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Abstract. In this paper we describe some results of the Alice project. Alice is an ontology based e-commerce project which aims to support online users in the task of shopping. Ontologies describing customers, products, typical shopping tasks and the external context form the basis for the Alice architecture. We also build upon two novel interface metaphors originally developed for navigating databases: the Guides metaphor and Dynamic Queries. The Guides metaphor was developed at Apple to reduce the cognitive load on learners navigating a large hypermedia database. Within Alice we use the Guides metaphor to allow online shoppers to interact with the system in a variety of ways. In effect, by choosing these options they are classifying themselves for the purpose of customizing system responses. We discuss the link between Alice Guides and Kozinets' notion of e-tribes or Virtual Communities of Consumption. Our second interface metaphor Dynamic Queries (coupled with Starfield displays) allows users to very quickly find relevant items by displaying the results of queries, posed via specialised slider widgets, within 100 milliseconds. We have constructed a tool, Quiver, which constructs Dynamic Query interfaces on-the-fly as the result of queries to knowledge models stored on the Alice server.

This paper addresses the issue of grounding spatial relations in natural language for human-robot interaction and robot control. The problem is approached by identifying two set of spatial relations, the image space-based and object-centered, and expressing them as fuzzy sets to capture the ambiguity inherent to the linguistic expressions for the relations. The sizes and shades of the scene objects have also been modeled as fuzzy sets for conditioning the spatial relations. To verify the validity of our approach and test its feasibility in a natural language-based interface, we have considered the typical scenarios of using the spatial relations in simple declarative and imperative sentences and designed simple grammars for parsing such sentences. Our experiment has shown that fuzzy spatial relation analysis provides a useful way for modeling the ambiguity or imprecision of the natural language in describing spatial relations and that it is possible to use the spatial relation models to support robot control and human-robot interaction in a natural language-based interface.

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NEANIAS is a research and innovation action project funded by the European Union under the Horizon 2020 program. The project addresses the challenge of prototyping novel solutions for the underwater, atmospheric and space research communities, creating a collaborative research ecosystem, and contributing to the effective materialization of the European Open Science Cloud (EOSC). NEANIAS drives the co-design, implementation, delivery, and integration into EOSC of innovative thematic and core services, derived from state-of-the-art assets and practices in the target scientific communities. We present the overall NEANIAS ecosystem architecture, with an emphasis on its core visualization services, detailing their specifications and software development plan, and focusing on the underpinning service-oriented architecture for their delivery. We report on the underlying ideas and guiding principles for designing such visualization services, outlining their current release status and future development roadmaps towards Technological Readiness Level (TRL) 8 maturity and EOSC integration.