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 behavior of current inductive logic programming #ILP# approaches in domains with continuous attribute values and in domains with noisy attributes and#or examples. Three research issues that emerge when a propositional instance-based learner is adapted to a #rst-order representation are identi#ed: #1# construction of cases from the knowledge base, #2# computation of similaritybetween arbitrarily complex cases, and #3# estimation of the relevance of predicates and attributes. Solutions to these issues are developed. Empirical results indicate that Ribl is able to achieve high classi#cation accuracy in a variety of domains. to appear in: Proc. 13th International Conference on Machine Learning, L. Saitta #ed.#, Morgan Kaufmann, 1996 1 Introduction The #eld of Inductive Logic Programming ...

The representation formalism as well as the representation language is of great importance for the success of machine learning. The representation formalism should be expressive, efficient, useful, and applicable. First-order logic needs to be restricted in order to be efficient for inductive and deductive reasoning. In the field of knowledge representation term subsumption formalisms have been developed which are efficient and expressive. In this paper, a learning algorithm, KLUSTER, is described which represents concept definitions in this formalism. KLUSTER enhances the representation language if this is necessary for the discrimination of concepts. Hence, KLUSTER is a constructive induction program. KLUSTER builds the most specific generalization and a most general discrimination in polynomial time. It embeds these concept learning problems into the overall task of learning a hierarchy of concepts.

... we show in this paper some exemplary techniques in the ontology learning cycle that we have implemented in our ontology learning environment, KAON Text-To-Onto.

The Semantic Web relies heavily on the formal ontologies that structure underlying data for the purpose of comprehensive and transportable machine understanding. Therefore, the success of the Semantic Web depends strongly on the proliferation of ontologies, which requires fast and easy engineering of ontologies and avoidance of a knowledge acquisition bottleneck.

Since real world databases are known to be very large, they raise problems of the access. Therefore, real world databases onlycan be accessed by database management systems and the number of accesses has to be reduced to a minimum. Considering this property, we are forced to use standard set--oriented interfaces of relational database management systems. We present a system for discovering data dependencies, which is build upon a set--oriented interface. The point of main effort has been put on the discovery of domain restrictions, unary inclusionand functional dependencies in relational databases. The system also embodies an inference relation to minimize database access. 1 Introduction Data dependencies are the most common type of semantic constraints in relational databases which determine the database design. Despite the advent of highly automated tools, database design still consists basically of two types of activities: first, reasoning about data types and data dependencies and...

. This paper presents a novel idea to the problem of learning concept descriptions from examples. Whereas most existing approaches rely on a large number of classified examples, the approach presented in the paper is aimed at being applicable when only a few examples are classified as positive (and negative) instances of a concept. The approach tries to take advantage of the information which can be induced from descriptions of unclassified objects using a conceptual clustering algorithm. The system Cola is described and results of applying Cola in two real-world domains are presented. 1 Introduction to appear in: Proc. 7th European Conference on Machine Learning (ECML-94), F. Bergadano, L. De Raedt (eds.), Springer-Verlag, Lecture Notes in Artificial Intelligence The learning task considered in "learning-from-examples" is to induce a description of a set of objects which are classified by a user or a system as instances (and non-instances) of a general meaningful class, i.e., a conc...

. When learning from very large databases, the reduction of complexity is extremely important. Two extremes of making knowledge discovery in databases (KDD) feasible have been put forward. One extreme is to choose a very simple hypothesis language, thereby being capable of very fast learning on real-world databases. The opposite extreme is to select a small data set, thereby being able to learn very expressive (first-order logic) hypotheses. A multistrategy approach allows one to include most of these advantages and exclude most of the disadvantages. Simpler learning algorithms detect hierarchies which are used to structure the hypothesis space for a more complex learning algorithm. The better structured the hypothesis space is, the better learning can prune away uninteresting or losing hypotheses and the faster it becomes. We have combined inductive logic programming (ILP) directly with a relational database management system. The ILP algorithm is controlled in a model-driven way by t...

In this paper, we are interested in empirical multiple predicate learning. The first solution to this problem that consists in putting together the definitions obtained by a single predicate learning system is rarely interesting. We explain why and we show how a single predicate learning system has been extended to a multiple predicate learning system called mult icn, which learns definite logic programs. Our system is based on the notion of extensional coverage but during the construction of the program, it builds a set of recursive dependencies that gives information about the mutually recursive calls. It has therefore two main advantages. First, it ensures that the learned program is globally consistent and complete, i.e., the learned program does not only extensionally cover the positive examples and reject the negative ones but it does prove that positive examples are true and negative ones are false in the semantics of the learned program. Secondly it uses only the k...

The growing volume of information databases presents opportunities for advanced data analysis techniques from machine learning (ML) research. Practical applications of ML are very different from theoretical or empirical studies, involving organizational and human aspects, and various other constraints. Despite the importance of applied ML, little has been discussed in the general ML literature on this topic. In order to remedy this situation, we studied practical applications of ML and developed a proposal for a seven-steps process that can guide practical applications of ML in engineering. The process is illustrated by relevant applications of ML in civil engineering. This illustration shows that the potential of ML has only begun to be explored, but also cautions that in order to be successful, the application process must carefully address the issues related to the seven-step process. 1 Introduction Over the last several decades we have witnessed an explosion in information generat...

. Integrity constraints are important logical tools for the general organization of knowledge. Integrity constraints (in short: ICs), which are commonly used in the field of deductive databases, specify general regularities like "a son is not older than his father." They facilitate the organization of knowledge in expert systems and can speed up the queryresponse time significantly. This paper presents an approach for inductively generating compact integrity constraints from knowledge bases, represented in first-order logic. To obtain the most powerful ICs, the huge space of potential ICs, which are principally consistent with a given knowledge base, is restricted by IC-schemes. IC-schemes specify ICs syntactically. The proposed method searches the resulting space of ICs efficiently by pruning away whole subspaces. The approach is also capable of detecting irregularities in "noisy" knowledge bases which might be inconsistent. Empirical results illustrate the appropriateness of this me...