Discovery of Class Relations in Exception Structured Knowledge Bases

Evaluating the success of a knowledge acquisition (KA) task is difficult and expensive. Most evaluation approaches rely on the expert themselves, either directly, or indirectly by relying on data previously prepared with the help of experts. In incremental KA, knowledge base (KB) errors are monitored and corrected by an expert. Thus, during its evolution a record of the knowledge based system (KBS) performance is usually easy to keep. We propose to integrate with the incremental KA process, an evaluation process based on a statistical analysis to estimate the effectiveness of the KBS, as the KBS is actually evolved. We tailor such an analysis for Ripple Down Rules (RDR), which is an effective incremental KA methodology where a record of the KBS performance can be easily derived and updated as new cases are processed by the system. An RDR KB is a collection of rules with hierarchical exceptions, which are entered and validated by the expert in the context of their use. This greatly facilitates the knowledge maintenance task which, characteristically in RDR, overlaps with the incremental KA process. The work in this paper aims to overlap evaluation with maintenance and development of the knowledge base. It also minimises the major expense in deploying the RDR KBS, that of keeping a domain expert on-line during maintenance and the initial period of deployment. The expert is not kept on-line longer than it is absolutely necessary. We use the structure and semantics of an evolving RDR KB, combined with proven machine learning statistical methods, to estimate the added value in every KB update, as the KB evolves. Using these values, the decisionmakers in the organisation employing the KBS can apply a cost-benefit analysis of the continuation of the incremental KA process. They can then determine when this process, involving keeping an expert on-line, should be terminated.

The need for analysis and modeling of knowledge has been espoused by many researchers as a prerequisite to building knowledge based systems (KBS). This approach has done little to alleviate the knowledge acquisition (KA) bottleneck or the maintenance problems associated with large KBS. For actual KA and maintenance we prefer to use a technique, known as ripple down rules (RDR) that is simple, yet reliable, and later see what models can be produced from the knowledge for the purpose of reuse. Tools based on Formal Concept Analysis have been added to RDR to uncover and explore the underlying conceptual structures.

Knowledge acquisition (KA) encompasses working with the expert to model the domain and a suitable problem solving method as preconditions for building a knowledge based system (KBS) and secondly working with the expert to populate the knowledge base. Ripple Down Rules (RDR) focuses on the second of these activities and allows an expert to populate a knowledge base (KB) without any knowledge engineering assistance. It is based on the idea that since the knowledge an expert provides is a justification of his or her judgment given in a specific context, this knowledge should only be used in the same context. Although the approach has been used for large single classification systems, it has the potential problem that the local nature of the knowledge may result in much repeated knowledge in the KB and much repeated knowledge acquisition. The study here attempts to quantitate and compare KB size and performance for systems built by experts with various levels of expertise and also inductively. The study also proposes a novel way of conducting such studies in that the different levels of expertise were achieved by using simulated experts. The conclusion from this study is that experts are likely to produce reasonably compact and efficient knowledge bases using the Ripple-Down Rule approach.

Ripple Down Rules (RDR) is an incremental Knowledge Acquisition (KA) technique that allows experts themselves to be in charge of performing the KA as well as the maintenance of the system. Although there are various real RDR approaches, fuzzy domain cannot be treated through RDR systems yet. The purpose of this work is to make use of the RDR advantages to construct fuzzy rule-based systems as well as to strengthen the utility of RDR in fuzzy domains. This aim has been achieved by introducing some assumptions relative to fuzzy domain modelling in combination with the construction of a new framework to manage and acquire (fuzzy) conclusions.

Conventional Knowledge-Based Systems (KBS) have no way of detecting or signalling when their knowledge is insufficient to handle a case. Consequently, these systems may produce an uninformed conclusion when presented with a case beyond their current knowledge (brittleness) which results in the KBS giving incorrect conclusions due to insufficient knowledge or ignorance on a specific case. Prudence Analysis (PA) has been shown to be a viable alternative to brittleness in Ripple Down Rules (RDR) knowledge bases. To date, there have been two approaches to Prudence; attribute-based and structural-based prudence. This paper introduces Integrated Prudence Analysis (IPA), a novel Prudence method formed by combining these methods.

Ripple Down Rules (RDR) were developed in answer to the problem of maintaining medium to large rule-based knowledge systems. Traditional approaches to knowledge-based systems gave little thought to...

A semantics-driven, fuzzy logic-based approach to knowledge representation and inference

A Knowledge Acquisition method “Ripple Down Rules” can directly acquire and encode knowledge from human experts. It is an incremental acquisition method and each new piece of knowledge is added as an exception to the existing knowledge base. There is another type of knowledge acquisition method that learns directly from data. Induction of decision tree is one such representative example. Noting that more data are stored in the database in this digital era, use of both expertise of humans and these stored data becomes even more important. Further, it is not appropriate to assume that the knowledge is stable and maintains its usefulness. Things change over time. It is not good to keep old useless knowledge in the knowledge base when such change happens. This paper attempts to integrate inductive learning and knowledge acquisition under a situation in which we can’t assume a stable environment. We show that using the minimum description length principle (MDLP), the knowledge base of Ripple Down Rules is automatically and incrementally constructed from data. We, thus, can use both human expertise and data simultaneously. When it is found that some change takes place, useless knowledge is automatically deleted based on MDLP, still keeping the consistency of knowledge base. Experiments are carefully designed and tested to verify that the proposed method indeed works for many data sets having different natures.

