Breaking free from your information prison: A recommender based on semantically enriched context descriptions

Lutz, Jonas; Thönssen, Barbara; Witschel, Hans Friedrich

doi:10.1109/es.2013.6690092

Cited by 3 publications

(5 citation statements)

References 16 publications

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“…The ERM technology requirement models specified by Governmental/National Archives for ERM implementations might have constrained technology innovation in ERM as vendors are mandated to provide compliant software to qualify for tender (Joseph, 2008), leading to fewer innovative features in standard ERM tools. For example, automatic document filing is not a part of standard, model-specified ERMS, but is a feature current technology can make available (Lutz et al, 2013). SharePoint disrupted the ERMS software space as Microsoft leveraged its partnership alliances to attain a dominant status (Joseph, 2008).…”

Section: Discussionmentioning

confidence: 99%

“…In a project called SEEK! sem, Lutz et al (2013) created a machine-learning solution to solve the Enterprise Portal challenge of manually uploading documents to registry folders. The project used a rule-based recommender algorithm to automatically decide the destination folder and metadata for a document based on its content.…”

Section: Automatic Document Classificationmentioning

confidence: 99%

“…-Implementation planning: Akussah and Asamoah (2015), Chigariro and Khumalo (2018), Dawes (2008), Gunnlaugsdottir (2008), Hase andGalt (2011), Haug (2012), Henriksen et al (2008), Jaakonmäki et al (2018), Katuu (2016), Külcü and Çakmak (2010), Mensah and Adams (2014), Mukred, et. al (2018) -Automatic Document Classification: Lutz et al (2013), Makhlouf (2015) -Content services: Goldschmidt et al (2012)…”

Section: Case Studiesmentioning

confidence: 99%

“…Automatic document classification techniques use algorithms that learn from human classifications, as such, they can perform the classification task as well as humans, once provided adequate training (Calvo, Lee, and Li, 2004). In a project called SEEK!sem, Lutz et al (2013) created a machine-learning solution to solve the Enterprise Portal challenge of manually uploading documents to registry folders. The project used a rule-based recommender algorithm to automatically decide the destination folder and metadata for a document based on its content.…”

Section: Automatic Document Classificationmentioning

confidence: 99%

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Electronic records management – a state of the art review

Oladejo

Hadžidedić

2021

RMJ

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Purpose This paper aims to examine the state of the art in electronic records management (ERM) with the goal of identifying the prevailing research topics, gaps and issues in the field. Design/methodology/approach First, a wide search was performed on academic research databases, limited to the period between 2008–2018. Second, the search results were reviewed for relevance and duplicates. Finally, the study sources were checked against the list of journals and conferences ranked by computing research and education and JourQual. The final sample of 55 selected studies was analyzed in depth. Findings ERM has lost some research momentum due to being deeply embedded in affiliate information systems areas and the changing records management landscape. Additionally, the requirement models specified by Governmental/National Archives might have constrained technology innovation in ERM. A lack of application was identified for the social media research area. Research limitations/implications Limitations were encountered in available search tool functionality and keyword confusion leading to inflated search results. While effort has been made to obtain optimal search results, some relevant articles may have been omitted. Originality/value The last ERM state-of-the-art review was in 1997. A lot has changed since then. This paper will help researchers understand the current state of ERM research, its understudied areas and identify gaps for future studies.

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Section: Discussionmentioning

confidence: 99%

Section: Automatic Document Classificationmentioning

confidence: 99%

Section: Case Studiesmentioning

confidence: 99%

Section: Automatic Document Classificationmentioning

confidence: 99%

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Electronic records management – a state of the art review

Oladejo

Hadžidedić

2021

RMJ

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“…Project no 14604.1 PFES-ES). The work complements previous work on automatically identifying related information objects regardless of their format (Lutz et al 2013). Evaluation was done by knowledge experts based on a comparison of retrieval time for finding given documents in manually and automatic generated information structures.…”

Section: Introductionmentioning

confidence: 99%

Where Did I(T) Put It? - A Holistic Solution to the Automatic Construction of Topic Trees for Navigation

Witschel

Thönssen

Lutz

2014

Proceedings of the International Conference on Knowledge Management and Information Sharing

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Managing information based on hierarchical structures is prevailing, be it by storing documents physically in a file structure like MS explorer or virtually in topic trees as in many web applications. The problem is that the structure evolves over time, created individually and hence reflecting individual opinions of how information objects should be grouped. This leads to time consuming searches and error prone retrieval results since relevant documents might be stored elsewhere. Our approach aims at solving the problem by replacing or complementing the manually created navigation structures by automatically created ones. We consider existing approaches for clustering and labelling and focus on yet unrewarding aspects like having information objects in inner nodes (as it is common in folder hierarchies) and cognitively adequate labelling for textual and non-textual resources. Evaluation was done by knowledge experts based on a comparison of retrieval time for finding given documents in manually and automatic generated information structures and showed the advantage of automatically created topic trees.

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Intranet Exploitation of Social Network Knowledge Intelligence

Underwood¹

2017

Harnessing Social Media as a Knowledge Management Tool

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Intranets are almost as old as the concept of a web site. More than twenty-five years ago the text Business Data Communications closed with a discussion of intranets (Stallings, 1990). Underlying technology improvements in intranets have been incremental; intranets were never seen as killer developments. Yet the popularity of Online Social Networks (OSNs) has led to increased interest in the part OSNs play – or could play – in using intranets to foster knowledge management. This chapter reviews research into how social graphs for an enterprise, team or other collaboration group interacts with the ways intranets have been used to display, collect, curate and disseminate information over the knowledge life cycle. Future roles that OSN-aware intranets could play in emerging technologies, such as process mining, elicitation methods, domain-specific intelligent agents, big data, and just-in-time learning are examined.

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Breaking free from your information prison: A recommender based on semantically enriched context descriptions

Cited by 3 publications

References 16 publications

Electronic records management – a state of the art review

Electronic records management – a state of the art review

Where Did I(T) Put It? - A Holistic Solution to the Automatic Construction of Topic Trees for Navigation

Intranet Exploitation of Social Network Knowledge Intelligence

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