From the web of data to a world of action

This is the author’s version of a work that was accepted for publication in Web Semantics: Science, Services and Agents on the World Wide Web. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Web Semantics: Science, Services and Agents on the World Wide Web 8.4 (2010): 10.1016/j.websem.2010.04.007This paper takes as its premise that the web is a place of action, not just information, and that the purpose of global data is to serve human needs. The paper presents several component technologies, which together work towards a vision where many small micro-applications can be threaded together using automated assistance to enable a unified and rich interaction. These technologies include data detector technology to enable any text to become a start point of semantic interaction; annotations for web-based services so that they can link data to potential actions; spreading activation over personal ontologies, to allow modelling of context; algorithms for automatically inferring 'typing' of web-form input data based on previous user inputs; and early work on inferring task structures from action traces. Some of these have already been integrated within an experimental web-based (extended) bookmarking tool, Snip!t, and a prototype desktop application On Time, and the paper discusses how the components could be more fully, yet more openly, linked in terms of both architecture and interaction. As well as contributing to the goal of an action and activity-focused web, the work also exposes a number of broader issues, theoretical, practical, social and economic, for the Semantic Web.Parts of this work were supported by the Information Society Technologies (IST) Program of the European Commission as part of the DELOS Network of Excellence on Digital Libraries (Contract G038- 507618). Thanks also to Emanuele Tracanna, Marco Piva, and Raffaele Giuliano for their work on On Time

Knowledge-Intensive Processes: Characteristics, Requirements and Analysis of Contemporary Approaches

Engineering of knowledge-intensive processes (KiPs) is far from being mastered, since they are genuinely knowledge- and data-centric, and require substantial flexibility, at both design- and run-time. In this work, starting from a scientific literature analysis in the area of KiPs and from three real-world domains and application scenarios, we provide a precise characterization of KiPs. Furthermore, we devise some general requirements related to KiPs management and execution. Such requirements contribute to the definition of an evaluation framework to assess current system support for KiPs. To this end, we present a critical analysis on a number of existing process-oriented approaches by discussing their efficacy against the requirements

Personal Knowledge Models with Semantic Technologies

Conceptual Data Structures (CDS) is a unified meta-model for representing knowledge cues in varying degrees of granularity, structuredness, and formality. CDS consists of: (1) A simple, expressive data-model; (2) A relation ontology which unifies the relations found in cognitive models of personal knowledge management tools, e. g., documents, mind-maps, hypertext, or semantic wikis. (3) An interchange format for structured text. Implemented prototypes have been evaluated

On the mining of artful processes

Artful processes are those processes in which the experience, intuition, and knowledge of the actors are the key factors in determining the decision making. These knowledge-intensive processes are typically carried out by the “knowledge workers”, such as professors, managers, researchers. They are often scarcely formalised or completely unknown a priori, and depend on the skills, experience, and judgment of the primary actors. Artful processes have goals and methods that change quickly over time, making them difficult to codify in the context of an enterprise application. Knowledge workers cannot be realistically expected to instruct the assistive system by modelling their artful processes: it would be time-consuming both in the initial definition and in the potential continuous revisions. To make things worse, time is the crucial resource that usually knowledge workers indeed lack. Despite the advent of structured case management tools, many enterprise processes are still “run” over emails. Thus, reverse engineering workflows of such processes and their integration with artefacts and other structured processes can accurately depict the enterprise’s process landscape. A system able to infer the models of the processes laying behind the email messages exchanged would be valuable and the result could materialise almost freely. This is the purpose of our approach, which is the core of this thesis and is named MailOfMine. Its investigation mainly resides in the Machine Learning area. More specifically, it relates to Information Retrieval (IR) and Process Mining (PM). We adopted well-known IR techniques in order to extract the activities out of the email messages. We propose a new algorithm for PM in order to discover the temporal rules that the activities adhere to: MINERful. The set of such rules, intended as temporal constraints, constitute the so called declarative modelling of workflows. Declarative models differ from the imperative in that they do not explicitly represent every possible execution that a process can be enacted through, i.e., there is no graph-like structure determining the whole evolution of a process instance, from the beginning to the end. They establish a set of constraints that must hold true, whatever the evolution of the process instance will be. What is not explicitly declared to be respected, is allowed. The reader can easily see that it is better suited to processes subject to frequent changes, with respect to the classical approach. From a more abstract perspective, this work challenges the problem of discovering highly flexible workflows (such as artful processes), out of semi-structured information (such as email messages)

Task-centred information management

The goal of DELOS Task 4.8 Task-centered Information Management is to provide the user with a Task-centered Information Management system (TIM), which automates user's most frequent activities, by exploiting the collection of personal documents. In previous work we have explored the issue of managing personal data by enriching them with semantics according to a Personal Ontology, i.e. a user-tailored description of her domain of interest. Moreover, we have proposed a task specification language and a top-down approach to task inference, where the user specifies main aspects of the tasks using forms of declarative scripting. Recently, we have addressed new challenging issues related to TIM user's task inference. More precisely, the first main contribution of this paper is the investigation of task inference theoretical issues. In particular, we show how the use of the Personal Ontology helps for computing simple task inference. The second contribution is an architecture for the system that implements simple task inference. In the current phase we are implementing a prototype for TIM whose architecture is the one presented in this paper

Task-centred information management

The goal of DELOS Task 4.8 Task-centered Information Management is to provide the user with a Task-centered Information Management system (TIM), which automates user's most frequent activities, by exploiting the collection of personal documents. In previous work we have explored the issue of managing personal data by enriching them with semantics according to a Personal Ontology, i.e. a user-tailored description of her domain of interest. Moreover, we have proposed a task specification language and a top-down approach to task inference, where the user specifies main aspects of the tasks using forms of declarative scripting. Recently, we have addressed new challenging issues related to TIM user's task inference. More precisely, the first main contribution of this paper is the investigation of task inference theoretical issues. In particular, we show how the use of the Personal Ontology helps for computing simple task inference. The second contribution is an architecture for the system that implements simple task inference. In the current phase we are implementing a prototype for TIM whose architecture is the one presented in this paper. © Springer-Verlag Berlin Heidelberg 2007