We propose a novel method for covariance matrix estimation based on shrinkage with a target inferred from biological background knowledge using methods of inductive logic programming. We show that our novel method improves on the state of the art when sample sets are small and some background knowledge expressed in a subset of firstorder logic is available. As we show in the experiments with ge...

Semantic matching determines the mappings between the nodes of two graphs (e.g., ontologies) by computing logical relations (e.g., subsumption) holding among the nodes that correspond semantically to each other. We present an approach to deal with the lack of background knowledge in matching tasks by using semantic matching iteratively. Unlike previous approaches, where the missing axioms are m...

This essay defends the view that inductive reasoning involves following inductive rules against objections that inductive rules are undesirable because they ignore background knowledge and unnecessary because Bayesianism is not an inductive rule. I propose that inductive rules be understood as sets of functions from data to hypotheses that are intended as solutions to inductive problems. Accord...

This paper presents a new relational learner that can e ciently handle a large-scale type hierarchy (i.e.,is a relations). Relational Learner with Hierarchical Background Knowledge (RHB) generates typed Prolog programs that discriminate between positive and negative examples on the basis of background knowledge including a large-scale type hierarchy. Previous learners, such as FOIL and GOLEM, v...

We describe our approach to document representation that captures contextual dependencies between terms in a corpus and makes use of these dependencies to represent documents. We have tried our representation scheme for automatic document categorisation on the Reuters’ test set of documents. We achieve a precision recall break even point of 84% which is comparable to the best known published re...

We present a description of three different algorithms that use background knowledge to improve text classifiers. One uses the background knowledge as an index into the set of training examples. The second method uses background knowledge to reexpress the training examples. The last method treats pieces of background knowledge as unlabeled examples, and actually classifies them. The choice of b...

We are interested in using Inductive Logic Programming (ILP) to infer grammars representing sets of protein sequences. ILP takes as input both examples and background knowledge predicates. This work is a rst step in optimising the choice of background knowledge predicates for predicting the function of proteins. We propose methods to obtain di erent sets of background knowledge. We then study t...

Clinical patient data, such as medical images and reports, establish the basis of the diagnostic process. In order to improve the access to heterogeneous and distributed clinical data sources recent work concentrates on extracting semantic annotations with links to concepts of medical ontologies, such as RadLex, FMA, SNOMED CT or others. However, these annotations are not always on the appropri...

The definition of similarity measures—one core component of each CBR application—leads to a serious knowledge acquisition problem if domain and application specific requirements have to be considered. To reduce the knowledge acquisition effort, different machine learning techniques have been developed in the past. In this paper, enhancements of our framework for learning knowledge-intensive sim...

It has been shown that prior knowledge and information are organized according to categories, and that also background knowledge plays an important role in classification. The purpose of this study is first, to investigate the relationship between background knowledge and text classification, and second, to incorporate this relationship in a computational model. Our behavioral results demonstra...