Knowledge graphs (KGs) play a crucial role in the integration and organization of heterogeneous data and knowledge, enabling advanced data analytics and decision-making across various industries. This vision paper addresses critical challenges in managing KGs, emphasizing their relevance in integrating information from disparate sources. We propose the concept of knowledge graph ecosystems and life cycles to systematically manage tasks, e.g., data integration, standardization, continuous updates, efficient querying, and provenance tracking. By adopting our approach, organizations can enhance the accuracy, consistency, and reliability of KGs, thus improving knowledge management, enabling the extraction of valuable insights, and ensuring transparency and accountability.

Abstract Data play a key role in AI systems that support decision-making processes. Data-centric AI highlights the importance of having high-quality input data to obtain reliable results. However, well-preparing data for machine learning is becoming difficult due to the variety of data quality issues and available data preparation tasks. For this reason, approaches that help users in performing this demanding phase are needed. This work proposes DIANA, a framework for data-centric AI to support data exploration and preparation, suggesting suitable cleaning tasks to obtain valuable analysis results. We design an adaptive self-service environment that can handle the analysis and preparation of different types of sources, i.e., tabular, and streaming data. The central component of our framework is a knowledge base that collects evidence related to the effectiveness of the data preparation actions along with the type of input data and the considered machine learning model. In this paper, we first describe the framework, the knowledge base model, and its enrichment process. Then, we show the experiments conducted to enrich the knowledge base in a particular case study: time series data streams.

The presence of accounts managed by cybersecurity experts, professionals, and organizations makes social media a valuable source for computer security awareness. By regularly capturing and analyzing the posts on emerging cyber threats, individuals and organizations can understand potential dangers in a timely manner and effectively implement mitigation strategies. However, retrieving relevant and informative posts from a social network is challenging due to the high percentage of posts containing uninformative content. This paper proposes a novel approach based on supervised classifiers for selecting relevant social media posts and categorizing them according to different types of vulnerabilities. To accomplish this task, we designed a pipeline combining text classifiers in cascade, training them on manually labelled data. We analyzed various neural network techniques, leveraging language-agnostic sentence-level embeddings and past user activity, validating these techniques in a cross-validation setup. With an achieved accuracy of 87%, our approach offers effective filtering and classification of social media posts, empowering cybersecurity professionals to stay informed and take appropriate measures.

The combination of data and technology is having a high impact on the way we live. The world is getting smarter thanks to the quantity of collected and analyzed data. However, it is necessary to consider that such amount of data is continuously increasing and it is necessary to deal with novel requirements related to variety, volume, velocity, and veracity issues. In this paper we focus on veracity that is related to the presence of uncertain or imprecise data: errors, missing or invalid data can compromise the usefulness of the collected values. In such a scenario, new methods and techniques able to evaluate the quality of the available data are needed. In fact, the literature provides many data quality assessment and improvement techniques, especially for structured data, but in the Big Data era new algorithms have to be designed. We aim to provide an overview of the issues and challenges related to Data Quality assessment in the Big Data scenario. We also propose a possible solution developed by considering a smart city case study and we describe the lessons learned in the design and implementation phases.

As cities are becoming green and smart, public information systems are being revamped to adopt digital technologies. There are several sources (official or not) that can provide information related to a city. The availability of multiple sources enables the design of advanced analyses for offering valuable services to both citizens and municipalities. However, such analyses would fail if the considered data were affected by errors and uncertainties: Data Quality is one of the main requirements for the successful exploitation of the available information. This paper highlights the importance of the Data Quality evaluation in the context of geographical data sources. Moreover, we describe how the Entity Matching task can provide additional information to refine the quality assessment and, consequently, obtain a better evaluation of the reliability data sources. Data gathered from the public transportation and urban areas of Curitiba, Brazil, are used to show the strengths and effectiveness of the presented approach.

In recent years, models, composition paradigms, and tools for mashup development have been proposed to support the integration of information sources, services and APIs available on the Web. The challenge is to provide a gate to a “programmable Web,” where end users are allowed to construct easily composite applications that merge content and functions so as to satisfy the long tail of their specific needs. The approaches proposed so far do not fully accommodate this vision. This article, therefore, proposes a mashup development framework that is oriented toward the End-User Development. Given the fundamental role of user interfaces (UIs) as a medium easily understandable by the end users, the proposed approach is characterized by UI-centric models able to support a WYSIWYG (What You See Is What You Get) specification of data integration and service orchestration. It, therefore, contributes to the definition of adequate abstractions that, by hiding the technology and implementation complexity, can be adopted by the end users in a kind of “democratic” paradigm for mashup development. This article also shows how model-to-code generative techniques translate models into application schemas, which in turn guide the dynamic instantiation of the composite applications at runtime. This is achieved through lightweight execution environments that can be deployed on the Web and on mobile devices to support the pervasive use of the created applications.