Ripple down rules (RDR) is an incremental knowledge acquisition (KA) methodology, where a knowledge base (KB) is constructed as a collection of rules with exceptions. Nested ripple down rules (NRDR) is an extension of this methodology which allows the expert to enter her/his own domain concepts and later refine these concepts hierarchically. In this paper we show similarities between incremental knowledge acquisition and database schema evolution, and propose to use the F2 object-oriented database management system (OODBMS) to implement an NRDR knowledge based system. We use the existing non-standard features of F2 and show how multiple instantiation and object migration (known as multiobjects feature in F2), and schema evolution capabilities in F2 easily accommodate all the update mechanisms required to incrementally build an NRDR KB. We illustrate our approach with a KA session.

Much of the research into reasoning about actions is driven by a set of small benchmark problems. For such problems, specifying action behaviours within any particular logic of action is a simple task. In more complex domains this may not be the case. A simple and proven method for eliciting knowledge from experts — called Ripple Down Rules (RDR) — can be adapted for representing action descriptions and plans.

The research aimed to develop expert system for helping lecturers to decide which courses to be taken by students in the next semester. The expert system contained specific pieces of knowledge to solve specific problems involved in the forms of system development and maintenance. This expert system was in the form of a website using the PHP programming language and MySQL database. The researcher used the Ripple Down Rules (RDR) method to identify the courses by putting forward the questions so that the system could decide which courses the students should take in next semester. The result shows that this web-based expert system can identify which courses that the students have to enroll after the students have answered questions generated by the system. The available data on the courses in the system adapt to the rules, so it is in line with the enrolled courses.

Although there is an abundance of how-to guides online, systematically utilising the collective knowledge represented in such guides has been limited. This is primarily due to how-to guides (effectively, informal process descriptions) being expressed in natural language, which complicates the process of extracting actions and data. This paper describes the use of Ripple-Down Rules (RDR) over the Stanford NLP toolkit to improve the extraction of actions and data from process descriptions in text documents. Using RDR, we can incrementally and rapidly build rules to refine the performance of the underlying extraction system. Although RDR has been widely applied, it has not so far been used with NLP phrase structure representations. We show, through implementation and evaluation, how the use of action-data extraction rules and knowledge acquisition in RDR is both feasible and effective.

Ripple down rules (RDR) are a case based reasoning technique that have been developed in answer to the need to provide a way of maintaining large KBS in domains where knowledge and practice are continually being reviewed. RDR are based on the view that knowledge is not an artifact but something that is made-up to fit a particular situation. In keeping with this, maintenance and context are key issues. RDR offer a number of features that make it particularly suitable for clinical support. These features are: knowledge is captured, changed and validated within the local context of cases; KA is simple and designed to be performed by the domain expert; and the use of cases, difference lists and the exception structure are intuitive and natural for experts. In medical domains, ownership and control of knowledge are seen as important issues and the ability to build a KBS without the need for a knowledge engineer is a real plus. The ease of developing and maintaining an RDR KBS means that it is feasible for individual pathology laboratories to customise the system to suit the local knowledge which they contain. The paper describes RDR, how they provide an environment for interacting with the knowledge according to the decision situation of the user and some results-to-date in the area of pathology.

The computer-assisted menu design still remains a difficult task. Usually knowledge that aids in menu design by a computer is hard-coded and because of that a computerised menu planner cannot handle the menu design problem for an unanticipated client. To address this problem we developed a menu design tool, MIKAS (menu construction using incremental knowledge acquisition system), an artificial intelligence system that allows the incremental development of a knowledge-base for menu design. We allow an incremental knowledge acquisition process in which the expert is only required to provide hints to the system in the context of actual problem instances during menu design using menus stored in a so-called Case Base. Our system incorporates Case-Based Reasoning (CBR), an Artificial Intelligence (AI) technique developed to mimic human problem solving behaviour. Ripple Down Rules (RDR) are a proven technique for the acquisition of classification knowledge from expert directly while they are using the system, which complement CBR in a very fruitful way. This combination allows the incremental improvement of the menu design system while it is already in routine use. We believe MIKAS allows better dietary practice by leveraging a dietitian's skills and expertise. As such MIKAS has the potential to be helpful for any institution where dietary advice is practised.

Ripple down rules (RDR)'s incremental knowledge acquisition provides computer vision applications with the ability to gradually adapt to the domain and circumvent some of its learning challenges. RDR use incremental exception-based theory revision and rely on the expert to provide the rule conditions. A computer vision expert whilst understanding their significance cannot always provide accurate rule conditions using numeric attributes. This work investigates the impact of the quasiexpertise of vision experts on the structure and performance of the acquired knowledge base. The findings provide insights into the design of features and strategies to facilitate the use of quasiexpertise for knowledge acquisition in computer vision.