• Community-based approach for fine-tuning Large Language Models • Focusing LLM training on Knowledge Graph substructures • Building expert systems by fine-tuning LLMs on domain-specific Knowledge Graph • Outperforming traditional fine-tuning in Knowledge Graph completion metrics Since the introduction of GPT-2, Large Language Models (LLMs) have proven to be able to handle various tasks with impressive performance. However, they sometimes generate incorrect output or even hallucinations. To overcome this problem, many researchers have investigated the possibility of integrating external factual knowledge, such as that encoded in Knowledge Graphs (KGs), into LLMs. Although there are many approaches in the existing literature that integrate KGs and LLMs in different ways, few of them use KGs to fine-tune LLMs, and none of them systematically use KG substructures. In this paper, we propose CoFine (Community-Based Fine-Tuner), an approach to fine-tune an LLM using the communities of a KG. CoFine works as follows: it first divides the KG into communities, each of which contains a homogeneous portion of the knowledge expressed by the KG. It then uses these communities to fine-tune the LLM. This way of proceeding allows LLM fine-tuning to focus on specific homogeneous information contained in the KG expressed by each community. CoFine allows the LLM to achieve a very high accuracy in knowledge completion tasks. This is evidenced by comparisons between CoFine and a baseline LLM fine-tuning approach, which showed that our approach achieves better results for all metrics considered with several KG.

In this paper, we introduce EgoFine, a new approach for fine-tuning Large Language Models (LLMs) based on ego networks extracted from Knowledge Graphs (KGs). EgoFine first identifies the most informative nodes in the KG using degree centrality. It then extracts their ego networks and generates structured training data through random paths within them, thus enabling LLMs to learn domain-specific knowledge. It further constructs negative samples to explicitly model the absence of relationships between entities. We present an experimental campaign involving three KGs (PrimeKG, WN18RR, and YAGO3) and four LLMs (Minerva-350M, Llama3.2-1B, Qwen2-1.5B, and Ministral-3B). This campaign demonstrates that EgoFine outperforms traditional embedding-based methods (e.g., AutoSF, BoxE, NodePiece, PairRE, and TransE), two state-of-the-art approaches integrating KGs and LLMs (e.g., GNN-RAG and KG-Adapter), as well as a baseline approach operating on the same principle as EgoFine but without exploiting the contribution of ego networks. Compared with this last approach, EgoFine improves Hit@1 values up to 47.37%, Mean Reciprocal Rank (MRR) values by up to 46.87%, F1-Score values by up to 36.59%, and Accuracy values by up to 30.91%. The paper also presents an ablation study devoted to evaluating several design choices underlying EgoFine, as well as an analysis of the EgoFine’s behavior when applied on dynamic or noisy KGs. This way of proceeding makes EgoFine particularly beneficial for a variety of real-world applications, including understanding complex biological mechanisms, reasoning about the relationships between legislative sources and court cases, interpreting and explaining complex industrial maintenance and production processes, and understanding the connections between attacks, exploits, and countermeasures in the context of cybersecurity.

Industry 5.0 envisions manufacturing systems that are human-centric, sustainable, and resilient. In this context, the Internet of Everything (IoE) enables integration of devices, people, and processes into a unified digital ecosystem. This paper presents a modular, semantically enriched framework that supports this transition by managing heterogeneous data sources—such as IoT sensors, wearable devices, and smart objects—through a layered architecture. The platform enables real-time data stream processing, semantic interoperability, and secure, context-aware access. Anomaly detection is enabled through a privacy-preserving mechanism based on behavioral fingerprinting and federated learning. The platform supports immersive human-machine interaction via gesture recognition, empowering workers to control and interact with industrial systems. Use cases demonstrate the system’s ability to support gesture-based control and intelligent monitoring, highlighting its potential to enhance adaptability, security, and worker empowerment in Industry 5.0 environments.

Vision Transformers (ViTs) have gained recognition in computer vision due to their outstanding performance. Despite their success, the explainability of ViT outputs is still a challenging issue. To address it, we propose a novel explainability method that leverages image patch embeddings from each attention layer of a ViT to construct similarity graphs. The latter are used to generate binary masks by exploring paths starting from specific patches. The masks from all layers are then aggregated into a comprehensive heatmap using the coverage bias formula. We tested our method on two Vision Transformer architectures (ViT-Base and DeiT-Base) and a subset of the ImageNet validation set. Using Insertion and Deletion metrics, we demonstrate the effectiveness of our proposed method compared to similar ones in the literature. Finally, we include a qualitative analysis that shows the capabilities of our method to make ViTs more interpretable.

