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Isaac, A.; Wang, S.; Zinn, C.; Matthezing, H.; Meij, L. van der; Schlobach, S.: Evaluating thesaurus alignments for semantic interoperability in the library domain (2009)
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Zhang, L.; Wang, S.; Liu, B.: Deep learning for sentiment analysis : a survey (2018)
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Ren, P.; Chen, Z.; Ma, J.; Zhang, Z.; Si, L.; Wang, S.: Detecting temporal patterns of user queries (2017)
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Wang, S.; Isaac, A.; Schlobach, S.; Meij, L. van der; Schopman, B.: Instance-based semantic interoperability in the cultural heritage (2012)
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Wang, S.; Ma, Y.; Mao, J.; Bai, Y.; Liang, Z.; Li, G.: Quantifying scientific breakthroughs by a novel disruption indicator based on knowledge entities : On the rise of scrape-and-report scholarship in online reviews research (2023)
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- Date
- 22. 1.2023 18:37:33
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Wang, S.; Koopman, R.: Embed first, then predict (2019)
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- Abstract
- Automatic subject prediction is a desirable feature for modern digital library systems, as manual indexing can no longer cope with the rapid growth of digital collections. It is also desirable to be able to identify a small set of entities (e.g., authors, citations, bibliographic records) which are most relevant to a query. This gets more difficult when the amount of data increases dramatically. Data sparsity and model scalability are the major challenges to solving this type of extreme multilabel classification problem automatically. In this paper, we propose to address this problem in two steps: we first embed different types of entities into the same semantic space, where similarity could be computed easily; second, we propose a novel non-parametric method to identify the most relevant entities in addition to direct semantic similarities. We show how effectively this approach predicts even very specialised subjects, which are associated with few documents in the training set and are more problematic for a classifier.
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Xie, I.; Babu, R.; Lee, H.S.; Wang, S.; Lee, T.H.: Orientation tactics and associated factors in the digital library environment : comparison between blind and sighted users (2021)
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- Abstract
- This is the first study that compares types of orientation tactics that blind and sighted users applied in their initial interactions with a digital library (DL) and the associated factors. Multiple methods were employed for data collection: questionnaires, think-aloud protocols, and transaction logs. The paper identifies seven types of orientation tactics applied by the two groups of users. While sighted users focused on skimming DL content, blind users concentrated on exploring DL structure. Moreover, the authors discovered 13 types of system, user, and interaction factors that led to the use of orientation tactics. More system factors than user factors affect blind users' tactics in browsing DL structures. The findings of this study support the social model that the sight-centered design of DLs, rather than blind users' disability, prohibits them from effectively interacting with a DL. Simultaneously, the results reveal the limitation of existing interactive information retrieval models that do not take people with disabilities into consideration. DL design implications are discussed based on the identified factors.
