The alarms of medical devices are a matter of concern in critical and perioperative care. The
frequent false alarms not only are a nuisance for patients and caregivers but can also …

Ontology is one of the fundamental cornerstones of the semantic Web. The pervasive use of
ontologies in information sharing and knowledge management calls for efficient and …

The Semantic Web relies heavily on formal ontologies to structure data for comprehensive
and transportable machine understanding. Thus, the proliferation of ontologies factors …

Metalearning is the study of principled methods that exploit metaknowledge to obtain
efficient models and solutions by adapting machine learning and data mining processes …

Ontology Learning greatly facilitates the construction of ontologies by the ontology engineer.
The notion of ontology learning that we propose here includes a number of complementary …

Abstract Inductive Logic Programming (ILP) is an area of Machine Learning which has now
reached its twentieth year. Using the analogy of a human biography this paper recalls the …

The growing volume of information databases presents opportunities for advanced data
analysis techniques from machine learning (ML) research. Practical applications of ML are …

A relational instance-based learning algorithm, called Ribl, is motivated and developed in
this paper. We argue that instancebased methods o er solutions to the often unsatisfactory …

We introduce a methodology for automating the maintenance of domain-specific taxonomies
based on natural language text understanding. A given ontology is incrementally updated as …

It is conventional wisdom in machine learning and data mining that logical models such as
rule sets are more interpretable than other models, and that among such rule-based models …