{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,19]],"date-time":"2026-01-19T08:23:14Z","timestamp":1768810994727,"version":"3.49.0"},"reference-count":47,"publisher":"Emerald","issue":"3","license":[{"start":{"date-parts":[[2022,6,8]],"date-time":"2022-06-08T00:00:00Z","timestamp":1654646400000},"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>Problem-solving\u201d is the most crucial key insight of scientific research. This study focuses on constructing the \u201cproblem-solving\u201d knowledge graph of scientific domains by extracting four entity relation types: problem-solving, problem hierarchy, solution hierarchy and association.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Design\/methodology\/approach<\/jats:title><jats:p>This paper presents a low-cost method for identifying these relationships in scientific papers based on word analogy. The problem-solving and hierarchical relations are represented as offset vectors of the head and tail entities and then classified by referencing a small set of predefined entity relations.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Findings<\/jats:title><jats:p>This paper presents an experiment with artificial intelligence papers from the Web of Science and achieved good performance. The F1 scores of entity relation types problem hierarchy, problem-solving and solution hierarchy, which were 0.823, 0.815 and 0.748, respectively. This paper used computer vision as an example to demonstrate the application of the extracted relations in constructing domain knowledge graphs and revealing historical research trends.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Originality\/value<\/jats:title><jats:p>This paper uses an approach that is highly efficient and has a good generalization ability. Instead of relying on a large-scale manually annotated corpus, it only requires a small set of entity relations that can be easily extracted from external knowledge resources.<\/jats:p><\/jats:sec>","DOI":"10.1108\/ajim-03-2022-0129","type":"journal-article","created":{"date-parts":[[2022,6,8]],"date-time":"2022-06-08T02:20:52Z","timestamp":1654654852000},"page":"481-499","source":"Crossref","is-referenced-by-count":14,"title":["Extracting entity relations for \u201cproblem-solving\u201d knowledge graph of scientific domains using word analogy"],"prefix":"10.1108","volume":"75","author":[{"given":"Guo","family":"Chen","sequence":"first","affiliation":[]},{"given":"Jiabin","family":"Peng","sequence":"additional","affiliation":[]},{"given":"Tianxiang","family":"Xu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5485-1407","authenticated-orcid":false,"given":"Lu","family":"Xiao","sequence":"additional","affiliation":[]}],"member":"140","published-online":{"date-parts":[[2022,6,8]]},"reference":[{"key":"key2023061902085207100_ref001","first-page":"592","article-title":"The AI2 system at SemEval-2017 Task 10 (ScienceIE): semi-supervised end-to-end entity and relation extraction","year":"2017"},{"key":"key2023061902085207100_ref002","first-page":"546","article-title":"SemEval 2017 task 10: ScienceIE - 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