Information Retrieval and Social Tagging for Digital Libraries Using Formal Concept Analysis

Eklund, Peter; Goodall, Peter; Wray, Tim

doi:10.1109/rivf.2010.5633402

Cited by 10 publications

(5 citation statements)

References 19 publications

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“…The consistency and interoperability of metadata is a primary challenge in WDL. Eklund and Wray (Eklund & Wray, 2010) reveal that the inconsistency of metadata can seriously affect the quality of results in search queries. However, it is difficult to collect consistent metadata for WDL.…”

Section: Case Study: the World Digital Librarymentioning

confidence: 99%

What Are the Core Elements Necessary for Effective Digital Asset and Media Management Practice?

Xing¹

2021

JSS

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Section: Case Study: the World Digital Librarymentioning

confidence: 99%

What Are the Core Elements Necessary for Effective Digital Asset and Media Management Practice?

Xing¹

2021

JSS

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“…This application of Formal Concept Analysis illustrates its interest for the analysis of information carried in a social network. Another interesting example is given in (Ducassé et al, 2011) and in (Eklund and Wray, 2010). The following section describes another application, which focuses on the members of the network.…”

Section: Figurementioning

confidence: 99%

Advances in FCA-based Applications for Social Networks Analysis

Aufaure

Grand

2013

International Journal of Conceptual Structures and Smart Applications

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Concept lattices have been widely used for various purposes in many different applications since the 1980s. Recent applications of Formal Concept Analysis include extensions of traditional FCA applications such as data and text mining, machine learning and knowledge management. Progress has also recently been made in software engineering, Semantic Web and databases. New applications have also emerged in the fields of healthcare, ecology, biology, agronomy, business and social networks. This article presents example of successful applications of FCA for Social Networks Analysis. We show the benefit of FCA solutions, as well as their combination with semantics and topology-based approaches. We conclude by presenting FCA-based visualization solutions and open challenges for FCA in the context of large and dynamic data.

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“…Nominal, many-valued, attributes are converted into as many boolean attributes as they have values and continuous/ ordinal attributes are hierarchically scaled or discretized as disjoint ranges of values. These processes are key to conducting FCA on data sets, and have been successfully applied, in a bespoke manner, to data in a number of problem domains, including crime detection (Poelmans et al, 2010;Poelmans et al, 2011), classification (Eklund, 2010), linguistics (Falk, 2010), and gene expression (Kaytoue et al, 2008). However, important issues provide barriers towards wider, more general, applicability and adoption of FCA.…”

Section: Fca Of Data Setsmentioning

confidence: 99%

Discovering Knowledge in Data Using Formal Concept Analysis

Andrews

Orphanides

2013

International Journal of Distributed Systems and Technologies

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Formal Concept Analysis (FCA) has been successfully applied to data in a number of problem domains. However, its use has tended to be on an ad hoc, bespoke basis, relying on FCA experts working closely with domain experts and requiring the production of specialised FCA software for the data analysis. The availability of generalised tools and techniques, that might allow FCA to be applied to data more widely, is limited. Two important issues provide barriers: raw data is not normally in a form suitable for FCA and requires undergoing a process of transformation to make it suitable, and even when converted into a suitable form for FCA, real data sets tend to produce a large number of results that can be difficult to manage and interpret. This article describes how some open-source tools and techniques have been developed and used to address these issues and make FCA more widely available and applicable. Three examples of real data sets, and real problems related to them, are used to illustrate the application of the tools and techniques and demonstrate how FCA can be used as a semantic technology to discover knowledge. Furthermore, it is shown how these tools and techniques enable FCA to deliver a visual and intuitive means of mining large data sets for association and implication rules that complements the semantic analysis. In fact, it transpires that FCA reveals hidden meaning in data that can then be examined in more detail using an FCA approach to traditional data mining methods.

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Information Retrieval and Social Tagging for Digital Libraries Using Formal Concept Analysis

Cited by 10 publications

References 19 publications

What Are the Core Elements Necessary for Effective Digital Asset and Media Management Practice?

What Are the Core Elements Necessary for Effective Digital Asset and Media Management Practice?

Advances in FCA-based Applications for Social Networks Analysis

Discovering Knowledge in Data Using Formal Concept Analysis

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