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Book ChapterDOI

Task-centred information management

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TLDR
The first main contribution of this paper is the investigation of task inference theoretical issues, and it is shown how the use of the Personal Ontology helps for computing simple task inference.
Abstract
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.

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Journal ArticleDOI

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

TL;DR: This work provides a precise characterization of KiPs and devise some general requirements related to KiPs management and execution that contribute to the definition of an evaluation framework to assess current system support for KiPs.
Journal ArticleDOI

Ontologies and the brain: Using spreading activation through ontologies to support personal interaction

TL;DR: The use of ontologies as a long-term knowledge store for PIM-related information, and the use of spreading activation over ontologies in order to provide context inference to tools that support TIM are proposed.
Book ChapterDOI

Mining Constraints for Artful Processes

TL;DR: This paper focuses on the mining algorithm, able to efficiently compute the set of constraints describing the artful process, and an experimental evaluation of it is reported.
Proceedings ArticleDOI

The personal project planner: planning to organize personal information

TL;DR: Results of an interim evaluation of the Personal Project Planner are very promising and suggest special directions of focus for limited available prototyping resources.
Book ChapterDOI

MailOfMine - Analyzing Mail Messages for Mining Artful Collaborative Processes

TL;DR: The MailOfMine approach is proposed, to automatically build, on top of a collection of email messages, a set of workflow models that represent the artful processes laying behind the knowledge workers activities.
References
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Book

Watch what I do: programming by demonstration

TL;DR: Part 1 Systems: Pygmalion tinker a predictive calculator rehearsal world smallStar peridot metamouse TELS eager garnet the Turvy experience chimera the geometer's sketchpad tourmaline a history-based macro by example system mondrian triggers the AIDE project.
Book ChapterDOI

Linking data to ontologies

TL;DR: This paper presents a new ontology language, based on Description Logics, that is particularly suited to reason with large amounts of instances and a novel mapping language that is able to deal with the so-called impedance mismatch problem.
Book

Your Wish is My Command: Programming By Example

TL;DR: This book discusses programming by example for information agents, domain-independent programming by demonstration in existing applications, and how to generalize by Removing Detail.
Proceedings ArticleDOI

EAGER: programming repetitive tasks by example

TL;DR: Abstract Eager utilizes a new interface technique, called anticipation, to show how it has generalized: when it detects a repetitive activity, it highlights menus and objects on the screen to indicate what it expects the user to do next.
Proceedings ArticleDOI

Active preference learning for personalized calendar scheduling assistance

TL;DR: The experimental results provide evidence of PLIANT's ability to learn user preferences under various conditions and reveal the tradeoffs made by the different active learning selection strategies.
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