The European Commission EU4Health program (2021-2027) is launched after the severe health crisis caused by COVID-19 to support member states in long-term health challenges to build more resilient health systems aimed at reducing inequalities in access to healthcare. In Italy, the PNRR program has among its goals the enhancement of the Diagnostic Therapeutic Care Pathways, particularly their complete informatisation to reduce the gap currently present at the regional and, in some cases, at the hospital level. This paper describes a possible AI framework as a starting point for a potential solution to this goal. The proposed solution involves the use of GraphDB for information persistence and evolved process management methods for the implementation of Care Pathways.

The availability on the Internet of huge amounts of blog posts, messages and comments allows to study the attitude of people on various topics. Sentiment Analysis, Opinion Mining and Emotion Analysis denote the area of research in Computer Science aimed at studying, analyzing and classifying text documents based on the underlying opinions expressed by their authors on various topics. While this is a tough task, because it is related to psychological aspects that are not always immediately evident in the lexical and syntactical aspects of the sentences, its importance may be paramount for several applications such as market analysis, political polls, etc. Fundamental pre-processing techniques for this task come from the area of Natural Language Processing, which may pose additional problems when the language of interest is different than English, and thus less (or less reliable) resources are available to extract the needed data from the text. This paper studies the performance of Sentiment Analysis, seen as a Text Categorization task, depending on the use of different classifiers and different features. While the approach is general, we focus on texts in Italian. The outcomes suggest which experimental settings can be most profitably used in this landscape, and show that significantly good results can be obtained.

Detecting the reading order among the layout components of a document's page is fundamental to ensure effectiveness or even applicability of subsequent content extraction steps. While in single-column documents the reading flow can be straightforwardly determined, in more complex documents the task may become very hard. This paper proposes an automatic strategy for identifying the correct reading order of a document page's components based on abstract argumentation. The technique is unsupervised, and works on any kind of document based only on general assumptions about how humans behave when reading documents. Experimental results show that it is effective in more complex cases, and requires less background knowledge, than previous solutions that have been proposed in the literature.

The current tools to create OWL-S annotations have been designed starting from the knowledge engineer’s point of view. Unfortunately, the formalisms underlying Semantic Web languages are often incomprehensible to the developers of Web services. To bridge this gap, it is desirable that developers are provided with suitable tools that do not necessarily require knowledge of these languages in order to create annotations on Web services. With reference to some characteristics of the involved technologies, this work addresses these issues, proposing guidelines that can improve the annotation activity of Web service developers. Following these guidelines, we also designed a tool that allows a Web service developer to annotate Web services without requiring him to have a deep knowledge of Semantic Web languages. A prototype of such a tool is presented and discussed in this paper.

Digital Libraries collect, organize and provide to end users large quantities of selected documents. While these documents come in a variety of formats, it is desirable that they are delivered to final users in a uniform way. Web formats are a suitable choice for this purpose. While Web documents are very flexible as to layout presentation, that is determined at runtime by the interpreter, documents coming from a library should preserve their original layout when displayed to final users. Using raster images would not allow the user to access the actual content of the document's components (text and images). This paper presents a technique to render in an HTML file the original layout of a document, preserving the peculiarity of its components (text, images, formulas, tables, algorithms). It builds on the DoMInUS framework, that can process documents in several source formats.

Tables are extremely important components of documents, because they bear very informative content in a compact and structured way. Being able to understand a table's internal organization would allow to extract and reuse the data they contain. This can be reduced to recognizing critical cells only. Since purely algorithmic approaches are unable to deal with the many different table layouts designed to represent particular kinds of information and/or particular perspectives on them, Machine Learning may represent an effective solution. On one hand, the spatial organization of tables puts a strong emphasis on the relationships among cells, on the other, the extreme variability in style, size, and aims of tables requires flexible approaches. This paper proposes the exploitation of a Statistical Relational Learning approach, that is able to model the complex spatial relationships involved in a table structure, by mixing the power of a relational representation formalism with the flexibility of a statistical learning tool. Experiments on a real-world dataset are reported both for single cell classification and for overall table structure recognition, whose results prove the validity of the proposed approach.

