解决学术知识图谱中单位不统一导致的数据难比较问题
Enhancing Information Retrieval in Digital Libraries through Unit Harmonisation in Scholarly Knowledge Graphs
- 用多维筛选框架统一不同研究的测量单位
- 支持跨文献异构数据的动态比对与过滤
- 适合需要整合多源科研数据的研究者
科学家长期依赖前人研究成果以开拓新方向,尤其注重复用已有实测数据。然而,检索他人论文内容仍是研究者的难题。如今,知识图谱作为语义数据库,在保存与检索学术知识方面发挥重要作用,有助于将传统搜索升级为智能知识检索,提升用户查询的相关性,尤其在信息与知识管理领域至关重要。但目前多数系统仅可检索论文元数据,难以访问实际内容。本文提出一种面向学术知识图谱的结构化内容分面搜索方法,可在不同研究使用不同测量单位的情况下,对实测数据进行比较与筛选。该系统向用户推荐适用单位作为筛选维度,并动态集成远程知识图谱内容,实现学术知识图谱的实体化,显著提升学术内容探索的可用性,使用户能更精准地过滤和获取所需信息。
原文摘要 · Abstract (English)
Scientists have always used the studies and research of other researchers to achieve new objectives and perspectives. In particular, employing and operating the measured data in previous studies is so practical. Searching the content of other scientists' articles is a challenge that researchers have always struggled with. Nowadays, the use of knowledge graphs as a semantic database has helped a lot in saving and retrieving scholarly knowledge. Such technologies are crucial to upgrading traditional search systems to smart knowledge retrieval, which is crucial to getting the most relevant answers for a user query, especially in information and knowledge management. However, in most cases, only the metadata of a paper is searchable, and it is still cumbersome for scientists to have access to the content of the papers. In this paper, we present a novel method of faceted search \emph{structured content} for comparing and filtering measured data in scholarly knowledge graphs while different units of measurement are used in different studies. This search system proposes applicable units as facets to the user and would dynamically integrate content from further remote knowledge graphs to materialize the scholarly knowledge graph and achieve a higher order of exploration usability on scholarly content, which can be filtered to better satisfy the user's information needs. The state of the art is that, by using our faceted search system, users can not only search the contents of scientific articles, but also compare and filter heterogeneous data.
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