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ENTICE aimed to use co-creative methodologies in order to build a solid creation pipeline for medical experiential content. The project has developed and evaluated immersive learning resources and tools aiming to support well-defined learning objectives using tangible and intangible resources (AR/VR/MR, 3D printing) that are highly sought in the fields of anatomy and surgery. In this paper the preliminary results from the evaluation of the learning resources and tools in 3 countries as well as the lessons learnt are presented towards to the improvement of the medical education process.

Curriculum size constantly challenges healthcare education and training. Technology enhanced, immersive educational content like Virtual, Augmented, or Mixed Reality (VR/AR/MR collectively XR) constantly aims to facilitate knowledge retention and skills acquisition in the healthcare sector. Core challenge in this effort is the increasing costs, in time and resources, required for designing and developing XR immersive educational content. An approach to address this challenge is participatory design methods. Co-creation approaches distribute the burden of content development amongst the educators' community and facilitate decentralized bottoms-up content creation. This approach requires data modeling approaches that facilitate digital asset discoverability, reusability and consumption through visual authoring tools. This work describes the conceptualization and implementation of a UX taxonomy for annotating immersive AR/VR/MR content at the asset level for maximum repurposing capacity. A brainstorming session between educational and technology experts was conducted and conceptual details of the terms of the taxonomy were described. The Simple Knowledge Organization System (SKOS) vocabulary was utilized to organize this devised taxonomy and link it with existing medical ontology terms, formulating an RDF endpoint of the nominated ENTICE ontology. This ontology was able to link medical terms with UX and educational properties in a data structure that can annotate and contextually encapsulate any XR digital asset. An example such term is described and presented as proof of application. The semantic modelling implemented in this work is directly applicable to a previously proposed visual data structure and subsequent authoring environment that could facilitate XR resource design and authoring from non-technical experts.

Ontology matching is an essential problem in the world of Semantic Web and other distributed, open world applications. Heterogeneity occurs as a result of diversity in tools, knowledge, habits, language, interests and usually the level of detail. Automated applications have been developed, implementing diverse aligning techniques and similarity measures, with outstanding performance. However, there are use cases where automated linking fails and there must be involvement of the human factor in order to create, or not create, a link. In this paper we present Alignment, a collaborative, system aided, interactive ontology matching platform. Alignment offers a user-friendly environment for matching two ontologies with the aid of configurable similarity algorithms.

Sports analysis tends to be more advanced and sophisticated in the last years, focusing on very detailed aspects of the game in order to result groundbreaking insights. New terms and expressions are shown up, corresponding to novel concepts and elements of sports. The diversity of terms used from various competitions and the media leads to a complexity for automated methods that aim to exploit textual sports sources for their analysis. This work searches, extracts and collects all the distinct terms that are used by major basketball competitions websites, via NLP techniques to create a generic classification of concepts related to basketball actions. The generated competitions vocabularies, as well as the generic vocabulary are transformed into SKOS hierarchical classification schemes and then linked to each other through their similar terms that correspond to the same actions. In this way, the generic basketball actions classification includes all the knowledge from the leagues vocabularies, the concepts and the synonyms from each competition in a hierarchical structure, forming a key device of comparability and aggregation. The use of the unified sports concepts classification could benefit the automation of multidimensional sports analysis through various competitions, years and media resources.

This paper describes the process of the construction of a scientific knowledge graph via semantic annotation and linking of academic research fields. The method aims to combine the broad scope of generic classifications of research areas with the specialized knowledge and domain fields of specific scientific classifications in order to build a unified scientific knowledge graph including all the research fields of the respective scientific areas in a common hierarchy. First, a survey of scientific classifications has been conducted in order to identify the ones that are the most common and complete for the knowledge graph to build upon. Classifications from different research domains have been used to semantically annotate their thematic topics and fields. The various scientific fields are connected based on their similarity, enlightening and creating, in this way, cross-domain research fields. A core scientific graph containing main fields of science with their relations to domain specific vocabularies and classifications has been created. This knowledge graph can be used to retrieve specific scientific fields in a related, broader or narrower, research area. Finally, a use case leveraging the semantic features of the graph is presented, indicating its usefulness for research activities and PhD web services.

Ontology matching is a crucial problem in the world of Semantic Web and other distributed, open world applications. Diversity in tools, knowledge, habits, language, interests and usually level of detail may drive in heterogeneity. Thus, many automated applications have been developed, implementing a large variety of matching techniques and similarity measures, with impressive results. However, there are situations where this is not enough and there must be human decision in order to create a link. In this paper we present Alignment, a collaborative, system aided, interactive ontology matching platform. Alignment offers a simple GUI environment for matching two ontologies with aid of configurable similarity algorithms.

OpenSpending is the world's largest repository of open fiscal datasets. As of now it only supported loading datasets from CSV files, leaving out more heavily-structured formats like RDF. Rudolf, an RDF backend has been developed to expose Data Cube RDF triples and OLAP operations in JSON format through an OpenSpending-compatible HTTP API. This API can be directly utilized by the OpenSpending Viewer application to transparently offer aggregate visualizations for RDF fiscal datasets out of the box.

Today, there is a growing need within citizens, journalists and many public bodies to explore and search for valuable information and knowledge in budget data, that are widely published by governments and municipalities across Europe. A significant element of these fiscal datasets are code lists, which serve not only for the coding and the simplicity of representation of budget concepts, but can also act as linking entities between budget datasets of different countries, bodies, years and structure, in order to facilitate their comparison. To this end, we made an extensive survey of code lists that refer or are related to fiscal or other concepts that are included in budget datasets, recording totally 239 international and national classifications. We selected some of these classifications to represent them semantically with SKOS and then we examined their linking possibilities. While manual and automated methods are insufficient for linking large code lists, tools using semi-automated methods, like Alignment seem to be more suitable for this specific task.