The focused access to knowledge resources like intranet documents plays a vital role in knowledge management and supports in general the shifting towards a Semantic Web. Ontologies act as a conceptual backbone for semantic document access by providing a common understanding and conceptualization of a domain. Building domain-specific ontologies is a time-consuming and expensive manual construction task. This paper describes our actual and ongoing work in supporting semi-automatic ontology acquisition from a corporate intranet of an insurance company. We present a comprehensive architecture and generic method for discovering a domain-tailored ontology from given intranet resources. 1 Introduction The amount of information available to corporate employees has grown drastically with the use of intranets. Unfortunately this growth of available information has made the access to useful or necessary information much more difficult due to the fact that the access is usually based on ...

Ontologies play an increasingly important role in Knowledge Management. One of the main problems associated with ontologies is that they need to be constructed and maintained. Manual construction of larger ontologies is usually not feasible within companies because of the effort and costs required. Therefore, a semi-automatic approach to ontology construction and maintenance is what everybody is wishing for. The paper presents a framework for semi-automatically learning ontologies from domainspecific texts by applying machine learning techniques. The TEXT-TO-ONTO framework integrates manual engineering facilities to follow a balanced cooperative modelling paradigm. 1

... This paper is focused on the extraction and use of logical rules for data understanding. All aspects of rule generation, optimization, and application are described, including the problem of finding good symbolic descriptors for continuous data, tradeoffs between accuracy and simplicity at the rule-extraction stage, and tradeoffs between rejection and error level at the rule optimization stage. Stability of rule-based description, calculation of probabilities from rules, and other related issues are also discussed. Major approaches to extraction of logical rules based on neural networks, decision trees, machine learning, and statistical methods are introduced. Optimization and application issues for sets of logical rules are described. Applications of such methods to benchmark and real-life problems are reported and illustrated with simple logical rules for many datasets. Challenges and new directions for research are outlined.

This paper describes our actual and ongoing work in supporting semi-automatic ontology acquisition from a corporate intranet of an insurance company. A comprehensive architecture and a system for semi-automatic ontology acquisition supports processing semi-structured information (e.g. contained in dictionaries) and natural language documents and including existing core ontologies (e.g. GermaNet, WordNet). We present a method for acquir- ing a application-tailored domain ontology from given heterogeneous intranet sources.

This report provides a description and a user's guide for AQ19, a program for machine learning and pattern discovery. AQ19 works in two modes: Theory Formation and Pattern Discovery. In Theory Formation mode, given examples of two or more concepts, AQ19 hypothesizes general descriptions of these concepts optimized according to a modifiable criterion of hypothesis preference. In Pattern Discovery mode, given data with indicated input and output variables, AQ19 determines strong patterns in the relationship between the input and output variables...

Current research on the VINLEN inductive database system is briefly reviewed and illustrated by selected results. The goal of research on VINLEN is to develop a methodology for deeply integrating a wide range of knowledge generation operators with a relational database and a knowledge base. The current system has already integrated an AQ learning system for generating attributional rules in two modes: theory formation, in which generated rules are consistent and complete with regard to data, and pattern discovery, in which generated rules represent strong patterns, not necessarily consistent or complete. It also has integrated a conceptual clustering module for splitting data into conceptual classes, and providing descriptions of those classes. Preliminary data management and knowledge visualization operators, such as the intelligent target data generator (ITG) and concept association graph display, have also been integrated. To facilitate an easy interaction with the system, a user-oriented visual interface has been implemented. An example of results from applying VINLEN to a medical problem domain is presented to illustrate VINLEN knowledge discovery and representation capabilities.

Knowledge scouts are software agents that autonomously search for and synthesize user-oriented knowledge (target knowledge) in large local or distributed databases. A knowledge generation metalanguage, KGL, is used to creating scripts defining such knowledge scouts. Knowledge scouts operate in an inductive database, by which we mean a database system in which conventional data and knowledge management operators are integrated with a wide range of data mining and inductive inference operators. Discovered knowledge is represented in two forms: (1) attributional rules, which are rules in attributional calculus---a logic-based language between propositional and predicate calculus, and (2) association graphs, which graphically and abstractly represent relations expressed by the rules. These graphs can depict multi-argument relationships among different concepts, with a visual indication of the relative strength of each dependency. Presented ideas are illustrated by two simple knowledge scouts, one that seeks relations among lifestyles, environmental conditions, symptoms and diseases in a large medical database, and another that searches for patterns of children's behavior in the National Youth Survey database. The preliminary results indicate a high potential utility of the presented methodology as a tool for deriving knowledge from databases. Keywords: data mining, knowledge discovery, knowledge scouts, inductive databases, knowledge visualization, knowledge generation language, association graphs, attributional calculus. 2 1

Knowledge scouts are software agents that autonomously synthesize knowledge of interest to a given user (target knowledge) by applying inductive database operators to a local or distributed dataset. This paper describes briefly a method and a scripting language for developing knowledge scouts, and then reports on experiments with a knowledge scout, SCAMP, for discovering patterns characterizing relationships among lifestyles, symptoms and diseases in a large medical database. Discovered patterns are presented in two forms: (1) attributional rules, which are expressions in attributional calculus, and (2) association graphs, which graphically and abstractly represent relations expressed by the rules. Preliminary results indicate a high potential utility of the presented methodology for deriving useful and understandable knowledge.

This paper addresses the well-known classification task of data mining, where the goal is to discover rules predicting the class of examples (records of a given data set). In the context of data mining, small disjuncts are rules covering a small number of examples. Hence, these rules are usually error-prone, which contributes to a decrease in predictive accuracy. At first glance, this is not a serious problem, since the impact on predictive accuracy should be small. However, although each small disjunct covers few examples, the set of all small disjuncts can cover a large number of examples. This paper presents evidence that this is the case in several data sets. This paper also addresses the problem of small disjuncts by using a hybrid decision-tree/genetic algorithm approach. In essence, examples belonging to large disjuncts are classified by rules produced by a decision-tree algorithm (C4.5), while examples belonging to small disjuncts are classified by a genetic algorithm specifically designed for discovering small-disjunct rules. We present results comparing the predictive accuracy of this hybrid system with the prediction accuracy of three versions of C4.5 alone in eight public domain data sets. Overall, the results show that our hybrid system achieves better predictive accuracy than all three versions of C4.5 alone.

The process of applying machine learning to data mining may require many trials, backtracking, and multiple executions of different learning and inference procedures. Such a process can be time-consuming, laborious, and prone to errors. To overcome these problems, this paper proposes an integration of diverse learning and inference procedures into a system that can automatically pursue different data mining tasks according to a high-level plan developed by a user. The solution involves a meta-language, called KGL (Knowledge Generation Language), for specifying a data exploration process in terms of high-level operators and conditional statements that depend on the results of previous operators. Operators invoke corresponding machine learning and inference programs, and specify their parameters according to the current tasks and previous results. To assist in illustrating the outcomes of exploration they are organized into association graphs (AGs), which can indicate logical, statistica...