<abstract xmlns="http://www.w3.org/1999/xhtml"><sec id="j_jdis-2017-0008_s_007_w2aab2b8c49b1b7b1aab1c15b1Aa"><h3>Purpose</h3><p>This study introduces an algorithm to construct tag trees that can be used as a user-friendly navigation tool for knowledge sharing and retrieval by solving two issues of previous studies, i.e. semantic drift and structural skew.</p></sec><sec id="j_jdis-2017-0008_s_008_w2aab2b8c49b1b7b1aab1c15b2Aa"><h3>Design/methodology/approach</h3><p>Inspired by the generality based methods, this study builds tag trees from a co-occurrence tag network and uses the <italic>h</italic>-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.</p></sec><sec id="j_jdis-2017-0008_s_009_w2aab2b8c49b1b7b1aab1c15b3Aa"><h3>Findings</h3><p>The proposed algorithm has been compared with a well-established solution <italic>Heymann Tag Tree</italic> (<italic>HTT</italic>). The experimental results using a social tag dataset showed that the proposed algorithm with its default condition outperformed <italic>HTT</italic> in precision based on Open Directory Project (ODP) classification. It has been verified that <italic>h</italic>-degree can be applied as a better node generality metric compared with degree centrality.</p></sec><sec id="j_jdis-2017-0008_s_010_w2aab2b8c49b1b7b1aab1c15b4Aa"><h3>Research limitations</h3><p>A thorough investigation into the evaluation methodology is needed, including user studies and a set of metrics for evaluating semantic coherence and navigation performance.</p></sec><sec id="j_jdis-2017-0008_s_011_w2aab2b8c49b1b7b1aab1c15b5Aa"><h3>Practical implications</h3><p>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.</p></sec><sec id="j_jdis-2017-0008_s_012_w2aab2b8c49b1b7b1aab1c15b6Aa"><h3>Originality/value</h3><p>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, <italic>h</italic>-degree, was investigated in a tag co-occurrence network.</p></sec></abstract>

PurposeThis study introduces an algorithm to construct tag trees that can be used as a user-friendly navigation tool for knowledge sharing and retrieval by solving two issues of previous studies, i.e. semantic drift and structural skew.Design/methodology/approachInspired 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.FindingsThe 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 limitationsA thorough investigation into the evaluation methodology is needed, including user studies and a set of metrics for evaluating semantic coherence and navigation performance.Practical implicationsThe 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/valueFew 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.

Enhancing Navigability: An Algorithm for Constructing Tag Trees

Department of Information Management, School of Government Management

Journal of Data and Information Science

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

{"article-title":"Enhancing Navigability: An Algorithm for Constructing Tag Trees"}

PurposeThis study introduces an algorithm to construct tag trees that can be used as a user-friendly navigation tool for knowledge sharing and retrieval by...

Enhancing Navigability: An Algorithm for Constructing Tag Trees

Article Category: Research Paper

Published Online: Mar 21, 2017

Page range: 56 - 75

Received: Nov 29, 2016

Accepted: Feb 22, 2017

DOI: https://doi.org/10.1515/jdis-2017-0008

Keywords
Semantic coherence, Structural balance, Tag tree, Resources navigation, Algorithm

© 2017 Chong Chen & Pengcheng Luo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Enhancing Navigability: An Algorithm for Constructing Tag Trees

Article Category: Research Paper

Published Online: Mar 21, 2017

Page range: 56 - 75

Received: Nov 29, 2016

Accepted: Feb 22, 2017

DOI: https://doi.org/10.1515/jdis-2017-0008

KeywordsSemantic coherence, Structural balance, Tag tree, Resources navigation, Algorithm

© 2017 Chong Chen & Pengcheng Luo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Keywords
Semantic coherence, Structural balance, Tag tree, Resources navigation, Algorithm