The aggregation of heterogeneous data from different institutions in cultural heritage and e-science has the potential to create rich data resources useful for a range of different purposes, from research to education and public interests. In this paper, we present the X3ML framework, a framework for information integration that handles effectively and efficiently the steps involved in schema mapping, uniform resource identifier (URI) definition and generation, data transformation, provision and aggregation. The framework is based on the X3ML mapping definition language for describing both schema mappings and URI generation policies and has a lot of advantages when compared with other relevant frameworks. We describe the architecture of the framework as well as details on the various available components. Usability aspects are discussed and performance metrics are demonstrated. The high impact of our work is verified via the increasing number of international projects that adopt and use this framework.

Purpose: – Marine species data are scattered across a series of heterogeneous repositories and information systems. There is no repository that can claim to have all marine species data. Moreover, information on marine species are made available through different formats and protocols. The purpose of this paper is to provide models and methods that allow integrating such information either for publishing it, browsing it or querying it. Aiming at providing a valid and reliable knowledge ground for enabling semantic interoperability of marine species data, in this paper the authors motivate a top level ontology, called MarineTLO and discuss its use for creating MarineTLO-based warehouses. Design/methodology/approach: – In this paper the authors introduce a set of motivating scenarios that highlight the need of having a top level ontology. Afterwards the authors describe the main data sources (Fisheries Linked Open Data, ECOSCOPE, WoRMS, FishBase and DBpedia) that will be used as a basis for constructing the MarineTLO. Findings: – The paper discusses about the exploitation of MarineTLO for the construction of a warehouse. Furthermore a series of uses of the MarineTLO-based warehouse is being reported. Originality/value: – In this paper the authors described the design of a top level ontology for the marine domain able to satisfy the need for maintaining integrated sets of facts about marine species and thus assisting ongoing research on biodiversity. Apart from the ontology the authors also elaborated with the mappings that are required for building integrated warehouses.

A roadmap is proposed that defines a systematic approach for craft preservation and its evaluation. The proposed roadmap aims to deepen craft understanding so that blueprints of appropriate tools that support craft documentation, education, and training can be designed while achieving preservation through the stimulation and diversification of practitioner income. In addition to this roadmap, an evaluation strategy is proposed to validate the efficacy of the developed results and provide a benchmark for the efficacy of craft preservation approaches. The proposed contribution aims at the catalyzation of craft education and training with digital aids, widening access and engagement to crafts, economizing learning, increasing exercisability, and relaxing remoteness constraints in craft learning.

The CIDOC Conceptual Reference Model (CIDOC-CRM) is an ISO Standard ontology for the cultural domain that is used for enabling semantic interoperability between museums, libraries, archives and other cultural institutions. For leveraging CIDOC-CRM, several processes and tasks have to be carried out. It is therefore important to investigate to what extent we can automate these processes in order to facilitate interoperability. For this reason, in this paper, we describe the related tasks, and we survey recent works that apply machine learning (ML) techniques for reducing the costs related to CIDOC-CRM-based compliance and interoperability. In particular, we (a) analyze the main processes and tasks, (b) identify tasks where the recent advances of ML (including Deep Learning) would be beneficial, (c) identify cases where ML has been applied (and the results are successful/promising) and (d) suggest tasks that can benefit from applying ML. Finally, since the approaches that leverage both CIDOC-CRM data and ML are few in number, (e) we introduce our vision for the given topic, and (f) we provide a list of open CIDOC-CRM datasets that can be potentially used for ML tasks.