Domenico Redavid

A formal model of the Semantic Web Service Ontology (WSMO)

Semantic Web Service, one of the most significant research areas within the Semantic Web vision, has attracted increasing attention from both the research community and industry. The Web Service Modelling Ontology (WSMO) has been proposed as an enabling framework for the total/partial automation of the tasks (e.g., discovery, selection, composition, mediation, execution, monitoring, etc.) involved in both intra- and inter-enterprise integration of Web services. To support the standardisation and tool support of WSMO, a formal model of the language is highly desirable. As several variants of WSMO have been proposed by the WSMO community, which are still under development, the syntax and semantics of WSMO should be formally defined to facilitate easy reuse and future development. In this paper, we present a formal Object-Z formal model of WSMO, where different aspects of the language have been precisely defined within one unified framework. This model not only provides a formal unambiguous model which can be used to develop tools and facilitate future development, but as demonstrated in this paper, can be used to identify and eliminate errors present in existing documentation.

A context-based architecture for RDF knowledge bases: approach, implementation and preliminary results

In this paper we present a context-based architecture and implementation for supporting the construction and management of contextualized RDF knowledge bases. The goal of this work is to take explicitly into account any possible contextual dependency of a collection of RDF models, without losing sight of performance and scalability issues. We are illustrating motivations, as well as theoretical background, implementation details and test-results of our latest works.

REDD: an algorithm for redundancy detection in RDF models

The base of Semantic Web specifications is Resource Description Framework (RDF) as a standard for expressing metadata. RDF has a simple object model, allowing for easy design of knowledge bases. This implies that the size of knowledge bases can dramatically increase; therefore, it is necessary to take into account both scalability and space consumption when storing such bases. Some theoretical results related to blank node semantics can be exploited in order to design techniques that optimize, among others, space requirements in storing RDF descriptions. We present an algorithm, called REDD, that exploits these theoretical results and optimizes the space used by a RDF description.

Optimizing RDF storage removing redundancies: an algorithm

Semantic Web relies on Resource Description Framework (RDF). Because of the very simple RDF Model and Syntax, the managing of RDF-based knowledge bases requires to take into account both scalability and storage space consumption. In particular, blank nodes semantics came up recently with very interesting theoretical results that can lead to various techniques that optimize, among others, space requirements in storing RDF descriptions. We present a prototypical evolution of our system called RDFCore that exploits these theoretical results and reduces the storage space for RDF descriptions.

Logic-based incremental process mining in smart environments

Understanding what the user is doing in a Smart Environment is important not only for adapting the environment behavior, e.g. by providing the most appropriate combination of services for the recognized situation, but also for identifying situations that could be problematic for the user. Manually building models of the user processes is a complex, costly and error-prone engineering task. Hence, the interest in automatically learning them from examples of actual procedures. Incremental adaptation of the models, and the ability to express/learn complex conditions on the involved tasks, are also desirable. First-order logic provides a single comprehensive and powerful framework for supporting all of the above. This paper presents a First-Order Logic incremental method for inferring process models, and show its application to the users daily routines, for predicting his needs and comparing the actual situation with the expected one. Promising results have been obtained with both controlled experiments that proved its efficiency and effectiveness, and with a domain-specific dataset.

Predicting Process Behavior in WoMan

In addition to the classical exploitation as a means for checking process enactment conformance, process models may be precious for making various kinds of predictions about the process enactment itself e.g., which activities will be carried out next, or which of a set of candidate processes is actually being executed. These predictions may be much more important, but much more hard to be obtained as well, in less common applications of process mining, such as those related to Ambient Intelligence. Also, the prediction performance may provide indirect indications on the correctness and reliability of a process model. This paper proposes a way to make these kinds of predictions using the WoMan framework for workflow management, that has proved to be able to handle complex processes. Experimental results on different domains suggest that the prediction ability of WoMan is noteworthy and may be useful to support the users in carrying out their processes.

Incremental Learning of Daily Routines as Workflows in a Smart Home Environment

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 environments behavior, as well as for identifying situations that could be problematic for the user. Enabling the environment to exploit models of the users 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 peoples 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.

Towards Dynamic Orchestration of Semantic Web Services

The Semantic Web together with Web services technologies enable new scenarios in which the machines use the Web to provide intelligent services in an autonomus way. The orchestration of Semantic Web Services now can be defined from an abstract perspective where their formal semantics can be exploited by software agents to replace human input. This paper tackles the more difficult use case, automatic composition, providing a complete solution to create and manage service processes in a semantically interoperable environment.

Contextualization of a RDF knowledge base in the VIKEF project

Due to the simplicity of RDF data model and semantics, complex application scenarios in which RDF is used to represent the application data model raise important design issues. Modelling e.g. the temporary evolution, relevance, trust and provenance in Knowledge Bases require more than just a set of universally true statements, without any reference to a situation, a point in time, or generally a context. Our proposed solution is to use the notion of context to separate statements that refer to different contextual information, which could so far not explicitly be tied to the statements. In this paper we describe a practical solution to this problem, which has been implemented in the VIKEF project, which deals with making explicit and intelligently useable information contained in vast collections of documents, databases and metadata repositories.

Sentiment analysis as a text categorization task: A study on feature and algorithm selection for Italian language

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.