Construction of deep resolution and retrieval platform for large scale scientific and technical literature

Wu, Suyan; Li, Wenbo; Jiangrui, Wu

doi:10.1109/icccbda.2018.8386545

Cited by 1 publication

(1 citation statement)

References 2 publications

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“…Suyan et.al. [10] constructed deep resolution and retrieval platform for large scale scientific and technical literature by applying methods of real-time construction of standardized Data Set for citation content analysis and proposing a scientific and technical literature retrieval platform based on Citation content. Yang et.all [11] applied Weighted Term Frequency to develop a scientific literature retrieval model and used simulated annealing algorithm to learn the weighted factor.…”

Section: Related Workmentioning

confidence: 99%

Semantic Information Retrival for Scientific Experimental Papers with Knowlege based Feature Extraction

Mubatada'i¹,

Barakbah²,

Helen³

2019

ISI

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Abstrack-Along with the times, demands for information retrievals in scientific papers have also increased. Regarding experimental scientific papers, researchers have difficulty in searching for information on experimental scientific papers because information retrieval engines have limitations in the search process due to text mining-based feature extraction of the entire text, while experimental types of scientific paper have specific contents, which should have a different treatment in feature extraction. In this paper, we propose a new system for information retrieval on experimental scientific papers. This system consists of 4 main functions: (1) Specific content-based feature extraction, (2) Classification model, (3) Context-based subspace selection, and (4) Context-dependent similarity measurement. In feature extraction, our system extracts feature category in experimental scientific papers with specific content-based features, which are data, problem, method and result. To perform the applicability of our proposed system, we tested 77 papers in the dataset with the Leave-One-Out validation model with several classification algorithm (Nearest Neighbour, Naive Bayes, Support Vector Machine and Decision Tree) and on average performed 66.65% precision rate and accuracy of 76,18% precision rate. We also made the experiment on the similarity, our proposed system performed 79.17% accuracy rate Keywords-Scientific experimental paper, Context-base subspace selection, Context-dependent similarity measurement. Intisari-Seiring dengan perkembangan zaman permintaan pencarian informasi dalam makalah ilmiah juga meningkat. Mesin pencari informasi yang ada saat ini memiliki keterbatasan dalam proses pencarian berdasarkan ekstraksi fitur berbasis text-mining dari seluruh teks, sedangkan jenis makalah ilmiah eksperimental memiliki konten spesifik. Dalam makalah yang kami usulkan sistem untuk pengambilan informasi pada makalah ilmiah eksperimental. Sistem terdiri dari 4 fungsi: (1) Ekstraksi fitur berbasis konten, (2) Model klasifikasi, (3) Pemilihan subruang berbasis konteks, dan (4) Pengukuran kesamaan berdasar pada konteks. Dalam Pemilihan Subruang Berbasis Konteks, sistem melakukan pengurangan dimensi dengan pemilihan subruang berbasis konteks yang dipilih oleh pengguna. Untuk mendapatkan hasil pencarian akhir, kami mengukur kesamaan konteks dengan membangun metrik dataset berdasar konteks ke paper. Untuk melakukan penerapan sistem yang kami usulkan, kami menguji 77 makalah dalam dataset dengan model validasi Leave-One-Out dengan beberapa algoritma klasifikasi (Nearest Neighbor, Naive Bayes, Support Vector Machine, dan Decision Tree) dan rata-rata melakukan presisi 66,65% tingkat dan akurasi tingkat presisi 76,18%. Kami juga melakukan percobaan pada pengukuran kesamaan dengan memberikan queri paper dan konten yang diinginkan (data, hasil, metode, dan masalah) sebagai konteks yang diberikan oleh pengguna. Dalam percobaan pengukuran kesamaan, sistem yang kami usulkan memiliki tingkat akurasi 79,17%.

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Section: Related Workmentioning

confidence: 99%