Information Retrieval in large digital document repositories is at the same time a hard and crucial task. While the primary type of information available in documents is usually text, images play a very important role because they pictorially describe concepts that are dealt with in the document. Unfortunately, the semantic gap separating such a visual content from the underlying meaning is very wide. Additionally image processing techniques are usually very demanding in computational resources. Hence, only recently the area of Content-Based Image Retrieval has gained more attention. In this paper we describe a new technique to identify known objects in a picture based on a comparison of the shapes to known models. The comparison works by progressive approximations to save computational resources, and relies on novel algorithmic and representational solutions to improve preliminary shape extraction.

Document layout analysis is crucial in the automatic document processing workflow, because its outcome affects all subsequent processing steps. A first problem concerns the possibility of dealing not only with documents having easy layout, but with so-called non-Manhattan layout documents as well. Another problem is that most available techniques can be applied to scanned document, due to the emphasis in previous decades being put on legacy documents digitization. Conversely, nowadays most documents come directly in digital format, and thus new techniques must be developed. A famous approach proposed in the literature for layout analysis was the RLSA, suitable to scanned black&white images and based the application of Run Length Smoothing and the AND logical operator. A recent variant thereof is based on the application of the OR operator, for which reason has been called RLSO. It exploits a bottom-up approach that proved able to handle even non-Manhattan layouts, on both scanned and natively digital documents. Like RLSA, it is based on the definition of thresholds for the smoothing operator, but the different approach requires different criteria than those that work in RLSA to define proper values. Since this is a hard and unnatural task for an (even expert) user, this paper proposes a technique to automatically define such thresholds for each single document, based on the distribution of spacing therein. Application on selected samples of documents, that aimed at covering a significant landscape of real cases, revealed that the approach is satisfactory for documents characterized by the use of a uniform text font size. It can provide a useful basis also for handling more complex cases.

Layout analysis is a fundamental step in automatic document processing. Many different techniques have been proposed to perform this task. Some follow a top-down approach: they start by identifying the high level components of the page structure and then recursively split them until basic blocks are found. On the other hand, bottom-up approaches start with the smallest elements (e.g., the pixels in case of digitized document) and then recursively merge them into higher level components. A first limitation of such methods is that most of them are designed to deal only with digitized documents and hence are not applicable to native digital documents which are nowadays pervasive. Furthermore, top-down and most of bottom-up methods are able to process Manhattan layout documents only. In this work, we propose a general bottom-up strategy to tackle the layout analysis of (possibly) non-Manhattan documents, and two specializations of it to handle both bitmap and PS/PDF sources. It was successfully embedded and tested in the DOMINUS document management system.

Organizing large repositories spread throughout the most diverse Web sites rises the problem of effective storage and efficient retrieval of documents. This can be obtained by selectively extracting from them the significant textual information, contained in peculiar layout components, that in turn depend on the identification of the correct document class. The continuous flow of new and different documents in a weakly structured environment like the Web calls for in- crementality, as the ability to continuously update or revise a faulty knowledge previously acquired, while the need to express structural relations among layout components suggest the exploitation of a powerful and symbolic representation language. This paper proposes the application of incremental first-order logic learning techniques in the document layout preprocessing steps, supported by good results obtained in experiments on a real dataset.

Digital repositories raise the need for an effective and efficient retrieval of the stored material. In this paper, we propose the intensive application of intelligent techniques to the steps of document layout analysis, document image classification and understanding on digital documents. Specifically, the complex interrelation existing among layout components, that are fundamental to assign them the proper semantic role, suggest the exploitation of first-order representations in some learning steps. Results obtained in a prototypical system for scientific conference management prove that the proposed approach can be beneficial both for the layout recognition and for the selection of interesting components of the document, from which extracting the text for categorizing the document according to its topic.