: FOIL is a learning system that constructs Horn clause programs from examples. This paper summarises the development of FOIL from 1989 up to early 1993 and evaluates its effectiveness on a non-trivial sequence of learning tasks taken from a Prolog programming text. Although many of these tasks are handled reasonably well, the experiment highlights some weaknesses of the current implementation. Areas for further research are identified. 1. Introduction The principal differences between zeroth-order and first-order supervised learning systems are the form of the training data and the way that a learned theory is expressed. Data for zeroth-order learning programs such as ASSISTANT [Cestnik, Kononenko and Bratko, 1986], CART [Breiman, Friedman, Olshen and Stone, 1984], CN2 [Clark and Niblett, 1987] and C4.5 [Quinlan, 1992] comprise preclassified cases, each described by its values for a fixed collection of attributes. These systems develop theories, in the form of decision trees o...

An approach to clustering is presented that adapts the basic top-down induction of decision trees method towards clustering. To this aim, it employs the principles of instance based learning. The resulting methodology is implemented in the TIC (Top down Induction of Clustering trees) system for first order clustering. The TIC system employs the first order logical decision tree representation of the inductive logic programming system Tilde. Various experiments with TIC are presented, in both propositional and relational domains.

Computing least common subsumers (Ics) and most specific concepts (msc) are inference tasks that can support the bottom-up construction of knowledge bases in description logics. In description logics with existential restrictions, the most specific concept need not exist if one restricts the attention to concept descriptions or acyclic TBoxes. In this paper, we extend the notions les and msc to cyclic TBoxes. For the description logic EC (which allows for conjunctions, existential restrictions, and the top-concept), we show that the les and msc always exist and can be computed in polynomial time if we interpret cyclic definitions with greatest fixpoint semantics.

... 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.

In this introduction, we define the term bias as it is used in machine learning systems. We motivate the importance of automated methods for evaluating and selecting biases using a framework of bias selection as sem'ch in bias and meta-bias spaces. Recent research in the field of mac}fine learning bias is stmmarized.

We introduce a methodology for concept learning from texts that relies upon secondorder reasoning about statements expressed in a (first-order) terminological representation language. This metareasoning approach allows for quality-based evaluation and selection of alternative concept hypotheses. Appeared in: S. Wermter, E. Riloff, G. Scheler (Eds.), Connectionist, Statistical and Symbolic Approaches to Learning for Natural Language Processing, Berlin etc: Springer, 1996, pp.453-468, (LNAI 1040) Connectionist,23 0 Language92-9295 1040) Learning from Texts - A Terminological Metareasoning Perspective Udo Hahn, Manfred Klenner & Klemens Schnattinger Freiburg University Computational Linguistics Group F Europaplatz 1, D-79085 Freiburg, Germany fhahn,klenner,schnattingerg@coling.uni-freiburg.de Abstract We introduce a methodology for concept learning from texts that relies upon second-order reasoning about statements expressed in a (first-order) terminological representation langua...

A novel first order clustering system, called C 0.5, is presented. It inherits its logical decision tree formalism from the TILDE system, but instead of using class information to guide the search, it employs the principles of instance based learning in order to perform clustering. Various experiments are discussed, which show the promise of the approach. 1 Introduction A decision tree is usually seen as representing a theory for classification of examples. If the examples are positive and negative examples for one specific concept, then the tree defines these two concepts. One could also say, if there are k classes, that the tree defines k concepts. Another viewpoint is taken in Langley's Elements of Machine Learning [ Langley, 1996 ] . Langley sees decision tree induction as a special case of the induction of concept hierarchies. A concept is associated with each node of the tree, and as such the tree represents a kind of taxonomy, a hierarchy of many concepts. This is very similar...

Feature construction through aggregation plays an essential role in modeling relational domains with one-to-many relationships between tables. One-to-many relationships lead to bags (multisets) of related entities, from which predictive information must be captured. This paper focuses on aggregation from categorical attributes that can take many values (e.g., object identifiers). We present a novel aggregation method as part of a relational learning system ACORA, that combines the use of vector distance and meta-data about the class-conditional distributions of attribute values. We provide a theoretical foundation for this approach deriving a “relational fixed-effect ” model within a Bayesian framework, and discuss the implications of identifier aggregation on the expressive power of the induced model. One advantage of using identifier attributes is the circumvention of limitations caused either by missing/unobserved object properties or by independence assumptions. Finally, we show empirically that the novel aggregators can generalize in the presence of identifier (and other high-dimensional) attributes, and also explore the limitations of the applicability of the methods. 1

Abstract — This work presents a method founded in instancebased learning for inductive (memory-based) reasoning on ABoxes. The method, which exploits a semantic dissimilarity measure between concepts and instances, can be employed both to answer class membership queries and to predict new assertions that may be not logically entailed by the knowledge base. In a preliminary experimentation, we show that the method is sound and it is actually able to induce new assertions that might be acquired in the knowledge base. I.

Metapattern (also known as metaquery) is a new approach for integrated data mining systems. Different from a typical "tool-box" like integration, where components must be picked and chosen by users without much help, metapatterns provide a common representation for inter-component communication as well as a human interface for hypothesis development and search control. One weakness of this approach, however, is that the task of generating fruitful metapatterns is still a heavy burden for human users. In this paper, we describe a metapattern generator and an integrated discovery loop that can automatically generate metapatterns. Experiments in both artificial and real-world databases have shown that this new system goes beyond the existing machine learning technologies, and can discover relational patterns without requiring humans to pre-label the data as positive or negative examples for some given target concepts. With this technology, future data mining systems could discover high-qu...