Search (7 results, page 1 of 1)

Zhang, L.; Liu, Q.L.; Zhang, J.; Wang, H.F.; Pan, Y.; Yu, Y.: Semplore: an IR approach to scalable hybrid query of Semantic Web data (2007) 0.04

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Zhang, L.; Lu, W.; Yang, J.: LAGOS-AND : a large gold standard dataset for scholarly author name disambiguation (2023) 0.04
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Abstract

In this article, we present a method to automatically build large labeled datasets for the author ambiguity problem in the academic world by leveraging the authoritative academic resources, ORCID and DOI. Using the method, we built LAGOS-AND, two large, gold-standard sub-datasets for author name disambiguation (AND), of which LAGOS-AND-BLOCK is created for clustering-based AND research and LAGOS-AND-PAIRWISE is created for classification-based AND research. Our LAGOS-AND datasets are substantially different from the existing ones. The initial versions of the datasets (v1.0, released in February 2021) include 7.5 M citations authored by 798 K unique authors (LAGOS-AND-BLOCK) and close to 1 M instances (LAGOS-AND-PAIRWISE). And both datasets show close similarities to the whole Microsoft Academic Graph (MAG) across validations of six facets. In building the datasets, we reveal the variation degrees of last names in three literature databases, PubMed, MAG, and Semantic Scholar, by comparing author names hosted to the authors' official last names shown on the ORCID pages. Furthermore, we evaluate several baseline disambiguation methods as well as the MAG's author IDs system on our datasets, and the evaluation helps identify several interesting findings. We hope the datasets and findings will bring new insights for future studies. The code and datasets are publicly available.

Date

22. 1.2023 18:40:36

Zhang, L.; Pan, Y.; Zhang, T.: Focused named entity recognition using machine learning (2004) 0.02

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Date: 15.10.2005 19:57:22

Zhang, L.; Thijs, B.; Glänzel, W.: What does scientometrics share with other "metrics" sciences? (2013) 0.01

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Abstract: In this article, the authors answer the question of whether the field of scientometrics/bibliometrics shares essential characteristics of "metrics" sciences. To achieve this objective, the citation network of seven selected metrics and their information environment is analyzed.

Zhang, L.: Grasping the structure of journal articles : utilizing the functions of information units (2012) 0.01

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Date: 6. 4.2012 18:43:22

Lee, H.-L.; Zhang, L.: Tracing the conceptions and treatment of genre in Anglo-American cataloging (2013) 0.01
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Abstract

This study examines the conceptions and treatment of genre in four sets of modern Anglo-American cataloging rules spanning 171 years. Genre-related rules are first identified through "genre(s)," "form(s)," and "type(s)" keyword searches, and manual examination of the contents, then analyzed by level of treatment genre receives and by user tasks, as defined in the Functional Requirements for Bibliographic Records. While genre is found to be sporadically addressed across the rules, its significance has increased over time. In conclusion, the authors call for a rigorous and functional definition of genre and an integrated approach to genre in cataloging.
Liu, X.; Guo, C.; Zhang, L.: Scholar metadata and knowledge generation with human and artificial intelligence (2014) 0.01
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Abstract

Scholar metadata have traditionally centered on descriptive representations, which have been used as a foundation for scholarly publication repositories and academic information retrieval systems. In this article, we propose innovative and economic methods of generating knowledge-based structural metadata (structural keywords) using a combination of natural language processing-based machine-learning techniques and human intelligence. By allowing low-barrier participation through a social media system, scholars (both as authors and users) can participate in the metadata editing and enhancing process and benefit from more accurate and effective information retrieval. Our experimental web system ScholarWiki uses machine learning techniques, which automatically produce increasingly refined metadata by learning from the structural metadata contributed by scholars. The cumulated structural metadata add intelligence and automatically enhance and update recursively the quality of metadata, wiki pages, and the machine-learning model.

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