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Data enrichment facilitates the creation of rich, expressive, and high-quality datasets, enabling valuable analytics and enhanced decision-making. The accurate geolocation of residential addresses and travel routes is crucial for determining the most appropriate locations of critical social infrastructure, such as educational and medical centres. This paper introduces an interactive semantic enrichment approach that enhances urban data by integrating high-quality geospatial information. The approach is supported by a modular and extensible data enrichment framework, which leverages existing geolocation services, enabling seamless data integration. Human-in-the-loop revision is employed to enhance the quality of geocoding results. A real-world pilot study conducted in Sofia, Bulgaria, was used to validate this approach, demonstrating its promising potential in addressing pressing issues in parametric urban planning.

Designing interactive prototypes involves multiple tools and skills. In addition, several design cycles are required to iterate through idea generation, evaluation of design alternatives, and development. Consequently, prototyping tools should offer flexibility and adaptability to allow designers to quickly test and evaluate different ideas, design alternatives, materials, interactions, etc. To meet these requirements, we designed Protobject - a rapid prototyping tool aimed at making the early stages of prototyping interactive products more flexible. Protobject allows designers to reinvent and reuse existing objects for prototyping purposes by making them interactive. After introducing the features of Protobject and discussing the differences with similar tools, we present a user evaluation through two workshop sessions held in Milan during Brera Design Days and attended by 22 people. The results suggest that Protobject facilitates cooperation between people with different skills by allowing them to envision interactive prototypes together.

When information available in unstructured or semi-structured formats, e.g., tables or texts, comes in, finding links between strings appearing in these sources and the entities they refer to in some background Knowledge Graphs (KGs) is a key step to integrate, enrich and extend the data and/or KGs. This Entity Linking task is usually decomposed into Entity Retrieval and Entity Disambiguation because of the large entity search space. This paper presents an Entity Retrieval service (LamAPI) and discusses the impact of different retrieval configurations, i.e., query and filtering strategies, on the retrieval of entities. The approach is to augment the search activity with extra information, like types, associated with the strings in the original datasets. The results have been empirically validated against public datasets.

Annotating tables with high-quality semantic labels and linking cells in these tables to entities in reference knowledge graphs (KGs) serve several downstream applications such as knowledge graph construction, data enrichment, data analytics, and so on. There is an increasing interest in combining algorithms for the automatic annotation of tables with interactive applications that let users control and improve the annotations and use them for data enrichment. In this paper, we propose a supervised entity linking network that behaves as a re-ranker and score normalizer of scores returned by off-the-shelves entity retrieval systems. By combining these refined scores with additional signals of reliability of the ranking obtained thereof, we can estimate a confidence score, which tells how reliable is the decision to link a cell to the top-ranked candidate entity. The confidence score is used to decide whether to establish a link or not and to prioritize the cells to be revised by the users to improve the annotation quality. Experiments suggest that the proposed approach provides an effective approach to identifying most critical decisions and supporting link revision within a human-in-the-Ioop data enrichment paradigm.

Introduction: The COVID-19 pandemic has led to the disruption of surgical training. Lack of communication, guidelines for managing clinical activity as well as concerns for safety in the workplace appeared to be relevant issues. This study aims to investigate how surgical training has been reorganized in Italy, almost 2 years after the outbreak of COVID-19 pandemic. Materials and methods: A 16-item-electronic anonymous questionnaire was designed through SurveyMonkey© web application. This survey was composed of different sections concerning demographic characteristics and impacts of the second COVID-19 pandemic wave on surgical and research/didactic activities. Changes applied in the training programme and activities carried out were also investigated. The survey was carried out in the period between June and October 2021. Results: Four hundred and thirty responses were collected, and 399 were considered eligible to be included in the study analysis. Three hundred and thirty-five respondents continued working in Surgical Units, with a significant reduction (less than one surgical session per week) of surgical sessions in 49.6% of them. With concern to didactic and research activities, 140 residents maintained their usual activity, while 116 reported a reduction. A sub-group analysis on resident moved to COVID-19 departments showed a reduction of research activities in 35% of them. During the period considered in this survey, the surgical training program was not substantially modified for most of participants (74.6%). Conclusion: Our survey demonstrated that surgical residency programs haven't improved 2 years after the beginning of the pandemic. Further improvements are needed to guarantee completeness of surgical training, even in emergency conditions.

