Background
Motivation
The development of appropriate technologies to enable direct and efficient access to relevant information is one of the key challenges for the knowledge society. Traditional database query languages are optimised to reconcile large volume data stocks with great efficiency based on exactly specified queries. Today, many application scenarios demand more advanced technologies that offer intelligent support to the user when searching for information. In e-Commerce, for example, the user searching for a product is seldom in a position to formulate an appropriate, exact specification for a query. Frequently, either the necessary background knowledge about the product offering is lacking or, over or under specified database queries lead to empty or unmanageable results, which is of little help in finding a suitable available product. Intelligent product recommendation systems present an alternative which, even with relatively vague desires and needs on the part of the user, can recommend suitable, target oriented products.
Such knowledge-based product recommendation systems are being developed primarily on the basis of technologies from the field of Case-Based Reasoning (CBR). The central idea here is to search on the basis of similarities to the stated enquiry, which always enables the return of the most appropriate, available information sorted by relevance/utility. A major role is played by the inclusion of knowledge domains in the form of ontologies and other application specific similarity measures.
myCBR Workbench and SDK
The development of even a quite simple CBR application already involves a number of steps, such as collecting case and background knowledge, modelling a suitable case representation, defining an accurate similarity measure, implementing retrieval functionality, and implementing user interfaces. Compared to other AI approaches, CBR allows to reduce the effort required for knowledge acquisition and representation significantly, which is certainly one of the major reasons for the commercial success of CBR applications. Nevertheless, implementing a CBR application from scratch remains a time consuming software engineering process and requires a lot of specific experience beyond pure programming skills.
Although CBR research has a history of over 20 years, and in spite of the broad commercial success of CBR applications in recent years, today only few CBR software tools for supporting the development process are available. The key motivation for implementing myCBR was the need for a compact and easy-to-use tool for building prototype CBR applications in teaching, research, and small industrial projects with low effort. Moreover, the tool should be easily extensible in order to facilitate the experimental evaluation of novel algorithms and research results. Therefore, it provides comfortable graphical user interfaces for modelling various kinds of attribute-specific similarity measures and for evaluating the resulting retrieval quality. In order to reduce also the effort of the preceding step of defining an appropriate case representation, it includes tools for generating the case representation automatically from existing raw data. The Software Development Kit (SDK) allows for easy integration into other applications and extension to specific requirements such as additional similarity calculations.
Research background
myCBR's foundation can be retraced to several European research projects, especially INRECA, INRECA II, and WEBSELL:
- INRECA - Induction and Reasoning from Cases (1993-1996). The ESPRIT project INRECA developed tools and methods for developing, validating, and maintaining decision support systems. Among those tools: a descriptive model editor for interactively defining classes, objects, attributes, and relations, and a case manager used this information to build automatically a 'questionnaire' for collecting cases.
- INRECA II - Information and Knowledge Reengineering for Reasoning from Cases (1996-1999). The ESPRIT project INRECA II's major concern was to offer an experience-based development of a methodology and a set of tools that supports the methodology for guiding CBR application development, validation, and maintenance. The INRECA II methodology is composed of a set of steps, guidelines and recommendations that allow a primarily non-CBR user to build, to validate, to maintain, and to scale up a CBR application. These steps include the initial understanding of the CBR technique, the building and the maintenance of a CBR application, and the evaluation, qualification and acceptance process of the CBR system being built. INRECA II showed the applicability of this CBR methodology to a variety of application tasks. These applications leverage the project in the sense that they will enable to apply an initial methodology, to make it grow and finally validate it. The project also resulted in a set of tools for building integrated case-based reasoning applications. The tools represented a significant commercial opportunity for the industrial partners of the project. Furthermore, INRECA II enhanced CBR technology in the areas of solution adaptation. (see: R. Bergmann, K.-D. Althoff, S. Breen, M. Göker, M. Manago, R. Traphöner, and S. Wess. Developing Industrial Case-Based Reasoning Applications: The INRECA Methodology. LNAI 1612. Springer-Verlag, Berlin, second edition, 2003.)
- WEBSELL - Intelligent Sales Assistants for the World Wide Web (1998-2000). The ESPRIT project WEBSELL developed Intelligent Sales Support technology and products for the World Wide Web. These products support web shoppers in two aspects of the sales process neglected before on the Web; helping the customer to select or configure the product that meets her needs and supporting the customer in navigating through the space of possible product alternatives.
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