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.

Most companies exploit information systems to manage their business processes. Logs generated by such systems might be used to automatically learn models of such processes, e.g. for analysis and conformance checking purposes. Since logs are often not generated specifically for this purpose, the reported activities might be too fine-grained, leading to very complex and incomprehensible (`spaghetti') models. Modularization is a way to improve understandability and reusability of the models. This work proposes an approach to automatically discover modules, in the form of subprocesses, in unstructured processes, using the WoMan framework. Experimental results on synthetic data and models are promising.

The Italian National Police started a research on vehicular traffic to improve road safety and reduce the number of theft victims. In order to support the discovery of anomalous behavior, this paper proposes a method for data analysis to automatically detect relevant hypotheses, a data mining technique to extract relevant information and a visualization technique. Traffic flow analysis is a challenging and complex task, due to the huge size of the data involved, thus falling in the realm of Big Data. Visual Analytics tools reduce and improve the search by representing a large amount of data in a small space through smart visualizations.

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.

Smart home environments should proactively support users in their activities, anticipating their needs according to their preferences. Understanding what the user is doing in the environment is important for adapting the environment's behavior, as well as for identifying situations that could be problematic for the user. Enabling the environment to exploit models of the user's most common behaviors is an important step toward this objective. In particular, models of the daily routines of a user can be exploited not only for predicting his/her needs, but also for comparing the actual situation at a given moment with the expected one, in order to detect anomalies in his/her behavior. While manually setting up process models in business and factory environments may be cost-effective, building models of the processes involved in people's everyday life is infeasible. This fact fully justifies the interest of the Ambient Intelligence community in automatically learning such models from examples of actual behavior. Incremental adaptation of the models and the ability to express/learn complex conditions on the involved tasks are also desirable. This article describes how process mining can be used for learning users’ daily routines from a dataset of annotated sensor data. The solution that we propose relies on a First-Order Logic learning approach. Indeed, First-Order Logic provides a single, comprehensive and powerful framework for supporting all the previously mentioned features. Our experiments, performed both on a proprietary toy dataset and on publicly available real-world ones, indicate that this approach is efficient and effective for learning and modeling daily routines in Smart Home Environments.

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.

In Artificial Intelligence with Coalition Structure Generation (CSG) one refers to those cooperative complex problems that require to find an optimal partition (maximizing a social welfare) of a set of entities involved in a system. The solution of the CSG problem finds applications in many fields such as Machine Learning (set covering machines, clustering), Data Mining (decision tree, discretization), Graph Theory, Natural Language Processing (aggregation), Semantic Web (service composition), and Bioinformatics. The problem of finding the optimal coalition structure is NP-complete. In this paper we present a greedy adaptive search procedure (GRASP) with path-relinking to efficiently search the space of coalition structures. Experiments and comparisons to other algorithms prove the validity of the proposed method in solving this hard combinatorial problem.

Workflow management is fundamental to efficiently, effectively, and economically carry out complex working and domestic activities. Manual engineering of workflow models is a complex, costly, and error-prone task. The WoMan framework for workflow management is based on first-order logic. Its core is an automatic procedure that learns and refines workflow models from observed cases of process execution. Its innovative peculiarities include incrementality (allowing quick learning even in the presence of noise and changed behavior), strict adherence to the observed practices, ability to learn complex conditions for the workflow components, and improved expressive power compared to the state of the art. This paper presents the entire algorithmic apparatus of WoMan, including translation and learning from a standard log format for case representation, import/export of workflow models from/into standard formalisms (Petri nets), and exploitation of the learned models for process simulation and monitoring. Qualitative and quantitative experimental evaluation shows the power and efficiency of WoMan, both in controlled and in real-world domains.

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.

Standardized processes are important for correctly carrying out activities in an organization. Often the procedures they describe are already in operation, and the need is to understand and formalize them in a model that can support their analysis, replication and enforcement. Manually building these models is complex, costly and error-prone. Hence, the interest in automatically learning them from examples of actual procedures. Desirable options are incrementality in learning and adapting the models, and the ability to express triggers and conditions on the tasks that make up the workflow. This paper proposes a framework based on First-Order Logic that solves many shortcomings of previous approaches to this problem in the literature, allowing to deal with complex domains in a powerful and flexible way. Indeed, First-Order Logic provides a single, comprehensive and expressive representation and manipulation environment for supporting all of the above requirements. A purposely devised experimental evaluation confirms the effectiveness and efficiency of the proposed solution.

The problem of implementing socially intelligent agents has been widely investigated in the field of both Embodied Conversational Agents (ECAs) and Social Robots that have the advantage of offering to people the possibility to relate with computer media at a social level. We focus our study on the recognition of the social response of users to embodied agents in the context of ambient intelligence. In this paper we describe how we extended a model for recognizing the social attitude in natural conversation from text by adding two additional knowledge sources: speech and gestures.

Metabolomics is increasingly becoming an important field. The fundamental task in this area is to measure and interpret complex time and condition dependent parameters such as the activity or flux of metabolites in cells, their concentration, tissues elements and other biosamples. The careful study of all these elements has led to important insights in the functioning of metabolism. Recently, however, there is a growing interest towards an intagrated approach to studying biological systems. This is the main goal in Systems Biology where a combined investigation of several components of a biological system is thought to produce a thorough understanding of such systems. Metabolic networks are not only structurally complex but behave also in a stochastic fashion. Therefore, it is necessary to express structure and handle uncertainty to construct complete dynamics of these networks. In this paper we describe how stochastic modeling and simulation can be performed in a symbolic-statistical machine learning (ML) framework. We show that symbolic ML deals with structural and relational complexity while statistical ML provides principled approaches to uncertainty modeling. Learning is used to analyze traces of biochemical reactions and model the dynamicity through parameter learning, while inference is used to produce stochastic simulation of the network.

