Enabling Case-Based Reasoning on the Web of Data (How to create a Web of Experience)


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Presentation at the "Reasoning from experiences on the Web" workshop (WebCBR 2010) at the International Conference on Case Based Reasoning 2010.

While Case-based reasoning (CBR) has successfully been deployed on the Web, its data models are typically inconsistent with existing information infrastructure and standards. In this paper, we examine how
CBR can operate on the emerging Web of Data, with mutual benefits. The
expense of knowledge engineering and curating a case base can be reduced
by using Linked Data from the Web of Data. While Linked Data provides experiential data from many different domains, it also contains inconsistencies, missing data and noise which provide challenges for logic-based reasoning. CBR is well suited to provide alternative and robust reasoning approaches. We introduce (i) a lightweight CBR vocabulary which is
suited for the open ecosystem of the emerging Web of Data, and provide
(ii) a detailed example of a case base using data from multiple sources. We
propose that for the first time the Web of Data provides data and a real
context for open CBR systems.

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  • Today I want to speak about a new source of experiential data: the Web of Data.
    This Web of Data is characterised by the links between different data sources.
    Some of these sources provide experiential data, which is linked to domain ontologies
    and background information from other domains, all provided by 3rd parties / not the original source.
    This provides data which can be viewed as cases which are interconnected between each other
    and which in part reuse existing case fragments, in line with the definition of case bases by Redmond.

  • 1.) the history of CBR and how it has created an unfulfilled potential to apply CBR to the Web,
    as a decentralised reasoning paradigm
    2.) the promise of the Web of Data to fulfill the potential of CBR to become a mainstream reasoning paradigm,
    due to the intrinsic links in Linked Data and the noise nature of the data which makes it difficult to apply rule based reasoning
    3.) a discussion of the concpetial/technical details behind the Web of Data,
    and the presentation of an example which shows how the CBR view can be applied to the WoD

  • use a running example such as Rome or Beck.

  • Types of knowledge containers: vocabulary/case/similarity/adaptation
    Explanation of vocabulary:
    * we define cbr:Case and cbr:Solution to allow differention between query
    and potential solutions. Resource can be instance of both at same time.
    * there is no class of problems, following early CBR research about the need to discover
    which parts of a case are required for the reasoning at run-time
    * the properties induce the classes of theur subject and object (e.g. case and solution )
    * cases are associated to case-bases, which allows maintainance of case bases

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