Retrieval and Ranking of Combining Ontology and Content Attributes for Scientific Document

Abstract

Traditional mathematical search models retrieve scientific documents only by mathematical expressions and their contexts and do not consider the ontological attributes of scientific documents, which result in gaps between the queries and the retrieval results. To solve this problem, a retrieval and ranking model is constructed that synthesizes the information of mathematical expressions with related texts, and the ontology attributes of scientific documents are extracted to further sort the retrieval results. First, the hesitant fuzzy set of mathematical expressions is constructed by using the characteristics of the hesitant fuzzy set to address the multi-attribute problem of mathematical expression matching; then, the similarity of the mathematical expression context sentence is calculated by using the BiLSTM two-way coding feature, and the retrieval result is obtained by synthesizing the similarity between the mathematical expression and the sentence; finally, considering the ontological attributes of scientific documents, the retrieval results are ranked to obtain the final search results. The MAP_10 value of the mathematical expression retrieval results on the Ntcir-Mathir-Wikipedia-Corpus dataset is 0.815, and the average value of the NDCG@10 of the scientific document ranking results is 0.9; these results prove the effectiveness of the scientific document retrieval and ranking method.

Recommendation Method of Power Knowledge Retrieval Based on Graph Neural Network

“…To address the problems of the non-adaptive propagation and non-robustness of graph neural networks (GNNs) in recommender systems, Fan [25] proposed the graph trend filtering networks for recommendations (GTN), which can capture the adaptive reliability of the interactions. Jiang [26] et al constructed a retrieval and ranking model that synthesizes information from mathematical expressions with relevant text, extracts ontological attributes from the scientific literature, and further ranks the retrieval results. Gayar [27] et al proposed an integrated search engine framework that combines the advantages of keyword-based and semantic-ontology-based search engines and solves the problems of the retrieval process, such as unclear retrieval features and a short response time.…”

Semantic Similarity Based on Taxonomies

“…They are the type of resources that are used to accomplish this task and the type of entities that are compared. On the basis of the type of used resources, most of the approaches fall into one of the following categories: corpus-based methods, that use large corpora of natural language texts and leverage co-occurrences of words [12]; knowledge-based methods that rely on structured resources [13]; and hybrid methods that are a mixture of both the mentioned approaches [14].…”