Linking folksonomy to Library of Congress subject headings: an exploratory study

Yi, Kwan; Chan, Lois Mai

doi:10.1108/00220410910998906

Cited by 69 publications

(76 citation statements)

References 12 publications

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“…Yi and Chan () chose to ignore subdivided authority records because they felt that associating a subdivided authority record with its main heading would not reflect a true broader relation (Yi & Chan, ). Our position is that the LCSH structure should reflect the existence of some kind of relation between a main heading and its subdivided versions.…”

Section: Discussionmentioning

confidence: 99%

“…The LCSH hierarchy cannot be called a tree since it does not adhere to these structural rules. Specifically: it is a set of independent branches without a common root node (Yi & Chan, ); in other words, there is a large number of orphan authority records that have no connection to the structure (see review by Shubert, ; Sinkankas, ); main headings often have multiple parents; it may contain cycles. …”

Section: Introductionmentioning

confidence: 99%

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Constructing a true LCSH tree of a science and engineering collection

Julien

Tirilly

Leide

et al. 2012

J Am Soc Inf Sci Tec

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The Library of Congress Subject Headings (LCSH) is a subject structure used to index large library collections throughout the world. Browsing a collection through LCSH is difficult using current online tools in part because users cannot explore the structure using their existing experience navigating file hierarchies on their hard drives. This is due to inconsistencies in the LCSH structure, which does not adhere to the specific rules defining tree structures. This article proposes a method to adapt the LCSH structure to reflect a real‐world collection from the domain of science and engineering. This structure is transformed into a valid tree structure using an automatic process. The analysis of the resulting LCSH tree shows a large and complex structure. The analysis of the distribution of information within the LCSH tree reveals a power law distribution where the vast majority of subjects contain few information items and a few subjects contain the vast majority of the collection.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Constructing a true LCSH tree of a science and engineering collection

Julien

Tirilly

Leide

et al. 2012

J Am Soc Inf Sci Tec

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“…Similar searches across library and information science literature turn up some explorative research on the possibilities of mapping LC data, but again no animated stack maps are found. 4 There is a product licensed by Bowker Inc. called StackMap that can be linked to catalog search results. When a user clicks on the map link next to a call number result, a map is displayed with the destination stack highlighted, but the information provided is locational only.…”

Section: Other Stack Maps and Related Technologiesmentioning

confidence: 99%

Animated Subject Maps for Book Collections

Donahue

2013

ITAL

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LC CLASSIFICATION, BOOKS, AND LIBRARY STACKSDuring the last few decades of technological evolution in libraries, we have helped facilitate a seismic shift from print-based to digital research. Our library websites are jammed with electronic resources, digital collection components, database links, virtual reference assistance, online tutorials, and mobile apps. Collection budgets too have shifted from a print to electronic focus. Many libraries are now spending less than 20 percent of their material budgets on print monographs. And yet, our stacks are still filled with books that often take up more than fifty percent of our library spaces. Knowledge organization schemas have also evolved in libraries. We have subject lists to help users to decide on which databases to select that reflect current disciplines and majors in higher education. Internal database navigation continues to evolve in terms of limits, fields, and subject searching. Web searching is based on the contemporary keyword approach where "everything is miscellaneous" and need not be organized, but nationwide, billions of books still sit on shelves according to Dewey or Library of Congress classification systems that were initially developed over a century ago. Some say these organizing systems are woefully antiquated and do not reflect our contemporary post-modern realities, though they still amply serve their purpose to assign call number locations for our books. We hear scant little of plans to update these classification schemes. Why invest more time, energy, and resources on revamped organization schemes for libraries?

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“…Explicit semantic analysis (ESA) uses concept vector representation to explicitly represent the meanings of a text fragment. Similar techniques were used by other researchers for solving practical problems such as synonymy and polysemy, bringing order to folksonomies (user vocabulary), learning personalized ontology, and document summarization . That is, conceptual annotation can help a user to have a rough idea about the hidden meanings of the discovered patterns.…”

Section: Introductionmentioning

confidence: 97%

Conceptual annotation of text patterns

Bashar

Shen

et al. 2017

Computational Intelligence

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Patterns are used as a fundamental means for analyzing data in many data mining applications. Many efficient techniques have been developed to discover patterns. However, the excessive number of discovered patterns and the lack of semantic information have made it difficult for a user to interpret and explore the patterns. A rough idea of the meanings of patterns can benefit the user in the process of exploring them. To address this issue, this paper presents a model for automatically annotating patterns with concepts. In addition, in a given context, the relative importance of each term that defines a concept is not the same. To define a context, there are a number of related information sources, such as documents, patterns, concepts, and an ontology. The question is which information sources are useful for estimating the relative importance of the terms? Should the most accurate one to be focused on or all of them be used to define the context? This research investigated these questions and defined an effective annotation context to estimate the relative importance of the terms, where the aim is to improve the performance of a machine that relies on the subject matter of a pattern set. The model is evaluated by comparing it with different baseline models on 2 standard datasets. The results show that the performance of the proposed model is significantly better.

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Linking folksonomy to Library of Congress subject headings: an exploratory study

Cited by 69 publications

References 12 publications

Constructing a true LCSH tree of a science and engineering collection

Constructing a true LCSH tree of a science and engineering collection

Animated Subject Maps for Book Collections

Conceptual annotation of text patterns

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