Vision Transformers (ViTs) have demonstrated outstanding performance across different computer vision tasks thanks to their self-attention mechanism that captures long-range dependencies effectively. However, the inherent complexity of ViTs presents significant challenges in explaining their outputs, which is fundamental in safety-critical domains. To tackle the challenge of explaining ViT outputs, this paper presents Grad-Mask, a novel method that integrates gradients into the mask generation process to create explanation heatmaps. GradMask uses the query, key, and value matrices from each attention layer and computes their gradients with respect to a target class. Afterward, it uses these gradients to generate binary masks, which are then weighted by the corresponding ViT’s confidence scores. Finally, it combines the weighted masks to generate the resulting heatmap. Experimental evaluations on an ImageNet subset with ViT and DeiT (Data-efficient Image Transformer) architectures show that GradMask achieves competitive performance according to standard explainability metrics, such as Insertion, Deletion, and Pointing Game. A hyperparameter analysis confirms the high computational efficiency of GradMask, while an ablation study highlights the importance of combining gradients and masks for the generation of the explanation heatmap. Finally, a qualitative analysis shows the improved explainability of GradMask compared to existing methods, making it a promising approach for understanding ViTs.

Abstract In recent years, Transformers have revolutionized the management of Natural Language Processing tasks, and Vision Transformers (ViTs) promise to do the same for Computer Vision ones. However, the adoption of ViTs is hampered by their computational cost. Indeed, given an image divided into patches, it is necessary to compute for each layer the attention of each patch with respect to all the others. Researchers have proposed many solutions to reduce the computational cost of attention layers by adopting techniques such as quantization, knowledge distillation and manipulation of input images. In this paper, we aim to contribute to the solution of this problem. In particular, we propose a new framework, called AgentViT, which uses Reinforcement Learning to train an agent that selects the most important patches to improve the learning of a ViT. The goal of AgentViT is to reduce the number of patches processed by a ViT, and thus its computational load, while still maintaining competitive performance. We tested AgentViT on CIFAR10, FashionMNIST, and Imagenette $$^+$$ + (which is a subset of ImageNet) in the image classification task and obtained promising performance when compared to baseline ViTs and other related approaches available in the literature.

Vision Transformers (ViTs), although very successful, have a major limitation to overcome, namely the need for significant computational resources to use them. Several approaches have been proposed to limit the resources required to work with ViTs, aiming at pruning the data provided in input to them. In this paper, we propose Token Reduction via an Attention-based Multilayer network (TRAM), the first approach that achieves this goal using a multilayer network-based representation of the attention matrices. TRAM can work with most ViTs without the need for fine-tuning. It makes several contributions to the literature in this research area; in particular, it is characterized by: (i) a new representation of ViTs based on a multilayer network; (ii) a new approach to evaluate the relevance of tokens based on a new centrality measure computed on the multilayer network; and (iii) an approach to reduce the number of tokens based on this centrality measure. We have validated TRAM by comparing it with several state-of-the-art approaches during an extensive experimental campaign carried out on different image datasets. The results obtained demonstrate not only the efficiency but also the effectiveness of TRAM in reducing the computational load of ViTs while still allowing them to provide accurate results. • TRAM represents tokens using an attention-based multilayer network. • TRAM reduces ViT computational demand without requiring fine-tuning. • TRAM improves FPS and GFlops with near-Vanilla model accuracy. • Visual analysis reveals TRAM’s token selection process.

In this paper, we propose Automatic Intent Detector (AID), a framework for automatic intent detection to facilitate the creation of a conversational agent. AID follows an eight-step process incorporating best practices from the current literature and introducing innovative approaches in certain steps. The most notable innovation within AID is the automatic labeling of clusters, which is based on detailed and sophisticated rules derived from linguistics. These rules focus on morphosyntactic analysis, while also taking into account an aspect of semantic role theory. Furthermore, as for the overall validation of the results obtained, it provides an approach based on the concepts of semantic coherence, variability, and label appropriateness. After describing AID at the technical level, we illustrate the experiments we conducted both on a dataset widely used as benchmark in the literature and on a real corporate dataset. Finally, we present a critical discussion on the results obtained.

