Conversational software, or chatbots in popular parlance, has been in vogue amongst organizations for a few decades now. In sync with the trend, various libraries and knowledge resource centres have also adopted them within their technological fold, with an aim to provide improved services to patrons. The only bottleneck towards such implementation has been the dearth of open source conversational software platforms. The purpose of the paper is to conceptualize a library chatbot using a recently developed, artificial intelligence-powered open source conversational software platform named Rasa Stack. It introduces the essence of chatbot technology and their present day application in libraries, illustrates how the technical underpinnings of Rasa Stack can be leveraged to develop a library chatbot, and reflects on the potential future research in this direction.
We propose a novel approach to the problem of semantic heterogeneity where data are organized into a set of stratified and independent representation layers, namely: conceptual (where a set of unique alinguistic identifiers are connected inside a graph codifying their meaning), language (where sets of synonyms, possibly from multiple languages, annotate concepts), knowledge (in the form of a graph where nodes are entity types and links are properties), and data (in the form of a graph of entities populating the previous knowledge graph). This allows us to state the problem of semantic heterogeneity as a problem of Representation Diversity where the different types of heterogeneity, viz. Conceptual, Language, Knowledge, and Data, are uniformly dealt within each single layer, independently from the others. In this paper we describe the proposed stratified representation of data and the process by which data are first transformed into the target representation, then suitably integrated and then, finally, presented to the user in her preferred format. The proposed framework has been evaluated in various pilot case studies and in a number of industrial data integration problems.
Since time immemorial, organization and visualization has emerged as the pre-eminent natural combination through which abstract concepts in a domain can be understood, imbibed and communicated. In the present era of big data and information explosion, domains are becoming increasingly intricate and facetized, often leaving traditional approaches of knowledge organization functionally inefficient in dynamically depicting intellectual landscapes. The paper attempts to present, ab initio, a step-by-step conceptual domain development methodology using knowledge graphs, rooted in the rudiments of interdisciplinary knowledge organization and knowledge cartography. It briefly highlights the implementation of the proposed methodology on business domain data, and considers its research ramifications, originality and limitations from multiple perspectives. The paper concludes by summarizing observations on the entire work and particularizing future lines of research.
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