Abstract Modern data mining applications require to perform incremental clustering over dynamic datasets by tracing temporal changes over the resulting clusters. In this paper, we propose A-Posteriori affinity Propagation (APP), an incremental extension of affinity propagation (AP) based on cluster consolidation and cluster stratification to achieve faithfulness and forgetfulness. APP enforces incremental clustering where i) new arriving objects are dynamically consolidated into previous clusters without the need to re-execute clustering over the entire dataset of objects, and ii) a faithful sequence of clustering results is produced and maintained over time, while allowing to forget obsolete clusters with decremental learning functionalities. Four popular labeled datasets are used to test the performance of APP with respect to benchmark clustering performances obtained by conventional AP and incremental affinity propagation based on nearest neighbor assignment algorithms. Experimental results show that APP achieves comparable clustering performance while enforcing scalability at the same time.

To cope with the growing volume, complexity, and articulation of legal documents as well as to foster digital justice and digital law, increasing effort is being devoted to legal knowledge extraction and digital transformation processes. In this paper, we present the ASKE (Automated System for Knowledge Extraction) approach to legal knowledge extraction, based on a combination of context-aware embedding models and zero-shot learning techniques into a three-phase extraction cycle, which is executed a number of times (called generations) to progressively extract concepts representative of the different meanings of terminology used in legal documents chunks. A graph-based data structure called ASKE Conceptual Graph is initially populated through a data preparation step, and it is continuously enriched at each ASKE generation with results of document chunk classification, new extracted terminology, and newly derived concepts. A quantitative evaluation of ASKE knowledge extraction and document classification is provided by considering the EurLex dataset. Furthermore, we present the results of applying ASKE to a real case-study of Italian case law decisions with qualitative feedback from legal experts in the framework of an ongoing national research project.

In this paper, we present CRIKE, a data-science approach to automatically detect concrete applications of legal abstract terms in case-law decisions. To this purpose, CRIKE relies on the use of the LATO ontology where legal abstract terms are properly formalized as concepts and relations among concepts. Using LATO, CRIKE aims at discovering how and where legal abstract terms are applied by judges in their legal argumentation. Moreover, we detect the terminology used in the text of case-law decisions to characterize concrete abstract-term instances. A case-study on a case-law decisions dataset provided by the Court of Milan, Italy, is also discussed.

In this paper, we present an approach for classifying heterogeneous Web information resources and organizing them as smart entity views based on a target entity of interest (e.g., person, place, town, event). In particular, we propose a dimensional clustering algorithm for the generation of smart entity views and we provide analysis techniques for enabling web resource exploration through entity visualization, entity analysis, and entity expansion.

The goal of this paper is to demonstrate inWalk, an interac-tive web-based system for linked data exploration featured by the notion of inCloud and thematic walk. The demon-stration focuses on the key functionalities of the system for smart data aggregation and navigation.

The huge amount of social data actually available and everyday produced demands for discovery techniques to mine prominent information topics. In this paper, we present topic-clouds as a solution for thematic, conceptual exploration of social data, with specific application to microblogging posts of Twitter. Topic-clouds are the result of a discovery approach based on classification and abstraction techniques to mine the most prominent topics that emerge from a possibly-large set of social data.

The development of solutions to effectively browse and explore the huge amount of data actually available in the Linked Data Cloud is getting more and more importance. As a result, automated techniques to generate high-level, concept-based information structures are recently being emerging. The notion of inCloud we proposed is an example in this direction. In this paper, we propose to extend the in Cloud construction process with crowdsourced knowledge, to benefit from human knowledge and experience for improving the overall quality of resulting inClouds for linked data exploration.

P2P techniques for semantic coordination based on semantic communities are recently emerging to enforce effective collaboration platforms. In this paper, we focus on semantic coordination in the iCoord P2P system and we introduce the notion of collective intelligence to enable semantic peer communities to setup a shared community vocabulary which is gradually built with the autonomous contribution of all the community members according to their terminological preferences. Techniques for extending iCoord to include in the collective intelligence also the social peer knowledge coming from conventional tagging systems are also presented.

The recent success of distributed and dynamic infrastructures for knowledge sharing has raised the need for semiautomatic/automatic ontology evolution strategies. Ontology evolution is generally defined as the timely adaptation of an ontology to changing requirements and the consistent propagation of changes to dependent artifacts. In this article, we present an ontology evolution approach in the context of multimedia interpretation. Ontology evolution in this context relies on the results obtained through reasoning for the interpretation of multimedia resources, through population of the ontology with new individuals or through enrichment of the ontology with new concepts and new semantic relations. The article analyses the results of interpretation, population and enrichment obtained in evaluation experiments in terms of measures such as precision and recall. The evaluation reveals encouraging results.

In this article, we describe the MX-Onto ontology for providing a Semantic Web compatible representation of music resources based on their context. The context representation is realized by means of an OWL ontology that describes music information and that defines rules and classes for a flexible genre classification. By flexible classification we mean that the proposed approach enables capturing the subjective interpretation of music genres by defining multiple membership relations between a music resource and the corresponding music genres, thus supporting context-based and proximity-based search of music resources.