Abstract The enormous growth of artificial intelligence (AI), and deep learning (DL) in particular, has led to the widespread use of these systems in a variety of contexts. One DL model capable of addressing complex computer vision tasks is the vision transformer (ViT). Despite its huge success, the reasoning behind the inferences it makes is often unclear, which poses significant challenges in critical scenarios. In this paper, we propose a new approach called MUltiplex Transformer EXplainer (MUTEX), which aims to explain the inferences made by ViTs. MUTEX combines multiplex network-based representations of attention matrices and mask perturbation approaches to provide insight into the inference process of ViTs. By mapping the attention layers of a ViT into a multiplex network, MUTEX is able to analyze the relationships between different parts of the input image and identify the image patches that most influence the inference process. We tested MUTEX on a subset of ImageNet and on BloodMNIST and compared its performance with that of existing visual explainability approaches. In addition, to assess the robustness and adaptability of MUTEX, we conducted a qualitative analysis, along with a hyperparameter and ablation study, which allowed us to further appreciate its potential in visual explainability of ViT.

In recent years, the enormous development of Machine Learning, especially Deep Learning, has led to the widespread adoption of Artificial Intelligence (AI) systems in a large variety of contexts. Many of these systems provide excellent results but act as black-boxes. This can be accepted in various contexts, but there are others (e.g., medical ones) where a result returned by a system cannot be accepted without an explanation on how it was obtained. Explainable AI (XAI) is an area of AI well suited to explain the behavior of AI systems that act as black-boxes. In this paper, we propose a model-agnostic XAI framework to explain the behavior of classifiers. Our framework is based on network theory; thus, it is able to make use of the enormous amount of results that researchers in this area have discovered over time. Being network-based, our framework is completely different from the other model-agnostic XAI approaches. Furthermore, it is parameter-free and is able to handle heterogeneous features that may not even be independent of each other. Finally, it introduces the notion of dyscrasia that allows us to detect not only which features are important in a particular task but also how they interact with each other.

Abstract In this paper, we propose a framework that uses the theory and techniques of (Social) Network Analysis to investigate the learned representations of a Graph Neural Network (GNN, for short). Our framework receives a graph as input and passes it to the GNN to be investigated, which returns suitable node embeddings. These are used to derive insights on the behavior of the GNN through the application of (Social) Network Analysis theory and techniques. The insights thus obtained are employed to define a new training loss function, which takes into account the differences between the graph received as input by the GNN and the one reconstructed from the node embeddings returned by it. This measure is finally used to improve the performance of the GNN. In addition to describe the framework in detail and compare it with related literature, we present an extensive experimental campaign that we conducted to validate the quality of the results obtained.

Social platforms are the preferred medium for many people to express their opinions on many topics. This has led many professionals from various fields (marketing, politics, research and development, etc.) to demand increasingly advanced approaches capable of analyzing the evolution of user or community sentiments on particular topics. In this paper, we want to make a contribution to addressing this issue. Specifically, we propose a model and a framework to analyze the dynamics of user and community sentiments in a social platform. In particular, our framework currently focuses on three activities, namely: (i) finding users capable of creating and maintaining a community that reflects their sentiment on a topic; (ii) studying how a user or community sentiment on a topic evolves over time; and (iii) investigating the cross-contamination between a user community and its neighborhood. We tested our framework by means of an extensive experimental campaign that we describe in the paper. Our framework is extremely scalable, and further activities can be easily implemented in it in the near future.

In the last few decades, we have witnessed an increasing focus on safety in the workplace. ICT has always played a leading role in this context. One ICT sector that is increasingly important in ensuring safety at work is the Internet of Things and, in particular, the new architectures referring to it, such as SIoT, MIoT and Sentient Multimedia Systems. All these architectures handle huge amounts of data to extract predictive and prescriptive information. For this purpose, they often make use of Machine Learning. In this paper, we propose a framework that uses both Sentient Multimedia Systems and Machine Learning to support safety in the workplace. After the general presentation of the framework, we describe its specialization to a particular case, i.e., fall detection. As for this application scenario, we describe a Machine Learning based wearable device for fall detection that we designed, built and tested. Moreover, we illustrate a safety coordination platform for monitoring the work environment, activating alarms in case of falls, and sending appropriate advices to help workers involved in falls.

