Meike Reichle

The SEASALT architecture and its realization within the docQuery project

SEASALT (Sharing Experience using an Agent-based System Architecture LayouT) presents an instantiation of the Collaborating Multi-Expert Systems (CoMES) approach [1]. It offers an application-independent architecture that features knowledge acquisition from a web-community, knowledge modularization, and agent-based knowledge maintenance. The paper introduces an application domain which applies SEASALT and describes each part of the novel architecture for extracting, analyzing, sharing and providing community experiences in an individualized way.

Knowledge engineering within the application-independent architecture SEASALT

Sharing Experience using an Agent-based System Architecture Layout (SEASALT) presents an instantiation of the Collaborating Multi-expert Systems (CoMES) approach. It integrates techniques from software engineering and combines them with artificial intelligence methodologies. The approach offers an application-independent architecture that features knowledge acquisition from a web-community, knowledge modularisation and agent-based knowledge maintenance. The paper introduces a travel medicine as application domain which applies SEASALT and describes each part of the novel architecture for extracting, analysing, sharing and providing community experiences in an individualised way.

Data preparation of web log files for marketing aspects analyses

This article deals with several aspects of a marketing-oriented analysis of web log files. It discusses their preprocessing and possible ways to enrich the raw data that can be gained from a web log file in order to facilitate a later use in different analyses. Further, we look at the question which requirements a good web log analysis software needs to meet and offer an overview over current and future analysis practices including their advantages and disadvantages.

Realizing Modularized Knowledge Models for Heterogeneous Application Domains

This paper addresses the realization of modularized knowledge models within a heterogeneous application domain using an existing knowledge management tool. The application domain we deal with is travel medicine, which combines medical aspects with geography, climate, holiday activities and associated traveling conditions. In this paper we present the applications requirements and show how knowledge models can be developed using an industrial strength application. Furthermore we present the challenges of a knowledge model based on multi-case bases whereas each case base represents its own area of expertise. Hence, we introduce our knowledge model for the travel medicine application and exemplify the implementation of typical data types and similarity measures.

Case-Based Reasoning in a Travel Medicine Application

This chapter focuses on knowledge management for complex application domains using Collaborative Multi-Expert-Systems. We explain how different knowledge sources can be described and organized in order to be used in collaborative knowledge-based systems. We present the docQuery system and the application domain travel medicine to exemplify the knowledge modularization and how the distributed knowledge sources can be dynamically accessed and finally reassembled. Further on we present a set of properties for the classification of knowledge sources and in which way these properties can be assessed.

A Value Supplementation Method for Case Bases with Incomplete Information

In this paper we present a method for supplementing incomplete cases with information from other cases within a case base. The acquisition of complete and correct cases is a time-consuming task, but nevertheless crucial for the quality and acceptance of a case-based reasoning system. The method introduced in this paper uses association rules to identify relations between attributes and, based on the discovered relations we are able to supplement values in order to complete cases. We argue that using these related attributes when retrieving supplementation candidates will yield better results than simply picking the case with the highest global similarity. The evaluation of the method is carried out using four different publicly available case bases.