Automated classification of web pages in hierarchical browsing

Golub, Koraljka; Lykke, Marianne

doi:10.1108/00220410910998915

Cited by 7 publications

(5 citation statements)

References 21 publications

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“…He used a panel of judges to rate assigned index terms both for their appropriateness to the document and their usefulness as access points. Golub and Lykke () evaluated automatic assignment of subject classes from the Engineering Index classification to a collection of 19,000 web pages for use in hierarchical subject browsing. Forty participants performed four controlled search tasks developed following the methodology of Borlund (), using simulated work tasks that represent situational relevance and thus allow for testing subjective relevance in a controlled environment.…”

Section: Evaluating Indexing Quality Directly Through Assessment By Amentioning

confidence: 99%

“…The work task was writing a fairly large report on a given theme. Golub and Lykke () investigated whether it is meaningful to use the Engineering Index classification scheme for browsing, and then, if proven useful, to investigate the performance of an automated classification algorithm based on the classification scheme. A user study was conducted in which users solved four controlled searching tasks in which they (a) browsed the classification scheme to examine the suitability of the classification system for browsing, and (b) judged the correctness of the automatically assigned classes.…”

Section: Evaluating Indexing Quality Indirectly Through Retrieval Permentioning

confidence: 99%

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A framework for evaluating automatic indexing or classification in the context of retrieval

Golub

Soergel

Buchanan

et al. 2015

Asso for Info Science & Tech

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Tools for automatic subject assignment help deal with scale and sustainability in creating and enriching metadata, establishing more connections across and between resources and enhancing consistency. Although some software vendors and experimental researchers claim the tools can replace manual subject indexing, hard scientific evidence of their performance in operating information environments is scarce. A major reason for this is that research is usually conducted in laboratory conditions, excluding the complexities of real‐life systems and situations. The article reviews and discusses issues with existing evaluation approaches such as problems of aboutness and relevance assessments, implying the need to use more than a single “gold standard” method when evaluating indexing and retrieval, and proposes a comprehensive evaluation framework. The framework is informed by a systematic review of the literature on evaluation approaches: evaluating indexing quality directly through assessment by an evaluator or through comparison with a gold standard, evaluating the quality of computer‐assisted indexing directly in the context of an indexing workflow, and evaluating indexing quality indirectly through analyzing retrieval performance.

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Section: Evaluating Indexing Quality Directly Through Assessment By Amentioning

confidence: 99%

Section: Evaluating Indexing Quality Indirectly Through Retrieval Permentioning

confidence: 99%

A framework for evaluating automatic indexing or classification in the context of retrieval

Golub

Soergel

Buchanan

et al. 2015

Asso for Info Science & Tech

Self Cite

View full text Add to dashboard Cite

show abstract

“…Organizing resources into a hierarchical structure for subject browsing has been recognized as an important tool in information-seeking processes (Golub & Lykke, 2009). A hierarchy can offer searchers information on the collection being searched before any interaction happens.…”

Section: Usability Of Hierarchies From a User-oriented Perspectivementioning

confidence: 99%

Enhancing Navigability: An Algorithm for Constructing Tag Trees

Cheng

Luo

2017

Journal of Data and Information Science

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Purpose: This study introduces an algorithm to construct tag trees that can be used as a userfriendly navigation tool for knowledge sharing and retrieval by solving two issues of previous studies, i.e. semantic drift and structural skew.Design/methodology/approach: Inspired by the generality based methods, this study builds tag trees from a co-occurrence tag network and uses the h-degree as a node generality metric. The proposed algorithm is characterized by the following four features: (1) the ancestors should be more representative than the descendants, (2) the semantic meaning along the ancestor-descendant paths needs to be coherent, (3) the children of one parent are collectively exhaustive and mutually exclusive in describing their parent, and (4) tags are roughly evenly distributed to their upper-level parents to avoid structural skew. Findings:The proposed algorithm has been compared with a well-established solution Heymann Tag Tree (HTT). The experimental results using a social tag dataset showed that the proposed algorithm with its default condition outperformed HTT in precision based on Open Directory Project (ODP) classification. It has been verified that h-degree can be applied as a better node generality metric compared with degree centrality. Research limitations:A thorough investigation into the evaluation methodology is needed, including user studies and a set of metrics for evaluating semantic coherence and navigation performance. Practical implications:The algorithm will benefit the use of digital resources by generating a flexible domain knowledge structure that is easy to navigate. It could be used to manage multiple resource collections even without social annotations since tags can be keywords created by authors or experts, as well as automatically extracted from text. Originality/value: Few previous studies paid attention to the issue of whether the tagging systems are easy to navigate for users. The contributions of this study are twofold: (1) an algorithm was developed to construct tag trees with consideration given to both semantic coherence and structural balance and (2) the effectiveness of a node generality metric, h-degree, was investigated in a tag co-occurrence network.

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“…As we see in the non match example above, this is particularly the case with discipline-based classifications such as DDC, where a subject can occur in very different hierarchies, depending on the focus of the cataloger. This issue was noted in a study by Golub and Lykke (2009), who combined a study of user hierarchical browsing behavior via automatically assigned classes by a document classification algorithm for a set of engineering web pages, with an investigation of the correctness of automatically assigned classes assigned as perceived by the users. They reported differences in the human judgments, and that some web pages posed particular issues for judgment of appropriate classes due to a lack of text.…”

mentioning

confidence: 99%

Augmenting Dublin Core digital library metadata with Dewey Decimal Classification

Khoo

Ahn

Binding

et al. 2015

Journal of Documentation

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Purpose – The purpose of this paper is to describe a new approach to a well-known problem for digital libraries, how to search across multiple unrelated libraries with a single query. Design/methodology/approach – The approach involves creating new Dewey Decimal Classification terms and numbers from existing Dublin Core records. In total, 263,550 records were harvested from three digital libraries. Weighted key terms were extracted from the title, description and subject fields of each record. Ranked DDC classes were automatically generated from these key terms by considering DDC hierarchies via a series of filtering and aggregation stages. A mean reciprocal ranking evaluation compared a sample of 49 generated classes against DDC classes created by a trained librarian for the same records. Findings – The best results combined weighted key terms from the title, description and subject fields. Performance declines with increased specificity of DDC level. The results compare favorably with similar studies. Research limitations/implications – The metadata harvest required manual intervention and the evaluation was resource intensive. Future research will look at evaluation methodologies that take account of issues of consistency and ecological validity. Practical implications – The method does not require training data and is easily scalable. The pipeline can be customized for individual use cases, for example, recall or precision enhancing. Social implications – The approach can provide centralized access to information from multiple domains currently provided by individual digital libraries. Originality/value – The approach addresses metadata normalization in the context of web resources. The automatic classification approach accounts for matches within hierarchies, aggregating lower level matches to broader parents and thus approximates the practices of a human cataloger.

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Automated classification of web pages in hierarchical browsing

Abstract: PreprintThis is the submitted version of a paper published in Journal of Documentation.Citation for the original published paper (version of record):

Cited by 7 publications

References 21 publications

A framework for evaluating automatic indexing or classification in the context of retrieval

A framework for evaluating automatic indexing or classification in the context of retrieval

Enhancing Navigability: An Algorithm for Constructing Tag Trees

Augmenting Dublin Core digital library metadata with Dewey Decimal Classification

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