With the advent of high-throughput and next-generation sequencing (NGS) technologies [1], huge amounts of ‘omics’ data (i.e. data from genomics, proteomics, pharmacogenomics, metagenomics, etc.) are continuously produced. Combining and integrating diverse omics data types is important in order to investigate the molecular machinery of complex diseases, with the hope for better disease prevention and treatment [2]. Experimental data repositories of omics data are publicly available, with the main aim of fostering the cooperation among research groups and laboratories all over the world. However, despite their openness, the effective integrated use of available public sources is hampered by the heterogeneity, complexity and large size of data stored therein. The main issues to be addressed when approaching omics data integration are related to the difficulty in managing and analyzing these data. Indeed, specific and multidisciplinary competences are required, and combining data of different types is not a simple task. Both the extensional (i.e. the real data) and intensional (i.e. the corresponding metadata) levels may be involved in this integration process, according to the specific problem under consideration. In the last few years, information systems researchers have made significant efforts in the proposal of effective methodologies for the integration of structured and semi-structured data formats [3]. However, omics data are often unstructured. This pushes toward the study of how data source integration can be successfully performed when structured, semi-structured and unstructured data sources coexist. This themed issue provides an extensive overview of the main challenges related to omics data integration and the methods that have been recently proposed in order to address them. It comprises nine manuscripts, each dealing with one of four central key issues, as detailed below. Understanding how the direct or indirect relationships among cellular components may impact the occurrence and progress of disorders and diseases is an important issue, which requires omics data integration to be addressed. Manuscripts of this group start from the assumption that, confirmed by several studies in the literature, the occurrence and progress of many diseases have genetic causes. For example, genetic variations have direct effects on individual phenotypes, possibly causing the production of partially or totally dysfunctional proteins. With this regards, Galano-Frutos, García-Cebollada and Sancho in Molecular Dynamics Simulations for Genetic Interpretation in Protein Coding Regions: Where we Are, Where to Go and When observe that predicting whether the replacement of one amino acid residue with another will be tolerated or cause disease is a key factor. In particular, first they review existing prediction tools based on evolutionary information and simple physical–chemical properties. Then, they describe more recent and accurate methods, such as full-atom molecular dynamics simulation in explicit solvent and discuss how these methods can be used in order to interpret human genetic variations at a large scale. Another important aspect is related to data coming from ‘single cell analysis’, which may be used in order to understand differences in healthy/unhealthy populations. In Computational methods for the integrative analysis of single cell data, Forcato, Romano and Bicciato describe the computational methods for the integrative analysis of single-cell genomic data. They mainly focus on the integration of single-cell RNA sequencing datasets and on the joint analysis of multimodal signals from individual cells. Omics data are represented in a wide variety of notations and formats, often with different levels of quality. This intrinsic heterogeneity in both data and repositories makes difficult their effective combination for producing new knowledge and may hamper their correct use and exploitation. ‘Genomic data integration’ is the topic of The road towards data integration in human genomics: players, steps and interactions by Bernasconi, Canakoglu, Masseroli and Ceri. In this manuscript, the authors first describe a technological pipeline from data production to data integration. Then, they propose a taxonomy of genomic data players and apply it to about 30 important players. They specifically focus on integrator players and evaluate the computational environment for data integration purposes provided by them. The role of ‘conceptual models’ to support the efficient management of genomic data is discussed in Using Conceptual Modeling to Improve Genome Data Management by Pastor, León Palacio, Reyes Román, García S. and Casamayor. The authors describe a solution that helps researchers to organize, store and process information and, at the same time, focuses only on relevant data minimizing the information overload in clinical research context. An overview of available ‘patient-level datasets’ containing both genotypic and phenotypic data is presented in GenoPheno: cataloging large-scale phenotypic and next generation sequencing data within human datasets by Gutiérrez-Sacristán, De Niz, Kothari,Won Kong, Mandl and Avillach. In this manuscript, the authors describe a dynamic, online catalog for consultation, contribution and revision by the research community. It consists of 30 datasets and was created by them with the purpose of making it publicly available. A survey on ‘machine learning’ methods operating on the cloud for gene regulation studies is presented in Machine learning-based analysis of multi-omics data on the cloud for investigating gene regulations by Oh, Park, Kim and Chae. The authors describe these methods, categorize them according to five different goals and summarize them in terms of multiomics input types. They explain the positive role that the cloud can play for the analysis of multiomics data. They also discuss some important issues to address when machine learning-based approaches operating on the cloud are adopted for the analysis of gene regulations. Structured sparsity regularization for analyzing high-dimensional omics data by Vinga focuses on ‘structured regularizers’ and ‘penalty functions’, when applied to omics data. The author analyzes their potential in identifying disease’s molecular signature, in order to create high-performance clinical decision support systems and, ultimately, favor personalized healthcare. Microbial communities and viral populations have a crucial role in the environment and in human health. In Comparison of Microbiome Samples: Methods and Computational Challenges, Comin, Di Camillo, Pizzi and Vandin provide a study on ‘metagenomic’ NGS datasets. These authors compare datasets from three different viewpoints, namely: (i) species identification and quantification; (ii) efficient computation of distances between metagenomic sample datasets; (iii) identification of metagenomics features associated with a phenotype. In Epidemiological Data Analysis of Viral Quasispecies in the Next-Generation Sequencing Era, Knyazev, Hughes, Skums and Zelikovsky deal with the analysis of intrahost RNA viral populations. In particular, they examine bioinformatics tools that: (i) characterize the complexity of intrahost viral population; (ii) support epidemiological analysis in inferring drug-resistant mutations, infection age and patient linkage; (iii) support surveillance systems for fast response and outbreak control. Hopefully, this themed issue will represent a springboard for fruitful collaborations among researchers from multidisciplinary areas, which could give a significant boost to the advancement of knowledge in different fields through a more effective analysis of omics data. The Editors are grateful to both the Editor-in-Chief and the Publisher for having trusted this project and for having supported them in all their needs. Many thank also to all the authors and reviewers, whose expertise and effort allowed the realization of this themed issue. Simona E. Rombo is Associate Professor in Computer Science at the Department of Mathematics and Computer Science of University of Palermo. Her main research interests include Bioinformatics, algorithms and methodologies for network analysis, Big Data analytics. She is the Principal Investigator of several national and international research projects in these fields, and she is cofounder of a spin-off working on decision support for Precision Medicine. SER has been visiting scientist at different research institutes, among which the Department of Computer Science at Purdue University and the College of Computing at Georgia Institute of Technology. Domenico Ursino received the MSc Degree in Computer Engineering from the University of Calabria in July 1995. He received the PhD in System Engineering and Computer Science from the University of Calabria in January 2000. From January 2005 to December 2017 he was an Associate Professor at the University Mediterranea of Reggio Calabria. From January 2018 he is a Full Professor at the Polytechnic University of Marche. His research interests include Social Network Analysis, Social Internetworking, Source and Data Integration, Innovation Management, Multiple Internet of Things scenarios, Knowledge Extraction and Representation, Biomedical Applications, Recommender Systems, Data Lakes. In these research fields, he published more than 200 papers. Pora Kim is an assistant professor in the School of Biomedical Informatics, The University of Texas Health Science Center at Houston. Her research interest includes bioinformatics and cancer genomics.

