Abstract. This paper presents a model for representing compliance rules related to subsurface objects. Rules expressed in this model can be automatically evaluated (using SHACL or SPARQL) on existing 3D city models expressed in RDF. The main characteristics of the proposed model are (1) its expressiveness, that comes from the use of formal ontologies for representing the rules and the objects they refer to, (2) its integrative nature, given by the interconnection among the proposed ontologies and the connection of these ontologies with CityGML and IFC (in an ontological form), and (3) its multi-geometry aspect. Preliminary results allow to automatically evaluate formally expressed compliance rules for underground objects in a 3D city model, that will considerably ease the task of professionals of the field.

Service self-composition is a well-understood research area focusing on service-based applications providing new services by automatically combining pre-existing ones. In this paper we focus on tuplebased coordination, and propose a solution leveraging logic tuples and tuple spaces to support semantic self-composition for services. A fullstack description of the solution is provided, ranging from a theoretical formalisation to a technologically valuable design and implementation.

Forecasts announce that the number of connected objects will exceed 20 billion by 2025. Objects, such as sensors, drones or autonomous cars participate in pervasive applications of various domains ranging from smart cities, quality of life, transportation, energy, business or entertainment. These inter-connected devices provide storage, computing and activation capabilities currently under-exploited. To this end, we defined “Spatial services”, a new generation of services seamlessly supporting users in their everyday life by providing information or specific actions. Spatial services leverage IoT, exploit devices capabilities (sensing, acting), the data they locally store at different time and geographic locations, and arise from the spontaneous interactions among those devices. Thanks to a learning-based coordination model, and without any pre-designed composition, reliable and pertinent spatial services dynamically and fully automatically arise from the self-composition of available services provided by connected devices. In this paper, we show how we extended our learning-based coordination model with semantic matching, enhancing syntactic self-composition with semantic reasoning. The implementation of our coordination model results in a learning-based semantic middleware. We validated our approach on various experiments: deployments of the middleware in various settings; instantiation of a specific scenario and various other case studies; experiments with hundreds of synthetic services; and specific experiments for setting up key learning parameters. We also show how the learning-based coordination model using semantic matching favours service composition, by exploiting three ontological constructions (is-a, isComposedOf, and equivalentTo), de facto removing the syntactic barrier preventing pertinent compositions to arise. Spatial services arise from the interactions of various objects, provide complex and highly adaptive services to users in seamless way, and are pertinent in a variety of domains such as smart cities or emergency situations.

Monitoring University students’ progress, in an interactive, synchronised, and coordinated way across a given study program is a challenge. Indeed, for a given student journey, University actors need a service offering personalised views and actions depending on their role (students, study advisors, scientific committee, faculty members, admission office), all linked to the same underlying information system, and abiding study regulations. The challenge is even greater when it comes to scaling up and providing such service for the whole University community (approx. 23K members in Geneva). Following a service science approach, we derived a method for automatically generating a service, compliant by design to study regulations, and offering personalised views to the various actors. The automatic implementation of the study regulations is based on the definition of generic rules able to define the different elements of the regulations that need to be respected across all curricula and programs. We provide a case study based on the PhD students regulations. We describe a proof-of-concept instantiating the service from the rules, role-centric views of the interface, as well as the underlying architecture relying on a semantic reasoning engine. The work presented in this paper has the potential to alleviate and improve the tasks of all the various actors involved in students monitoring, going beyond PhD students. This approach automatises the implementations of any study program and can be applied to any University.

Abstract. In the recent years the concept of knowledge graph has emerged as a way to aggregate information from various sources without imposing too strict data modelling constraints. Several graph models have been proposed during the years, ranging from the “standard” RDF to more expressive ones, such as Neo4J and RDF-star. The adoption of knowledge graph has become established in several domains. It is for instance the case of the 3D geoinformation domain, where the adoption of semantic web technologies has led to several works in data integration and publishing. However, yet there is not a well-defined model or technique to represent 3D geoinformation including uncertainty and time variation in knowledge graphs. In this paper we propose a model to represent parameterized geometries of subsurface objects. The vocabulary of the model has been defined as an OWL ontology and it extends existing ontologies by adding classes and properties to represent the uncertainty and the spatio-temporal behaviour of a geometry, as well as additional attributes, such as the data provenance. The model has been validated on significant use cases showing different types of uncertainties on 3D subsurface objects. A possible implementation is also presented, using RDF-star for the data representation.