We present high performing SLS algorithms for learning and inference in Markov Logic Networks (MLNs). MLNs are a state-of-the-art representation formalism that integrates first-order logic and probability. Learning MLNs structure is hard due to the combinatorial space of candidates caused by the expressive power of first-order logic. We present current work on the development of algorithms for learning MLNs, based on the Iterated Local Search (ILS) metaheuristic. Experiments in real-world domains show that the proposed approach improves accuracy and learning time over the existing state-of-the-art algorithms. Moreover, MAP and conditional inference in MLNs are hard computational tasks too. This paper presents two algorithms for these tasks based on the Iterated Robust Tabu Search (IRoTS) schema. The first algorithm performs MAP inference by performing a RoTS search within a ILS iteration. Extensive experiments show that it improves over the state-of the-art algorithm in terms of solution quality and inference times. The second algorithm combines IRoTS with simulated annealing for conditional inference and we show through experiments that it is faster than the current state-of-the-art algorithm maintaining the same inference quality.

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.

The recent explosion in Internet usage and the growing amount of digital images caused by the more and more ubiquitous presence of digital cameras has created a demand for effective and flexible techniques for automatic image retrieval. As the volume of the data increases, memory and processing requirements need to correspondingly increase at the same rapid pace, and this is often prohibitively expensive. Image collections on this scale make performing even the most common and simple image processing and machine learning tasks non trivial. In this paper we present a method to reduce the computational complexity of a widely known method for image indexing and retrieval based on a second order statistical measure. The aim of the paper is twofold: Q1) is it possible to efficiently extract an approximate distribution of the image features with a resulting low error? Q2) how the resulting approximate distribution affects the similarity-based accuracy? In particular, we propose a sampling method to approximate the distribution of correlograms, adopting a Monte Carlo approach to compute the distribution on a subset of pixels uniformly sampled from the original image. A further variant is to sample the neighborhood of each pixel too. Validation on the Caltech 101 dataset proved that the proposed approximate distribution, obtained with a considerable decrease of the computational time, has an error very low when compared to the exact distribution. Result obtained in the second experiment on a similarity-based ranking task are encouraging.

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.

First-Order Logic formulæ are a powerful representation formalism characterized by the use of relations, that cause serious computational problems due to the phenomenon of indeterminacy (various portions of one description are possibly mapped in different ways onto another description). Being able to identify the correct corresponding parts of two descriptions would help to tackle the problem: hence, the need for a framework for the comparison and similarity assessment. This could have many applications in Artificial Intelligence: guiding subsumption procedures and theory revision systems, implementing flexible matching, supporting instance-based learning and conceptual clustering. Unfortunately, few works on this subject are available in the literature. This paper focuses on Horn clauses, which are the basis for the Logic Programming paradigm, and proposes a novel similarity formula and evaluation criteria for identifying the descriptions components that are more similar and hence more likely to correspond to each other, based only on their syntactic structure. Experiments on real-world datasets prove the effectiveness of the proposal, and the efficiency of the corresponding implementation in the above tasks.

Many real-world applications of AI require both probability and first-order logic to deal with uncertainty and structural complexity. Logical AI has focused mainly on handling complexity, and statistical AI on handling uncertainty. Markov Logic Networks (MLNs) are a powerful representation that combine Markov Networks (MNs) and first-order logic by attaching weights to first-order formulas and viewing these as templates for features of MNs. State-of-the-art structure learning algorithms of MLNs maximize the likelihood of a relational database by performing a greedy search in the space of candidates. This can lead to suboptimal results because of the incapability of these approaches to escape local optima. Moreover, due to the combinatorially explosive space of potential candidates these methods are computationally prohibitive. We propose a novel algorithm for learning MLNs structure, based on the Iterated Local Search (ILS) metaheuristic that explores the space of structures through a biased sampling of the set of local optima. The algorithm focuses the search not on the full space of solutions but on a smaller subspace defined by the solutions that are locally optimal for the optimization engine. We show through experiments in two real-world domains that the proposed approach improves accuracy and learning time over the existing state-of-the-art algorithms.

In real-world digital libraries, artificial intelligence techniques are essential for tackling the automatic document processing task with sufficient flexibility. The great variability in document kind, content and shape requires powerful representation formalisms to catch all the domain complexity. The continuous flow of new documents requires adaptable techniques that can progressively adjust the acquired knowledge on documents as long as new evidence becomes available, even extending if needed the set of recognized document types. Both these issues have not yet been thoroughly studied. This paper presents an incremental first-order logic learning framework for automatically dealing with various kinds of evolution in digital repositories content: evolution in the definition of class definitions, evolution in the set of known classes and evolution by addition of new unknown classes. Experiments show that the approach can be applied to real-world.

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.

We focus on the problem of specialization in a description logics (DL) representation, specifically the ALN language. Standard approaches to learning in these representations are based on bottom-up algorithms that employ the lcs operator, which, in turn, produces overly specific (overfitting,) and still redundant concept definitions. In the dual (top-down) perspective, this issue can be tackled by means of an ILP downward operator. Indeed, using a mapping from DL descriptions onto a clausal representation, we define a specialization operator computing maximal specializations of a concept description on the grounds of the available positive and negative examples.