People's daily life is increasingly intertwined with smart devices, which are more and more used in dynamic contexts. Therefore, searching and exploiting the wealth of information produced by the Internet of Things (IoT) require novel models, including a representation of the actual context of use. The definition of context is inherently difficult, due to the variety of application scenarios and user needs. In this article, we propose a general model for devices' contexts representing context components at different resolutions (or levels of granularity). This enables the definition of a multiresolution context-based algorithm for querying the IoT, according to given preferences and contexts that can be tightened or relaxed depending on the given application goal. Experimental results show how the proposed approach outperforms traditional solutions by increasing the retrieval of relevant results while keeping precision under control.

The Internet of Things (IoT) is currently considered the new frontier of the Internet. One of the most effective ways to investigate and implement IoT is based on the use of the social network paradigm. In the last years, social network researchers have introduced new models capable of capturing the growing complexity of this scenario. One of the most known of them is the Social Internetworking System, which models a scenario comprising several related social networks. In this paper, we investigate the possibility of applying the ideas characterizing the Social Internetworking System to IoT and we propose a new paradigm capable of modelling this scenario and of favoring the cooperation of objects belonging to different IoTs. Furthermore, in order to give an idea of both the potentialities and the complexity of this new paradigm, we illustrate in more detail one of the most interesting issues regarding it, namely the redefinition of the betweenness centrality measure.

