Journal of Logic Programming, 19, 629-679.
4. Narasimha, V., Kappara, P., Ichise, R., & Vyas, O. P. (2011). LiDDM: A Data Mining System for Linked Data.
5. Lavrac N., Flach P., and Zupan B. (1999). Rule Evaluation Measures: A Unifying View. In Proceedings of the 9th International Workshop on Inductive Logic Programming (ILP '99). Springer-Verlag, London, UK, 174-185.
6. Paulheim, H., & Fumkranz, J. (2012, June). Unsupervised generation of data mining features from linked open data. In Proceedings of the 2nd International Conference on Web Intelligence, Mining and Semantics (p. 31). ACM.
7. Paulheim, H. (2012). Generating possible interpretations for statistics from Linked Open Data. In The SemanticWeb: Research and Applications. Springer, pp. 560574.
8. Verborgh, R., Van Deursen, D., Mannens, E., & Van de Walle, R. (2010). Enabling advanced context-based multimedia interpretation using linked data.