Diese Datenbank enthält über 40.000 Dokumente zu Themen aus den Bereichen Formalerschließung – Inhaltserschließung – Information Retrieval.
© 2015 W. Gödert, TH Köln, Institut für Informationswissenschaft / Powered by litecat, BIS Oldenburg (Stand: 28. April 2022)
1Chen, L. ; Fang, H.: ¬An automatic method for ex-tracting innovative ideas based on the Scopus® database.
In: Knowledge organization. 46(2019) no.3, S.171-186.
Abstract: The novelty of knowledge claims in a research paper can be considered an evaluation criterion for papers to supplement citations. To provide a foundation for research evaluation from the perspective of innovativeness, we propose an automatic approach for extracting innovative ideas from the abstracts of technology and engineering papers. The approach extracts N-grams as candidates based on part-of-speech tagging and determines whether they are novel by checking the Scopus® database to determine whether they had ever been presented previously. Moreover, we discussed the distributions of innovative ideas in different abstract structures. To improve the performance by excluding noisy N-grams, a list of stopwords and a list of research description characteristics were developed. We selected abstracts of articles published from 2011 to 2017 with the topic of semantic analysis as the experimental texts. Excluding noisy N-grams, considering the distribution of innovative ideas in abstracts, and suitably combining N-grams can effectively improve the performance of automatic innovative idea extraction. Unlike co-word and co-citation analysis, innovative-idea extraction aims to identify the differences in a paper from all previously published papers.
Inhalt: DOI:10.57 71/0943-7444-2019-3-171.
Themenfeld: Informetrie ; Computerlinguistik
2Fang, H.: ¬A discussion of citations from the perspective of the contribution of the cited paper to the citing paper.
In: Journal of the Association for Information Science and Technology. 69(2018) no.12, S.1513-1520.
Abstract: To more reasonably allocate a paper's credit, this article argues that both a paper's authors and references contribute to a given paper. Accordingly, we quantitatively represent the proportion of contributions from each author and reference to a paper. A paper's credit can be allocated among its authors and references based on their contributions. All papers carry innate credit because of publication. If cited, they also carry external credit from the citing papers. The proportion of a paper's credit allocated to references can be regarded as a credit output and serves as an input for these references. In this scenario, only the credit assigned to a paper's authors remains as the paper's deserved credit. The credit of papers can be transferred in a direction opposite that of knowledge diffusion. Via this method, the estimate of an individual reference's contribution incorporates content-based citation analysis, a promising method to differentiate different citations. A paper's deserved credit represents the contribution of the paper's authors to the scientific community via the new knowledge they provide in the paper. Therefore, it is rational to evaluate papers according to their deserved credit, not the credit they carry.
3Fang, H.: Classifying research articles in multidisciplinary sciences journals into subject categories.
In: Knowledge organization. 42(2015) no.3, S.139-153.
Abstract: In the Thomson Reuters Web of Science database, the subject categories of a journal are applied to all articles in the journal. However, many articles in multidisciplinary Sciences journals may only be represented by a small number of subject categories. To provide more accurate information on the research areas of articles in such journals, we can classify articles in these journals into subject categories as defined by Web of Science based on their references. For an article in a multidisciplinary sciences journal, the method counts the subject categories in all of the article's references indexed by Web of Science, and uses the most numerous subject categories of the references to determine the most appropriate classification of the article. We used articles in an issue of Proceedings of the National Academy of Sciences (PNAS) to validate the correctness of the method by comparing the obtained results with the categories of the articles as defined by PNAS and their content. This study shows that the method provides more precise search results for the subject category of interest in bibliometric investigations through recognition of articles in multidisciplinary sciences journals whose work relates to a particular subject category.
Inhalt: Vgl.: http://www.ergon-verlag.de/isko_ko/downloads/ko_42_2015_3.pdf.
Themenfeld: Automatisches Klassifizieren
Objekt: Web of science
4Liu, X. ; Zheng, W. ; Fang, H.: ¬An exploration of ranking models and feedback method for related entity finding.
In: Information processing and management. 49(2013) no.5, S.995-1007.
Abstract: Most existing search engines focus on document retrieval. However, information needs are certainly not limited to finding relevant documents. Instead, a user may want to find relevant entities such as persons and organizations. In this paper, we study the problem of related entity finding. Our goal is to rank entities based on their relevance to a structured query, which specifies an input entity, the type of related entities and the relation between the input and related entities. We first discuss a general probabilistic framework, derive six possible retrieval models to rank the related entities, and then compare these models both analytically and empirically. To further improve performance, we study the problem of feedback in the context of related entity finding. Specifically, we propose a mixture model based feedback method that can utilize the pseudo feedback entities to estimate an enriched model for the relation between the input and related entities. Experimental results over two standard TREC collections show that the derived relation generation model combined with a relation feedback method performs better than other models.
Inhalt: Vgl.: doi: 10.1016/j.ipm.2013.03.004.
Themenfeld: Semantisches Umfeld in Indexierung u. Retrieval ; Retrievalalgorithmen