Time is a useful dimension to explore in text databases especially when historical and factual information is concerned. As documents generally refer to different events and time periods, understanding the focus time of key sentences, defined as the time the content refers to, is a crucial task to temporally annotate a document. In this paper, we leverage a bag of linked entities representation of sentences and temporal information from Wikipedia and DBpedia to implement a novel approach to focus time estimation. We evaluate our approach on sample datasets and compare it with a state of the art method, measuring improvements in MRR.

White Matter fibers play an important role in the working of brain. In order to improve their analysis, it is important to cluster them in homogeneous bundles. In this activity, the amount of data to process is huge, and an automated approach to carrying out this task is in order. Since fiber clustering should consider the position of fibers in the three-dimensional space, we are in presence of a multi-dimensional clustering problem. In this paper, we propose an automated approach to solving it. Our approach is based on a particular string representation of fibers and on a new string dissimilarity metric. Thanks to these two novelties, we can reduce the complex problem of White Matter fiber clustering to a much simpler and well-known string clustering problem. Interestingly, this way of proceeding can be extended to define other multi-view data applications, as well as to integrate (possibly heterogeneous) data coming from different domains.

The social network phenomenon involves hundreds of millions of people every day. This enormous volume of activity results in a huge source of information that can be valuable in many fields, for both research and application purposes. The relevance of this information strongly depends on the evolution occurring in the social Web, in which interaction among different social networks and their cross-relationships are becoming progressively more important. This, in fact, represents the basis of an emergent scenario called Social Internetworking Scenario. However, efficiently accessing and fruitfully querying this huge information source is not easy, because no tool to support applications needing a massive utilization of cross-social-network data exists. In this paper, we fill this gap by proposing Social Network Account Knowledge Extractor (SNAKE), a system supporting the extraction of structural data from a social network account. SNAKE is implemented in such a way as to be easily integrated in any social-network-based application. To show the practical relevance of our proposal, we present our experience gained in three possible real-life applications strongly relying on information provided by SNAKE. Copyright © 2014 John Wiley & Sons, Ltd.

In new generation social networks, we expect that the paradigm of Social Internetworking Systems (SISs, for short) will be more and more important. In this new scenario, the role of Social Network Analysis is of course still crucial but the preliminary step to do is designing a good way to crawl the underlying graph. While this aspect has been deeply investigated in the field of social networks, it is an open issue when moving towards SISs. Indeed, we cannot expect that a crawling strategy which is good for social networks, is still valid in a Social Internetworking Scenario, due to its specific topological features. In this paper, we first confirm the above claim and, then, define a new crawling strategy specifically conceived for SISs. Finally, we show that it fully overcomes the drawbacks of the state-of-the-art crawling strategies.

The diffusion of social networks has stimulated folksonomy-based systems (hereafter, folk-systems) to equip themselves with functionalities for the management of social relationships among users. This suggests that folk-systems and social networks ha

Social (or folksonomic) tagging has become a very popular way to describe content within Web 2.0 websites. However, as tags are informally defined, continually changing, and ungoverned, it has often been criticised for lowering, rather than increasing, the efficiency of searching. To address this issue, a variety of approaches have been proposed that recommend users what tags to use, both when labeling and when looking for resources. These techniques work well in dense folksonomies, but they fail to do so when tag usage exhibits a power law distribution, as it often happens in real-life folksonomies. To tackle this issue, we propose an approach that induces the creation of a dense folksonomy, in a fully automatic and transparent way: when users label resources, an innovative tag similarity metric is deployed, so to enrich the chosen tag set with related tags already present in the folksonomy. The proposed metric, which represents the core of our approach, is based on the mutual reinforcement principle. Our experimental evaluation proves that the accuracy and coverage of searches guaranteed by our metric are higher than those achieved by applying classical metrics.

In this paper, we present a multiagent system to support patients in search of healthcare services in an e-health scenario. The proposed system is HL7-aware in that it represents both patient and service information according to the directives of HL7, the information management standard adopted in medical context. Our system builds a profile for each patient and uses it to detect Healthcare Service Providers delivering e-health services potentially capable of satisfying his needs. In order to handle this search it can exploit three different algorithms: the first, called PPB, uses only information stored in the patient profile; the second, called DS-PPB, considers both information stored in the patient profile and similarities among the e-health services delivered by the involved providers; the third, called AB, relies on {\rm A}{\bf^*}, a popular search algorithm in Artificial Intelligence. Our system builds also a social network of patients; once a patient submits a query and retrieves a set of services relevant to him, our system applies a spreading activation technique on this social network to find other patients who may benefit from these services.

