29 research outputs found

    Knowledge modelling with the open source tool myCBR

    Get PDF
    Building knowledge intensive Case-Based Reasoning applications requires tools that support this on-going process between domain experts and knowledge engineers. In this paper we will introduce how the open source tool myCBR 3 allows for flexible knowledge elicitation and formalisation form CBR and non CBR experts. We detail on myCBR 3 's versatile approach to similarity modelling and will give an overview of the Knowledge Engineering workbench, providing the tools for the modelling process. We underline our presentation with three case studies of knowledge modelling for technical diagnosis and recommendation systems using myCBR 3

    Integration of linked open data in case-based reasoning systems

    Get PDF
    This paper discusses the opportunities of integrating Linked Open Data (LOD) resources into Case-Based Reasoning (CBR) systems. Upon the application domain travel medicine, we will exemplify how LOD can be used to fill three out of four knowledge containers a CBR system is based on. The paper also presents the applied techniques for the realization and demonstrates the performance gain of knowledge acquisition by the use of LOD

    Similarity Measure Development for Case-Based Reasoning- A Data-driven Approach

    Full text link
    In this paper, we demonstrate a data-driven methodology for modelling the local similarity measures of various attributes in a dataset. We analyse the spread in the numerical attributes and estimate their distribution using polynomial function to showcase an approach for deriving strong initial value ranges of numerical attributes and use a non-overlapping distribution for categorical attributes such that the entire similarity range [0,1] is utilized. We use an open source dataset for demonstrating modelling and development of the similarity measures and will present a case-based reasoning (CBR) system that can be used to search for the most relevant similar cases

    Building case-based reasoning applications with myCBR and COLIBRI Studio

    Get PDF
    myCBR and COLIBRI Studio are two well-established opensource frameworks for building case-based reasoning (CBR) applications, though they follow different approaches and support different phases of the CBR application development. In a nutshell: Where myCBR supports its users in developing a knowledge model for representing cases, it more or less leaves the software developers alone when they try to develop an application that uses the generated knowledge model. COLIBRI Studio, on the other hand, is focused in the development of applications that use that knowledge model. As soon as you have a knowledge model COLIBRI Studio offers templates for a variety of application types and supports in generating its source code. This paper explains the strengths and weaknesses of both frameworks regarding the rapid development of CBR applications. It also shows how to use both of them in conjunction

    Approaches to the use of sensor data to improve classroom experience

    Get PDF
    quipping classrooms with inexpensive sensors can enable students and teachers with the opportunity to interact with the classroom in a smart way. In this paper an approach to acquiring contextual data from a classroom environment, using inexpensive sensors, is presented. We present our approach to formalising the usage data. Further we demonstrate how the data was used to model specific room usage situation as cases in a Case-based reasoning (CBR) system. The room usage data was than integrated in a room recommendations system, reasoning on the formalised usage data. We also detail on our on-going work to integrating the systems presented in this paper into our Smart University vision

    Case acquisition from text: ontology-based information extraction with SCOOBIE for myCBR

    Get PDF
    myCBR is a freely available tool for rapid prototyping of similarity-based retrieval applications such as case-based product recommender systems. It provides easy-to-use model generation, data import, similarity modelling, explanation, and testing functionality together with comfortable graphical user interfaces. SCOOBIE is an ontology-based information extraction system, which uses symbolic background knowledge for extracting information from text. Extraction results depend on existing knowledge fragments. In this paper we show how to use SCOOBIE for generating cases from texts. More concrete we use ontologies of the Web of Data, published as so called Linked Data interlinked with myCBR’s case model. We present a way of formalising a case model as Linked Data ready ontology and connect it with other ontologies of the Web of Data in order to get richer cases

    Implementation of similarity measures for event sequences in myCBR

    Get PDF
    The computation of the similarities between event sequences is important for many fields because many activities follow a sequential order. For instance, an industrial plan that triggers different types of alarms due to detected event sequences or the treatment sequence that a patient receives while he/she is hospitalized. With the appropriate tools and techniques to compute the similarity between two event sequences we may be able to detect patterns or regularities in event data and so be able to perform predictions or recommendations based on detected similar sequences. The present work is intended to describe the implementation of two event sequence similarity measures in myCBR, with the purpose of creating a similarity measurement approach for complex domains that employ the use of event sequences. Besides, an initial experimentation is performed in order to study if the proposed measures and measurement approach are able to predict future situations based on similar event sequences

    Clood CBR: towards microservices oriented case-based reasoning.

    Get PDF
    CBR applications have been deployed in a wide range of sectors, from pharmaceuticals; to defence and aerospace to IoT and transportation, to poetry and music generation; for example. However, a majority of these have been built using monolithic architectures which impose size and complexity constraints. As such these applications have a barrier to adopting new technologies and remain prohibitively expensive in both time and cost because changes in frameworks or languages affect the application directly. To address this challenge, we introduce a distributed and highly scalable generic CBR system, Clood, which is based on a microservices architecture. This splits the application into a set of smaller, interconnected services that scale to meet varying demands. Experimental results show that our Clood implementation retrieves cases at a fairly consistent rate as the casebase grows by several orders of magnitude and was over 3,700 times faster than a comparable monolithic CBR system when retrieving from half a million cases. Microservices are cloud-native architectures and with the rapid increase in cloud-computing adoption, it is timely for the CBR community to have access to such a framework

    myEACBR - myCBR as explanation-aware Protégé plugin

    Get PDF
    Explanation, trust, and transparency are concepts that are strongly tied in with users' confidence in, and acceptance of computerised systems. Case-based reasoning (CBR) systems lend themselves easily to generate explanations, as they typically organise and represent knowledge in a way that makes it possible to reason about and thereby generate explanations. The work presented here is a first step towards making a CBR engine explanation-aware. We demonstrate how a plugin for Protégé and myCBR can facilitate explanations for the retrieval phase of a CBR system
    corecore