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Open access

Chong Chen and Pengcheng Luo

Abstract

Purpose

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.

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.

Open access

Bo Hu, Yu-kun Jin, Wan-jiang Gu, Jun Liu, Hua-qin Qin, Chong Chen and Ying-yu Wang

Open access

Jian-ming Zheng, Ming-quan Chen, Meng-qi Zhu, Ning Li, Qian Li, Xin-yu Wang, Chong Huang and Guang-feng Shi

Abstract

Objective To assess on-treatment serum HBsAg and HBV DNA kinetics in HBeAg-positive CHB patients to predict the efficacy of pegylated interferon (PEG-IFN) in early phase of treatment.

Methods Forty-one treatment-naive HBeAg-positive patients treated with PEG-IFNα 2a at a dose of 180 μg/week for at least 24 weeks were evaluated. Their treatment response was assessed, including normalization of serum ALT, decline of serum HBV DNA and loss of HBeAg.

Results We found that a decrease of HBV DNA level at the 4th week was positively correlated with the decrease of HBV DNA level at the 12th week and 24th week (r = 0.8202, P < 0.0001 and r = 0.6838, P < 0.0001, respectively). We observed that a decrease of HBsAg level at the 4th week was positively correlated with decrease of HBsAg level at the 12th week and 24th week (r = 0.4868, P = 0.0023 and r = 0.4251, P = 0.0109, respectively). A decrease of HBsAg level at the 24th week was positively correlated with the decrease of HBV DNA level at the 24th week (r = 0.5262, P = 0.0024). Serum level of IFN and IFN neutralizing antibody had no relationship with HBV DNA or HBsAg titers kinetics.

Conclusions The decline of serum HBV DNA and hepatitis B surface antigen at the 4th week can be used to predict the response to PEG-IFNα 2a in patients with HBeAg positive chronic hepatitis B.