Ontologies significantly enhance the possibility to make agent-based Web applications really “semantic”. However, in order to make their usage both effective and efficient, some alignment problems, arising from the presence of semantic heterogeneitie

Social internetworking systems are a significantly emerging new reality; they group together a set of social networks and allow their users to share resources, to acquire opinions and, more in general, to interact, even if these users belong to different social networks and, therefore, did not previously know each other. In this context the notions of trust and reputation play a very relevant role. These notions have been widely studied in the past in several contexts whereas they have been largely neglected in the social internetworking research; however, since this application field presents several peculiarities, the results found in other application contexts are not automatically valid here. This paper introduces a model to represent and handle trust and reputation in a social internetworking system and proposes an approach that exploits these parameters to provide users with suggestions about the most reliable persons they can contact or social networks they can register to.

In this paper, we present a multiagent system aiming at assisting citizens in the current e-government scenario characterized by a huge amount of heterogeneous services that makes it difficult to quickly answer citizen queries. We show that the adoption of intelligent agent technology makes our system capable of helping a citizen in his search of services in such a way as to satisfy his interests and to face his needs; moreover, this technology ensures a high level of proactivity because it can identify services potentially relevant to a citizen even though he has never required them explicitly; finally, it can enhance citizen participation to decisional processes because it can encourage citizens to form communities who can debate in such a way as to propose the activation of new services of interest to them.

In this paper, we propose an Extensible Markup Language (XML)-based multiagent recommender system for supporting online recruitment services. Our system is characterized by the following features: 1) it handles user profiles for personalizing the job search over the Internet; 2) it is based on the intelligent agent technology; and 3) it uses XML for guaranteeing a light, versatile, and standard mechanism for information representation, storing, and exchange. This paper discusses the basic features of the proposed system, presents the results of an experimental study we have carried out for evaluating its performance, and makes a comparison between the proposed system and other e-recruitment systems already presented in the past.

In this article, we propose an agent-based approach for managing e-commerce activities. In our approach, an agent is present in each e-commerce site, managing the information stored there. In addition, another agent is associated with each customer, handling his/her profile. The proposed approach is based on the use of a particular conceptual model called the Behaviour-Semantic Distance and Relevance (B-SDR) network, which is capable of uniformly representing and handling information stored in e-commerce sites and customer profiles. The capabilities of the B-SDR network model are exploited to let customer and site agents cooperate in such a way in order to support a customer in identifying, whenever he/she accesses an e-commerce site, those products and services present in the site itself and for better matching his/her interests. The approach has been implemented in a prototype in which its functionalities are discussed here also. © 2004 Wiley Periodicals, Inc.

A large amount of research is currently focusing on the issue of the adaptive control of the quality-of-service (QoS) provided to multimedia applications in heterogeneous wireless systems. In this paper, the authors aim at contributing to this issue by proposing a mechanism that exploits user profiling techniques and suitable QoS mapping functions to introduce the soft QoS idea into a wireless multimedia scenario. The research objective is a QoS control architecture, which enables the continuous convergence between the actual user preferences and expectations and the resource constraints of the underlying wireless system. The proposed architecture operates between the system and the application layer. This allows it to achieve the intended results, by means of an effective dynamic reconfiguration of the applications and the contemporary renegotiation of the wireless resources.

Abstract In this paper we present DIKE, a system supporting the semi‐automatic construction of cooperative information systems from heterogeneous databases. The input of DIKE consists of the set of databases to belong to the cooperative system. First, DIKE constructs a data repository representing a structured, integrated and consistent description of the information stored in the input databases. The data repository thus constructed is then used as the core structure of a mediator‐like module supporting the user‐friendly integrated access to available data resources. The core of DIKE is the extraction and exploitation of the inter‐schema knowledge (in the form of inter‐schema properties) relative to the involved database schemas. Copyright © 2003 John Wiley & Sons, Ltd.

The availability of automatic tools for inferring semantics of database schemes is useful to solve several database design problems such as that of obtaining cooperative information systems or data warehouses from large sets of data sources. In this context, a main problem is to single out similarities or dissimilarities among scheme objects (interscheme properties). This paper presents graph-based techniques for a uniform derivation of interscheme properties including synonymies, homonymies, type conflicts, and subscheme similarities. These techniques are characterized by a common core: the computation of maximum weight matchings on some bipartite weighted graphs derived using a suitable metrics to measure semantic closeness of objects. The techniques have been implemented in a system prototype. Several experiments conducted with it, and (in part) accounted for in the paper, confirmed the effectiveness of our approach.