In recent years, there has been an increasing interest in extracting and annotating tables on the Web. This activity allows the transformation of text data into machine-readable formats to enable the execution of various artificial intelligence tasks, e.g. semantic search and dataset extension. Semantic Table Interpretation is the process of annotating elements in a table. Current approaches are mainly based on lexical matching algorithms that rely on metadata associated with tables or custom Knowledge Graphs. Their main limitations are due to the lack of metadata, the little use of contextual semantics, and the incompleteness of the proposed methods that do not include all the necessary steps. In this paper, we propose a comprehensive approach and a tool that provides an unsupervised method to annotate independent tables, possibly without header row or other external information. The approach is based on the definition of a context created from the elements within the table in order to discriminate among matching entities found in shared Knowledge Graphs and create high-quality annotations. The approach has achieved excellent results in an international challenge, thus proving its effectiveness.

Data enrichment is a critical task in the data preparation process in which a dataset is extended with additional information from various sources to perform analyses or add meaningful context. Facilitating the enrichment process design for data workers and supporting its execution on large datasets are only supported to a limited extent by existing solutions. Harnessing semantics at scale can be a crucial factor in effectively addressing this challenge. This chapter presents a comprehensive approach covering both design- and run-time aspects of tabular data enrichment and discusses our experience in making this process scalable. We illustrate how data enrichment steps of a Big Data pipeline can be implemented via tabular transformations exploiting semantic table annotation methods and discuss techniques devised to support the enactment of the resulting process on large tabular datasets. Furthermore, we present results from experimental evaluations in which we tested the scalability and run-time efficiency of the proposed cloud-based approach, enriching massive datasets with promising performance.

Recent developments in data analysis and machine learning support novel data-driven operations. Event data provide social and environmental context, thus, such data may become essential for the workflow of data analytic pipelines. In this paper, we introduce our Business Event Exchange Ontology (BEEO), based on Schema.org that enables data integration and analytics for event data. BEEO is available under Apache 2.0 license on GitHub, and is seeing adoption among both its creator companies and other product and service companies. We present and discuss the ontology development drivers and process, its structure, and its usage in different real use cases.

EW-Shopp is an innovation project, the aim of which is to build a platform for support of data linking, integration, and analytics in companies from the e-commerce, retail, and marketing industries. The project consortium joins several business partners from different sectors of e-commerce including marketing, price comparison, and both web and brick-and-mortar stores. The project is developing several pilot services to test the platform and inform its further development.

Citizens managing life events are often challenged in finding the needed municipality services (MuSe) and understanding the specific delivery conditions of such services. The lack of a comprehensive view of available services on municipality portals and online systems makes the challenge harder. In this paper, we describe the MuSe ontology, developed in the context of an Italian project that: i) supports the creation of formal and machinereadable descriptions of services available to citizens; ii) enables for personalised recommendations of services based on citizen profiles. Moreover, the paper describes how such recommendations are defined by means of an innovative semantic solution for supply demand matchmaking and how such recommendations are shown to citizens.

Digital marketing is a domain where data analytics are a key factor to gaining competitive advantage and return of investment for companies running and monetizing digital marketing campaigns on, e.g., search engines and social media. In this paper, we propose an end-to-end approach to enrich marketing campaigns performance data with third-party event data (e.g., weather events data) and to analyze the enriched data in order to predict the effect of such events on campaigns’ performance, with the final goal of enabling advanced optimization of the impact of digital marketing campaigns. The use of semantic technologies is central to the proposed approach: event data are made available in a format more amenable to enrichment and analytics, and the actual data enrichment technique is based on semantic data reconciliation. The enriched data are represented as Linked Data and managed in a NoSQL database to enable processing of large amounts of data. We report on the development of a pilot to build a weather-aware digital marketing campaign scheduler for JOT Internet Media—a world leading company in the digital marketing domain that has amassed a huge amount of data on campaigns performance over the years—which predicts the best date and region to launch a marketing campaign within a seven-day timespan. Additionally, we discuss benefits and limitations of applying semantic technologies to deliver better optimization strategies and competitive advantage.

This paper describes MantisTable, an open source Semantic Table Interpretation tool, which automatically annotates tables using a Knowledge Graph. MantisTable provides a graphical interface allowing users to analyse the results of the semantic table interpretation process and validate the final annotations. The tool also provides a guided mode for viewing and editing annotations by users. Thanks to MantisTable features, it is possible to create semantic annotations and favour the publication and exchange of tabular data.

Enriching datasets with additional information to build robust models is an essential task in many data science applications. Also, the huge availability of Linked Data encourages to reuse and integrate such high-quality information. The ASIA tool assists users in annotating tabular data both at schema- and instance-level, in such a way to enable data extension. This demo paper presents its core capabilities.

