he Best of Both Worlds: Highlighting the Synergies of Combining Manual and Automatic Knowledge Organization Methods to Improve Information Search and Discovery

Cleverley, Paul Hugh; Burnett, Simon

doi:10.5771/0943-7444-2015-6-428

Cited by 5 publications

(4 citation statements)

References 35 publications

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“…In the present study, searchers expressed a need for reference to relationships between products, projects, people, business areas, documents, resources and vocabulary variations. The findings further support Cleverley and Burnett's (2015) finding that precision in referencing is more important than referencing for serendipity. The present searchers did not express a need for inspiration, and they expressed a need for support to recall relationships that they knew about or had a presumption about but could remember.…”

Section: Discussionsupporting

confidence: 83%

“…The searchers' explanations corresponded to Cleverley and Burnett's (2015) findings regarding query expansion in enterprise search where clusters of related concepts are useful inspiration. In the present study, searchers expressed a need for reference to relationships between products, projects, people, business areas, documents, resources and vocabulary variations.…”

Section: Discussionmentioning

confidence: 57%

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The role of historical and contextual knowledge in enterprise search

Lykke

Bygholm

Søndergaard

et al. 2021

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PurposeThe purpose of the study is to examine enterprise searching practices across different work areas and work tasks in an enterprise search system in an international biotechnology company.Design/methodology/approachA mixed-method approach studying employees' authentic search activities during a 4-month period by log data, questionnaire survey and interviews. The log data analysed the entire active searcher group, whereas the questionnaire and interviews focused on frequent searchers.FindingsThe three studies provided insight into the searching activities and an understanding of the way searchers used the enterprise search system to search for information as part of their work tasks. The data identified three searcher groups, each with specific search characteristics. Four work task types were identified, and for all four types the searchers applied a tracing searching technique with use of contextual and historical relationships as paths.Practical implicationsThe findings point to the importance of knowledge on historical and contextual relations in enterprise search.Originality/valueThe work sheds new light on enterprise searchers' information search practices. A significant contribution is the identification of a tracing search method used in relation to four essential work task types. Another contribution is the importance of historical and contextual knowledge to support the tracing search and decide what paths to follow.

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Section: Discussionsupporting

confidence: 83%

Section: Discussionmentioning

confidence: 57%

The role of historical and contextual knowledge in enterprise search

Lykke

Bygholm

Søndergaard

et al. 2021

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show abstract

“…For example the phrase "The iPhone6 is expensive but nice" has the opposite polarity to the phrase "The iPhone6 is nice but expensive" in terms of consumer sentiment, evidencing the significance of small word order effects. Despite this, recent literature (Khan and Vorley 2017) tends to focus on inductive statistical clustering to glean insights from 'big data', largely neglecting the value that automatically applying deductive KOS/categories to text (Cleverley and Burnett 2015) might bring to facilitate discovery of new insights. Michel et al (2011) used the term 'culturomics' to define the study of word frequency patterns (corpus analysis) using automated techniques on 'big data'.…”

Section: Content Analysismentioning

confidence: 99%

Using Knowledge Organization Systems to Automatically Detect Forward-looking Sentiment in Company Reports to Infer Social Phenomena

Cleverley¹,

Muir²

2018

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“…The thesaurus, which consists of a word order table and a category table, is the most widely accepted and professional Chinese government thesaurus (Di, 2014). However, owing to the absence of corpus support in the early manual thesaurus (Cleverley & Burnett, 2015), the term relation of the thesaurus is not complete and needs to be optimized urgently. Jia and Wu (2014) transformed the thesaurus with the “Fast” subject words to meet public retrieval needs.…”

Section: Introductionmentioning

confidence: 99%

An ontology automation construction scheme for Chinese e‐government thesaurus optimizing

Wang

Zhang

Deng

et al. 2020

Proc Assoc Inf Sci Technol

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To optimize the term hierarchy in the manual e‐government thesaurus, we combine the mainstream knowledge organization technology to form a complete set of ontology automation construction scheme. We build an e‐government knowledge base by using subject words in the Comprehensive E‐government Thesaurus as the term set and encyclopedia text as the corpus. The specific work includes the extraction of semantic features from the bag‐of‐words model, determination of the number of clusters by linear and nonlinear dimensionality reduction, division of terms by spectral clustering, social network analysis to determine the class label, and storing knowledge ontology via OWL. The recall rate of term hierarchy in the ontology is excellent, indicating the ontology has good knowledge extensibility, and also proving the efficiency of the scheme proposed in this work. Besides, the application model of a term hierarchy in information retrieval can show a richer semantic relation than the original thesaurus to guide the retrieval extension of government information resources.

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he Best of Both Worlds: Highlighting the Synergies of Combining Manual and Automatic Knowledge Organization Methods to Improve Information Search and Discovery

Abstract: This version may not include final proof corrections and does not include published layout or pagination.

Cited by 5 publications

References 35 publications

The role of historical and contextual knowledge in enterprise search

The role of historical and contextual knowledge in enterprise search

Using Knowledge Organization Systems to Automatically Detect Forward-looking Sentiment in Company Reports to Infer Social Phenomena

An ontology automation construction scheme for Chinese e‐government thesaurus optimizing

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