In this paper we propose a multi-agent model for handling e-commerce activities. In our model, an agent is present in each e-commerce site, managing the information stored therein. In addition, another agent is associated with each customer handling her/his profile. The proposed model is based on the exploitation of a particular conceptual model, called the B-SDR network, capable of representing and handling both information stored in e-commerce sites and customer profiles. The capabilities of the B-SDR network model are exploited to let customer and site agents to cooperate in such a way to support a customer to detect, whenever she/he accesses an e-commerce site, those products and services present in the site itself and better matching her/his interests.

XML is rapidly becoming a standard for information exchange over the Web. Web providers and applications using XML for representing and exchanging their data make their information available in such a way that interoperability can be easily reached. However in order to guarantee both the exchange of XML documents and the interoperability between information providers, it is often needed to single out semantic similarity properties relating concepts of different XML documents. This paper gives a contribution to this framework by proposing a technique for extracting synonymies and homonymies. The derivation technique is based on a rich conceptual model (called SDR-Network) which is used to represent concepts expressed in XML documents as well as the semantic relationships holding among them.

Studies the interaction between the extraction of heterogeneous database inter-scheme properties, such as synonymies, homonymies and type conflicts (which are exploited for defining the semantics of the involved schemes for integration purposes), and the resolution of type conflicts. As a matter of fact, the transformations required by type conflict resolution could invalidate some of the detected inter-scheme properties. We propose a semi-automatic approach to face this problem, based on an iterative computation: each iteration of the computation derives inter-scheme properties, verifies if at least one type conflict exists and, in the affirmative case, modifies schemes for solving the derived type conflicts. We prove that the number of iterations required by the computation to terminate is polynomial in the number of objects belonging to the involved schemes.

The availability of automatic tools for inferring semantics from database schemes is very relevant in designing large cooperative information system applications involving many information sources. Deriving semantics from existing data sources exploits properties of objects belonging to different input schemes (interscheme properties), such as synonymies, homonymies, type conflicts, and subscheme similarities. The paper gives a contribution in this context by proposing a collection of graph based techniques for a uniform derivation of all interscheme properties. All techniques are characterized by a common core consisting of the computation of a maximum weight matching on suitable bipartite graphs. The computation of the maximum weight matching is based on a suitable metrics which is used to measure object semantic similarities. A running example is provided to illustrate the approach.

Article Free Access Share on An automatic technique for detecting type conflicts in database schemes Authors: Luigi Palopoli Dipartimento di Elettronica, Informatica e Sistemistica, Università della Calabra, 87036 Rende (CS), Italy Dipartimento di Elettronica, Informatica e Sistemistica, Università della Calabra, 87036 Rende (CS), ItalyView Profile , Domenico Saccá View Profile , Domenico Ursino Dipartimento di Elettronica, Informatica e Sistemistica, Università della Calabra, 87036 Rende (CS), Italy Dipartimento di Elettronica, Informatica e Sistemistica, Università della Calabra, 87036 Rende (CS), ItalyView Profile Authors Info & Claims CIKM '98: Proceedings of the seventh international conference on Information and knowledge managementNovember 1998 Pages 306–313https://doi.org/10.1145/288627.288671Online:01 November 1998Publication History 22citation346DownloadsMetricsTotal Citations22Total Downloads346Last 12 Months9Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF

An important tool for the integration of large federated database systems is a global dictionary describing all the involved schemes into an unified framework. The first step in the construction of such a dictionary is the discovery of the properties holding among objects in different schemes. This paper presents novel algorithms to discover possible synonyms, homonyms and inclusions. In addition, the paper also deals with another crucial step in the construction of a dictionary: schema integration. The approach proposed for this step exploits inter-schema properties discovered in the previous step to achieve schema integration. This approach is also concerned with producing suitable abstractions in order to structure the description of the global dictionary into a hierarchy of concepts in order to yield a more flexible, uniform view of the attached databases. The above two steps are interleaved with other steps (mainly devoted to interfacing with database administrators and to validating the discovered properties) and have been experimented with for the construction of a global dictionary for a large number of public administration database systems.