2017 IEEE International Conference on Data Mining Workshops (ICDMW) (2017)
New Orleans, Louisiana, USA
Nov. 18, 2017 to Nov. 21, 2017
In this article we address the problem of expanding the set of papers that researchers encounter when conducting bibliographic research on their scientific work. Using classical search engines or recommender systems in digital libraries, some interesting and relevant articles could be missed if they do not contain the same search key-phrases that the researcher is aware of. We propose a novel model that is based on a supervised active learning over a semantic features transformation of all articles of a given digital library. Our model, named Semantic Search-by-Examples (SSbE), shows better evaluation results over a similar purpose existing method, More-Like-This query, based on the feedback annotation of two domain experts in our experimented use-case. We also introduce a new semantic relatedness evaluation measure to avoid the need of human feedback annotation after the active learning process. The results also show higher diversity and overlapping with related scientific topics which we think can better foster transdisciplinary research.
digital libraries, information retrieval, learning (artificial intelligence), recommender systems, search engines, text analysis
H. T. Al-Natsheh, L. Martinet, F. Muhlenbach, F. Rico and D. A. Zighed, "Semantic Search-by-Examples for Scientific Topic Corpus Expansion in Digital Libraries," 2017 IEEE International Conference on Data Mining Workshops (ICDMW), New Orleans, Louisiana, USA, 2018, pp. 747-756.