Attempts to overcome limitations in the attribute-value representation for machine learning has led to much interest in learning from structured data, concentrated in the research areas of inductive logic programming (ILP) and multi-relational data mining (MDRM). The expressivenessa nd encapsulationo f the object-oriented data model has led to its widespread adoption in software and database design. The considerable congruence between this model and individual-centred models in inductive logic programming presents new opportunities for mining object data specific to its domain. This thesis investigates the use of object-orientation in knowledge representation for multi-relational data mining. We propose a language for expressing object model metaknowledge and use it to extend the reasoning mechanisms of an object-oriented logic. A refinement operator is then defined and used for feature search in a object-oriented propositionalisation-based ILP classifier. An algorithm is proposed for reducing the large number of redundant features typical in propositionalisation. A data mining system based on the refinement operator is implemented and demonstrated on a real-world computational linguistics task and compared with a conventional ILP system. Keywords: Object orientation; data mining; inductive logic programming; propositionalisation; refinement operators; feature reduction
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:440262 |
| Date | January 2007 |
| Creators | Rawles, Simon Alan |
| Contributors | Flach, Peter |
| Publisher | University of Bristol |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://hdl.handle.net/1983/c13bda2c-75c9-4bfa-b86b-04ac06ba0278 |
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