Automatic keyword assignment system for medical research articles using nearest-neighbor searches

Basit Öğe Kaydı
dc.contributor.authorDilmaç, Fatih
dc.contributor.authorAlpkoçak, Adil
dc.date.accessioned2023-03-22T19:47:21Z
dc.date.available2023-03-22T19:47:21Z
dc.date.issued2022
dc.departmentBelirleneceken_US
dc.description.abstractAssigning accurate keywords to research articles is increasingly important concern. Keywords should be selected meticulously to describe the article well since keywords play an important role in matching readers with research articles in order to reach a bigger audience. So, improper selection of keywords may result in less attraction to readers which results in degradation in its audience. Hence, we designed and developed an automatic keyword assignment system (AKAS) for research articles based on k-nearest neighbor (k-NN) and threshold-nearest neighbor (t-NN) accompanied with information retrieval systems (IRS), which is a corpus-based method by utilizing IRS using the Medline dataset in PubMed. First, AKAS accepts an abstract of the research article or a particular text as a query to the IRS. Next, the IRS returns a ranked list of articles to the given query. Then, we selected a set of documents from this list using two different methods, which are k-NN and t-NN representing the first k documents and documents whose similarity is greater than the threshold value of t, respectively. To evaluate our proposed system, we conducted a set of experiments on a selected subset of 458,594 PubMed articles. Then, we performed an experiment to observe the performance of AKAS results by comparing with the original keywords assigned by authors. The results we obtained showed that our system suggests keywords more than 55% match in terms of F-score. We presented both methods we used and results of experiments, in detail.en_US
dc.identifier.doi10.55730/1300-0632.3907
dc.identifier.endpage1838en_US
dc.identifier.issn1300-0632
dc.identifier.issn1303-6203
dc.identifier.issue5en_US
dc.identifier.scopus2-s2.0-85139281329en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.startpage1821en_US
dc.identifier.urihttps://doi.org/10.55730/1300-0632.3907
dc.identifier.urihttps://hdl.handle.net/20.500.14034/650
dc.identifier.volume30en_US
dc.identifier.wosWOS:000904725600010en_US
dc.identifier.wosqualityQ4en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherScientific And Technological Research Council Turkeyen_US
dc.relation.journalTurkish Journal Of Electrical Engineering And Computer Sciencesen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectAutomatic keyword assignmenten_US
dc.subjectinformation retrievalen_US
dc.subjectk-nearest neighbors,t-nearest neighborsen_US
dc.subjectPubMeden_US
dc.subjectExtractionen_US
dc.subjectClassificationen_US
dc.subjectCategorizationen_US
dc.titleAutomatic keyword assignment system for medical research articles using nearest-neighbor searchesen_US
dc.typeArticleen_US

Dosyalar

Orijinal paket
Listeleniyor 1 - 1 / 1
Küçük Resim
İsim:
Automatic keyword assignment system for medical research articles.pdf
Boyut:
397.52 KB
Biçim:
Adobe Portable Document Format
Açıklama:
Tam metin / Full text
İndir

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
Bilgisayar Mühendisliği Bölümü Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu