{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,25]],"date-time":"2026-01-25T04:29:34Z","timestamp":1769315374841,"version":"3.49.0"},"reference-count":66,"publisher":"Emerald","issue":"3","license":[{"start":{"date-parts":[[2023,5,29]],"date-time":"2023-05-29T00:00:00Z","timestamp":1685318400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["AJIM"],"published-print":{"date-parts":[[2023,6,19]]},"abstract":"<jats:sec><jats:title content-type=\"abstract-subheading\">Purpose<\/jats:title><jats:p>This study analyzed the interdisciplinary characteristics of Chinese research studies in library and information science (LIS) measured by knowledge elements extracted through the Lexicon-LSTM model.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Design\/methodology\/approach<\/jats:title><jats:p>Eight research themes were selected for experiment, with a large-scale (<jats:italic>N<\/jats:italic>\u00a0=\u00a011,625) dataset of research papers from the China National Knowledge Infrastructure (CNKI) database constructed. And it is complemented with multiple corpora. Knowledge elements were extracted through a Lexicon-LSTM model. A subject knowledge graph is constructed to support the searching and classification of knowledge elements. An interdisciplinary-weighted average citation index space was constructed for measuring the interdisciplinary characteristics and contributions based on knowledge elements.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Findings<\/jats:title><jats:p>The empirical research shows that the Lexicon-LSTM model has superiority in the accuracy of extracting knowledge elements. In the field of LIS, the interdisciplinary diversity indicator showed an upward trend from 2011 to 2021, while the disciplinary balance and difference indicators showed a downward trend. The knowledge elements of theory and methodology could be used to detect and measure the interdisciplinary characteristics and contributions.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Originality\/value<\/jats:title><jats:p>The extraction of knowledge elements facilitates the discovery of semantic information embedded in academic papers. The knowledge elements were proved feasible for measuring the interdisciplinary characteristics and exploring the changes in the time sequence, which helps for overview the state of the arts and future development trend of the interdisciplinary of research theme in LIS.<\/jats:p><\/jats:sec>","DOI":"10.1108\/ajim-03-2022-0130","type":"journal-article","created":{"date-parts":[[2023,5,26]],"date-time":"2023-05-26T21:58:55Z","timestamp":1685138335000},"page":"589-617","source":"Crossref","is-referenced-by-count":10,"title":["Measuring the interdisciplinary characteristics of Chinese research in library and information science based on knowledge elements"],"prefix":"10.1108","volume":"75","author":[{"given":"Jinxiang","family":"Zeng","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shujin","family":"Cao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6289-9814","authenticated-orcid":false,"given":"Yijin","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pei","family":"Pan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yafang","family":"Cai","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"140","published-online":{"date-parts":[[2023,5,29]]},"reference":[{"issue":"1","key":"key2023061902085725700_ref001","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1109\/MIS.2003.1179189","article-title":"Automatic ontology-based knowledge extraction and tailored biography generation from the web","volume":"18","year":"2003","journal-title":"IEEE Intelligent Systems"},{"issue":"12","key":"key2023061902085725700_ref002","doi-asserted-by":"crossref","first-page":"3095","DOI":"10.1002\/asi.23568","article-title":"Highly cited papers in library and information science (LIS): authors, institutions, and network structures","volume":"67","year":"2016","journal-title":"Journal of the Association for Information Science and Technology"},{"key":"key2023061902085725700_ref003","first-page":"1","article-title":"Rising popularity of interdisciplinary research\u2014an analysis of citation networks","year":"2014"},{"issue":"3","key":"key2023061902085725700_ref004","doi-asserted-by":"crossref","first-page":"2071","DOI":"10.1007\/s11192-015-1762-8","article-title":"Evolution of research subjects in library and information science based on keyword, bibliographical coupling, and co-citation analyses","volume":"105","year":"2015","journal-title":"Scientometrics"},{"key":"key2023061902085725700_ref005","doi-asserted-by":"crossref","first-page":"298","DOI":"10.1109\/HIS.2009.65","article-title":"Medical adverse events classification for domain knowledge extraction","volume-title":"2009 Ninth International Conference on Hybrid Intelligent Systems","year":"2009"},{"issue":"3","key":"key2023061902085725700_ref006","doi-asserted-by":"crossref","first-page":"583","DOI":"10.1016\/j.joi.2013.03.003","article-title":"The distribution of references across texts: some implications for citation analysis","volume":"7","year":"2013","journal-title":"Journal of Informetrics"},{"issue":"3","key":"key2023061902085725700_ref007","doi-asserted-by":"crossref","first-page":"35","DOI":"10.2478\/jdis-2021-0013","article-title":"Automatic key phrase extraction from scientific Chinese medical abstracts based on character-level sequence labeling","volume":"6","year":"2021","journal-title":"Journal of Data and Information Science"},{"key":"key2023061902085725700_ref008","first-page":"239","year":"2016"},{"issue":"9","key":"key2023061902085725700_ref009","first-page":"189","article-title":"Exploring interdisciplinary knowledge discovery methods based on citation keywords","volume":"39","year":"2020","journal-title":"Journal of Intelligence"},{"issue":"6","key":"key2023061902085725700_ref010","article-title":"A rule-based named-entity recognition method for knowledge extraction of evidence-based dietary recommendations","volume":"12","year":"2017","journal-title":"PLoS One"},{"issue":"7","key":"key2023061902085725700_ref011","doi-asserted-by":"crossref","first-page":"768","DOI":"10.1061\/(ASCE)CO.1943-7862.0000646","article-title":"Domain ontology for construction knowledge","volume":"139","year":"2013","journal-title":"Journal of Construction Engineering and Management"},{"issue":"14","key":"key2023061902085725700_ref012","doi-asserted-by":"crossref","first-page":"i37","DOI":"10.1093\/bioinformatics\/btx228","article-title":"Deep learning with word embeddings improves biomedical named entity recognition","volume":"33","year":"2017","journal-title":"Bioinformatics"},{"key":"key2023061902085725700_ref013","doi-asserted-by":"crossref","unstructured":"Han, H.Q., Zhu, L.J., Fu, Y., Xu, S., Zhang, Z.F. and Shi, C.D. 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