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.

Increasingly, it has been hard to ignore that standalone KOSs are becoming functionally ineffective components for knowledge-based AI systems, given their inability to capture the continuous facetization and drift of domains. The paper proposes a radical re-conceptualization of KOSs as a first step to solve such an inability, and proposes: (i) a first theoretical characterization of Knowledge Organization Ecosystems (KOEs), (ii) as a consequence of such a novel characterization, a first examination and characterization of KOEs as Socio-Technical Systems (STSs), and (iii) motivating KOEs to be STSs which are grounded in AI ethics cardinals.

Recent work in Machine Learning and Computer Vision has highlighted the presence of various types of systematic flaws inside ground truth object recognition benchmark datasets. Our basic tenet is that these flaws are rooted in the many-to-many mappings which exist between the visual information encoded in images and the intended semantics of the labels annotating them. The net consequence is that the current annotation process is largely underspecified, thus leaving too much freedom to the subjective judgment of annotators. In this paper, we propose vTelos, an integrated Natural Language Processing, Knowledge Representation, and Computer Vision methodology whose main goal is to make explicit the (otherwise implicit) intended annotation semantics, thus minimizing the number and role of subjective choices. A key element of vTelos is the exploitation of the WordNet lexico-semantic hierarchy as the main means for providing the meaning of natural language labels and, as a consequence, for driving the annotation of images based on the objects and the visual properties they depict. The methodology is validated on images populating a subset of the ImageNet hierarchy.

Knowledge Organization (KO) and Knowledge Representation (KR) have been the two mainstream methodologies of knowledge modelling in the Information Science community and the Artificial Intelligence community, respectively. The facet-analytical tradition of KO has developed an exhaustive set of guiding canons for ensuring quality in organising and managing knowledge but has remained limited in terms of technology-driven activities to expand its scope and services beyond the bibliographic universe of knowledge. KR, on the other hand, boasts of a robust ecosystem of technologies and technology-driven service design which can be tailored to model any entity or scale to any service in the entire universe of knowledge. This paper elucidates both the facet-analytical KO and KR methodologies in detail and provides a functional mapping between them. Out of the mapping, the paper proposes an integrated KR-enriched KO methodology with all the standard components of a KO methodology plus the advanced technologies provided by the KR approach. The practical benefits of the methodological integration has been exemplified through the flagship application of the Digital University at the University of Trento, Italy.