This paper presents a subsystem of a comprehensive platform dedicated to data transformation, linking and extension of large data sets. Furthermore, we detail and discuss both the main requirements that have led to the design and development of the platform, and the devised approach, which is a direct outcome of the requirement elicitation and discussion phase. In particular, the platform supports both design and run time aspects of the data transformation process, which is reflected in the architecture. Some initial tests have been carried out on a prototype implementation of our architecture on data sets of ~1TB featuring promising performance.

As Linked Data available on the Web continue to grow, understanding their structure and content remains a challenging task making such the bottleneck for their reuse. ABSTAT is an online profiling tool which helps data consumers in better understanding the data by extracting ontology-driven patterns and statistics about the data. This demo paper presents the capabilities of the new added feature of ABSTAT.

Services composition has been much investigated over the last decade without reaching shared and consolidated results mainly for the lack of interoperable descriptions of services and the consequent need of extensive user intervention. In this paper, we propose a light and practical approach to create machine-readable descriptions of output data that can be merged or used (as-is or adapted) as input data to other services. The solution relies on the popular and standard OpenAPI descriptions augmented with annotations based on JSON-LD format. Services descriptions are created by table annotations techniques applied on sets of given or retrieved output values. The approach has been implemented in a tool and validated with a set of real services.

Cloud computing and Internet of Things are imposing a dramatic change in software development and delivery. Moreover, ICT solutions are paving innovation in every sector and therefore becoming a business factor for the success of any enterprise. The implications are manifold since technical issues need to be harmonised with social, organisational and legal aspects. In this paper we illustrate and comment the current trends to identify research directions to build services as comprehensive components accessible via APIs. The goal is to deliver services ecosystems, which call for open platforms to manage services that can connect and interact via shared protocols in dynamic heterogeneous contexts. Machine-readable semantic descriptions, microservices (single-function services), and containers (independent units of deployment) are discussed as building blocks for software architectures of the future.

The ability to provide appropriate and complete API descriptions to let users discover services that satisfy a set of requirements and compose them to fulfil more complex users’ needs is critical for the success of any modern ICT solution. Composition suffers from the lack of semantic matching between properties included in published API descriptions. The work presented in this paper addresses this issue by discussing the current formats and tools to build API descriptions, and presenting a method for extracting and associating semantic to properties. Such method relies on a revised version of Table Interpretation techniques to support semantic annotations of API properties. The objectives are to enrich the popular OpenAPI Specification format with semantic annotations, and add the functionality of semantic annotation and composition to the associated editor.

We present Touch&Screen, a wide set of interaction techniques for the remote control of widgets (menu, lists, videos, maps etc.) for large screens through smartphones. After presenting the design of these widgets and the related control interfaces of the smartphone, we evaluated the interaction through two user studies. The first study (48 users) aimed to evaluate the user experience (naturalness, usability, etc.) and spontaneity of use. The second user study (12 users) aimed to evaluate our interaction techniques comparatively with direct touch and a commercial smartphone application designed to control cursors on distant screens. The evaluation results show that Touch&Screen is faster and more natural than existing solutions to control distant screens.

In recent years several description tools and formats have been introduced for describing REST Web APIs both in human and machine readable formats. Although these descriptions provide functional information about the APIs (e.g. HTTP methods, URIs, model schema, etc.), the information that qualifies the properties of APIs (e.g. classification of input arguments and response data) is missing. We envisage that providing a complete set of information to the users will facilitate the composition of APIs to fulfil users’ specific needs. This paper analyses the current state of the art in Web API Descriptions and Semantic Annotations to show that although there are solutions with semantic capabilities, most of them fails to add semantic annotations automatically or semi-automatically. Moreover, advanced technical skills are needed to manage semantics and compose different Web APIs, which reduce the number of potential users of such solutions. The goal is to enhance actual API descriptions by creating a simple description format to annotate properties at semantic level to support semi-automatic composition. To achieve this goal, we propose an extension of the Open API Initiative (OAI) specification to create comprehensive descriptions. The approach focuses on the emerging concept of API Profiling to add descriptive information of data semantics by addressing Dublin Core Application Profile (DCAP) guidelines.

YouTube4Two is an application that exploits the YouTube media library (through its API) to demonstrate a new style of social interaction. Two co-present people can share a video and act autonomously to navigate the related-video and comment lists, and search for videos. The novelty is that they can use their own smartphones connected via In- Ternet to control the shared application. The application has been designed according to the responsive-web-design (RWD) principle to smoothly pass from desktop interface (controlled by mouse and keyboard) to smartphone inter- face (with touch control). YouTube4Two introduces the multi-device responsive Web design (MD-RWD) style that extends the RWD style by introducing the separation be- Tween displayed content (on a shared screen) and displayed control commands (on personal smartphones) to support shared control over an application