The rapid growth of the Web and the ease with which data can be accessed facilitate the distribution and sharing of information. Information dissemination often takes the form of documents that are made available at Web servers, or that are actively broadcasted by Web servers to interested clients. In this paper, we present an XML-compliant formalism for specifying security-related information for Web document protection. In particular, we introduceX-Sec, an XML-based language for specifying subject credentials and security policies and for organizing them into subject profiles and policy bases, respectively. The language is complemented by a set of subscription-based schemes for accessing distributed Web documents, which rely on defined XML subject profiles and XML policy bases.

No abstract available.

The problem of defining global views of heterogeneous data sources to support querying and cooperation activities is becoming more and more important due to the availability of multiple data sources within complex organizations and in global information systems. Global views are defined to provide a unified representation of the information in the different sources by analyzing conceptual schemas associated with them and resolving possible semantic heterogeneity. We propose an affinity based unification method for global view construction. In the method: (1) the concept of affinity is introduced to assess the level of semantic relationship between elements in different schemas by taking into account semantic heterogeneity; (2) schema elements are classified by affinity levels using clustering procedures so that their different representations can be analyzed for unification; (3) global views are constructed starting from selected clusters by unifying representations of their elements. Experiences of applying the proposed unification method and the associated tool environment ARTEMIS on databases of the Italian Public Administration information systems are described.

In order to design workflows in changing and dynamic environments, a flexible, correct, and rapid realization of models of the activity flow is required. In particular, techniques are needed to design workflows capable of adapting themselves effectively when exceptional situations occur during process execution. The authors present an approach to flexible workflow design based on rules and patterns developed in the framework of the WIDE project. Rules allow a high degree of flexibility during workflow design by modeling exceptional aspects of the workflow separately from the main activity flow. Patterns model frequently occurring exceptional situations in a generalized way by providing the designer with skeletons of rules and suggestions about their instantiation, together with indications on relationships with other rules, with the activity flow, and with related information. Pattern based design relies on a pattern catalog containing patterns to be reused and on a formal basis for specializing and instantiating available patterns.

To support the development of uniform query interfaces over distributed and heterogeneous databases, tools for the analysis and reconciliation of database conceptual schemas are required. The paper presents the ARTEMIS tool environment developed to support the analyst in the process of analyzing and reconciling sets of heterogeneous data schemas. Schema analysis in ARTEMIS is performed according to the concept of affinity, while schema reconciliation is performed interactively on clusters of elements with affinity, based on unification rules. Issues on expected tool interaction and on experimentation and applicability of ARTEMIS are discussed.

Workflow design involves modeling different aspects of a business process. Among these aspects, workflow design should consider also security requirements. These relate to the authorizations for the users in the organization to execute workflow tasks

The problem of analyzing and classifying conceptual schemas is becomig increasingly important due to the availability of a large number of schemas related to existing applications. The purposes of schema analysis and classification activities can be different: to extract information on intensional properties of legacy systems in order to restructure or migrate to new architectures; to build libraries of reference conceptual components to be used in building new applications in a given domain; and to identify information flows and possible replication of data in an organization. This article proposes a set of techniques for schema analysis and classification to be used separately or in combination. The techniques allow the analyst to derive significant properties from schemas, with human intervention limited as far as possible. In particular, techniques for associating descriptors with schemas, for abstracting reference conceptual schemas based on schema clustering, and for determining schema similarity are presented. A methodology for systematic schema analysis is illustrated, with the purpose of identifying and abstracting into reference components the similar and potentially reusable parts of a set of schemas. Experiences deriving from the application of the proposed techniques and methodology on a large set of Entity-Relationship conceptual schemas of information systems in the Italian Public Administration domain are described

Criteria and techniques to support the establishment of a semantic dictionary for database interoperability are described. The techniques allow the analysis of conceptual schemas of databases in a federation and the definition and maintenance of concept hierarchies. Similarity-based criteria are used to evaluate concept closeness and, consequently, to generate concept hierarchies. Experimentation of the techniques in the public administration domain is discussed.

Global authorizations in database federations can be derived from local authorizations exported by the databases composing the federation. Particularly, when several component databases participate in the federation and a high number of users and pro

System evolution and re-engineering are becoming increasingly important as the size and complexity of Information Systems continue to grow. The paper addresses the problems related to the development of abstraction mechanisms for building reference conceptual architectures of Information Systems, i.e. standardized descriptions of their most significant concepts, to be used for matching and validating changes to the system.

A methodological approach is presented to guide the definition of reusable components to be exploited for designing applications not from scratch, but by tailoring and adapting existing components. Reusability at the requirements specification level is considered, and tools to define reusable components are illustrated. Reusable components are defined as generic components with associated meta-components providing guidelines for reuse in a given application.>