Bipartite Network of Interest (BNOI): Extending Co-Word Network with Interest of Researchers Using Sensor Data and Corresponding Applications as an Example

Traditional co-word networks do not discriminate keywords of researcher interest from general keywords. Co-word networks are therefore often too general to provide knowledge if interest to domain experts. Inspired by the recent work that uses an automatic method to identify the questions of interest to researchers like problems and solutions, we try to answer a similar question what sensors can be used for what kind of applications, which is great interest in sensor- related fields. By generalizing the specific questions as questions of interest, we built a knowledge network considering researcher interest, called bipartite network of interest (BNOI). Different from a co-word approaches using accurate keywords from a list, BNOI uses classification models to find possible entities of interest. A total of nine feature extraction methods including N-grams, Word2Vec, BERT, etc. were used to extract features to train the classification models, including naive Bayes (NB), support vector machines (SVM) and logistic regression (LR). In addition, a multi-feature fusion strategy and a voting principle (VP) method are applied to assemble the capability of the features and the classification models. Using the abstract text data of 350 remote sensing articles, features are extracted and the models trained. The experiment results show that after removing the biased words and using the ten-fold cross-validation method, the F-measure of sensors and applications are 93.2% and 85.5%, respectively. It is thus demonstrated that researcher questions of interest can be better answered by the constructed BNOI based on classification results, comparedwith the traditional co-word network approach.

Automated summary

Researchers attempt to answer the question of which sensors can be used for what kind of applications by establishing a knowledge network called BNOI that considers researcher interest. They use classification models to find possible entities of interest and employ various feature extraction methods and fusion strategies. Experimental results demonstrate that BNOI, based on classification results, provides more accurate answers to researcher questions of interest compared to traditional co-word network methods.