Despite an improved digital access to scientific publications in the last decades, the fundamental principles of scholarly communication remain unchanged and continue to be largely document-based. The document-oriented workflows in science have reached the limits of adequacy as highlighted by recent discussions on the increasing proliferation of scientific literature, the deficiency of peer-review and the reproducibility crisis. In ScienceGRAPH we aim to develop a novel model for representing, analysing, augmenting and exploiting scholarly communication in a knowledge-based way by expressing and linking scientific contributions and related artefacts through semantically rich, interlinked knowledge graphs. The model is based on deep semantic representation of scientific contributions, their manual, crowd-sourced and automatic augmentation and finally the intuitive exploration and interaction employing question answering on the resulting ScienceGRAPH base. Currently, knowledge graphs are still confined to representing encyclopaedic, factual information. ScienceGRAPH advances the state-of-the-art by enabling to represent complex interdisciplinary scientific information including fine-grained provenance preservation, discourse capture, evolution tracing and concept drift. Also, we will demonstrate that we can synergistically combine automated extraction and augmentation techniques, with large-scale collaboration to reach an unprecedented level of knowledge graph breadth and depth. As a result, we expect a paradigm shift in the methods of academic discourse towards knowledge-based information flows, which facilitate completely new ways of search and exploration. The efficiency and effectiveness of scholarly communication will significant increase, since ambiguities are reduced, reproducibility is facilitated, redundancy is avoided, provenance and contributions can be better traced and the interconnections of research contributions are made more explicit and transparent.

Our aim here is to enhance reliability of AI in society by implementing realtime compliance mechanism for legal and ethical norms. Our contribution is to build a compliance mechanism by considering legal norms as hard constraints which must be satisfied and ethical norms as soft constraints which should be satisfied as much as possible. This combination of multiple norm compliance has not been investigated and is one of the novel parts of the project We retain realtime scalability by introducing a partial evaluation mechanism along the execution sequence of an AI agent that checks the legal norms and a speculative computation that checks ethical norms with multiple possible sequences of comparison of these soft norm. There are many researches working on offline compliance check of norms whereas there are few researches working on online compliance check. In addition, to our knowledge, these online compliance mechanisms only check the violation of the hard norms while they don’t consider soft norms violation since it needs online norm revision. We investigated here such online belief revision method in soft constraints called ‘’speculative computation’’ and we will apply this method to soft norm revision. As far as we know, this is the first attempt to formalize online norm revision. Japanese team has been long working on legal reasoning and offline compliance mechanism of legal norms and proposed a legal representation language called PROLEG (PROLOG based LEGal reasoning system). French team has been working on formalizing ethics in logic and given a rigorous framework using Event Calculus which represents temporal behavior of AI agents to represent various variations of ethical norms. German team has been working on knowledge representation issues such as aspect- oriented (metadata) knowledge modelling and reasoning with such aspect metadata scopes (scoped reasoning). They have developed tools and standards to represent rules (norms). We expect to develop a unified system of handling various norms such as legal norms and ethical norms simultaneously. Their tools and standards will be used to describe various norms. Therefore, the combination of three teams is essential to achieve our goal. Moreover, we also expect that combining legal norms and ethical norms will provoke interesting interactions between each other and it leads to new research topics to understand normative reasoning more deeply. If we succeed in this project, we expect that AI will be more reliable entity and a good partner with humans in the upcoming years.

POIs are the content of any application, service, and product even remotely related to our physical surroundings. From navigation applications, to social networks, to tourism, and logistics, we use POIs to search, communicate, decide, and plan our actions. The Big Data assets for POIs and the evolved POI value chain introduced opportunities for growth, but also complexity, intensifying the challenges relating to their quality-assured integration, enrichment, and data sharing. POI data are by nature semantically diverse and spatiotemporally evolving, representing different entities and associations depending on their geographical, temporal, and thematic context. Pioneered by the FP7 project GeoKnow, linked data technologies have been applied to effectively extract the maximum possible value from open, crowdsourced and proprietary Big Data sources. Validated in the domains of tourism and logistics, these technologies have proven their benefit as a cost-effective and scalable foundation for the quality-assured integration, enrichment, and sharing of generic-purpose geospatial data In SLIPO, we argue that linked data technologies can address the limitations, gaps and challenges of the current landscape in integrating, enriching, and sharing POI data. Our goal is to transfer the research output generated by our work in project GeoKnow, to the specific challenge of POI data, introducing validated and cost-effective innovations across their value chain