Cross-media Scientific Research Achievements Query based on Ranking Learning

TL;DR

This paper proposes a ranking learning-based method for cross-media scientific research achievement query, addressing the challenges of ambiguous and multi-modal data to improve retrieval accuracy for scientific information.

Contribution

It introduces a novel ranking learning approach tailored for cross-media scientific achievement retrieval, enhancing query effectiveness over traditional keyword-based methods.

Findings

Improved retrieval accuracy for scientific achievements across media types

Effective handling of ambiguous and proper noun-rich scientific data

Development of a cross-media scientific research achievement query system

Abstract

With the advent of the information age, the scale of data on the Internet is getting larger and larger, and it is full of text, images, videos, and other information. Different from social media data and news data, scientific research achievements information has the characteristics of many proper nouns and strong ambiguity. The traditional single-mode query method based on keywords can no longer meet the needs of scientific researchers and managers of the Ministry of Science and Technology. Scientific research project information and scientific research scholar information contain a large amount of valuable scientific research achievement information. Evaluating the output capability of scientific research projects and scientific research teams can effectively assist managers in decision-making. In view of the above background, this paper expounds on the research status from four aspects: characteristic learning of scientific research results, cross-media research results query, ranking learning of scientific research results, and cross-media scientific research achievement query system.