Knowledge Management on the Desktop

Drăgan, Laura; Decker, Stefan

doi:10.1007/978-3-642-33876-2_33

Cited by 4 publications

(4 citation statements)

References 6 publications

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“…So far, the system would sooner or later run into scaling problems typically found in SD systems [3]. That is another reason why we started investigating solutions based on human forgetting, which are discussed in the following.…”

Section: Semantic Desktop and Managed Forgettingmentioning

confidence: 99%

Advanced Memory Buoyancy for Forgetful Information Systems

Jilek

Chwalek

Schwarz

et al. 2019

AIS-TES

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Knowledge workers face an ever increasing flood of informa- tion in their daily lives. To counter this and provide better support for information management and knowledge work in general, we have been investigating solutions inspired by human forgetting since 2013. These solutions are based on Semantic Desktop (SD) and Managed Forgetting(MF) technology. A key concept of the latter is the so-called Memory Buoyancy (MB), which is intended to represent an information item’s current value for the user and allows to employ forgetting mechanisms. The SD thus continuously performs information value assessment up- dating MB and triggering respective MF measures. We extended an SD- based organizational memory system, which we have been using in daily work for over seven years now, with MF mechanisms directly embedding them in daily activities, too, and enabling us to test and optimize them in real-world scenarios. In this paper, we first present our initial version of MB and discuss success and failure stories we have been experiencing with it during three years of practical usage. We learned from cognitive psychology that our previous research on context can be beneficial for MF. Thus, we created an advanced MB version especially taking user context, and in particular context switches, into account. These enhancements as well as a first prototypical implementation are presented, too.

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Section: Semantic Desktop and Managed Forgettingmentioning

confidence: 99%

Advanced Memory Buoyancy for Forgetful Information Systems

Jilek

Chwalek

Schwarz

et al. 2019

AIS-TES

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“…From Evidence Collection to User Support Measures. Concerning SemDesk applications, two categories, newly created semantic ones and plug-ins to enhance traditional, non-semantic ones, could be observed so far [7]. We recently presented our idea of Plug-Outs [15], headless plug-ins often just having the rudimentary functionality of sending out in-app events to the SemDesk.…”

Section: The Semantic Desktop As An Ecosystem For Managed Forgettingmentioning

confidence: 99%

Managed Forgetting to Support Information Management and Knowledge Work

et al. 2018

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Trends like digital transformation even intensify the already overwhelming mass of information knowledge workers face in their daily life. To counter this, we have been investigating knowledge work and information management support measures inspired by human forgetting. In this paper, we give an overview of solutions we have found during the last five years as well as challenges that still need to be tackled. Additionally, we share experiences gained with the prototype of a first forgetful information system used 24/7 in our daily work for the last three years. We also address the untapped potential of more explicated user context as well as features inspired by Memory Inhibition, which is our current focus of research.

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“…Since the early 2000s, there has been a lot of research on Semantic Desktop (SD) [8] systems. A survey covering several relevant approaches can be found in [2]. Especially in the last decade, ideas like linking such systems to the internet [3] or the (heterogeneous) IT environment of companies [7] have been pursued.…”

Section: Introductionmentioning

confidence: 99%

Interactive Concept Mining on Personal Data -- Bootstrapping Semantic Services

Schröder,

Jilek,

Dengel

2019

Preprint

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Semantic services (e.g. Semantic Desktops) are still afflicted by a cold start problem: in the beginning, the user's personal information sphere, i.e. files, mails, bookmarks, etc., is not represented by the system. Information extraction tools used to kick-start the system typically create 1:1 representations of the different information items. Higher level concepts, for example found in file names, mail subjects or in the content body of these items, are not extracted. Leaving these concepts out may lead to underperformance, having to many of them (e.g. by making every found term a concept) will clutter the arising knowledge graph with nonhelpful relations. In this paper, we present an interactive concept mining approach proposing concept candidates gathered by exploiting given schemata of usual personal information management applications and analysing the personal information sphere using various metrics. To heed the subjective view of the user, a graphical user interface allows to easily rank and give feedback on proposed concept candidates, thus keeping only those actually considered relevant. A prototypical implementation demonstrates major steps of our approach.

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Knowledge Management on the Desktop

Cited by 4 publications

References 6 publications

Advanced Memory Buoyancy for Forgetful Information Systems

Advanced Memory Buoyancy for Forgetful Information Systems

Managed Forgetting to Support Information Management and Knowledge Work

Interactive Concept Mining on Personal Data -- Bootstrapping Semantic Services

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