Enhancing Information Retrieval in Digital Libraries through Unit Harmonisation in Scholarly Knowledge Graphs

2025-12-06

AI Summary

In digital libraries, heterogeneous measurement units across scientific literature impede cross-study data comparison and retrieval. To address this, we propose a facet-based structured retrieval method centered on measurement units. Our approach establishes a standardized unit mapping system and a semantic annotation framework to achieve unified representation of multi-source, heterogeneous measurement data within academic knowledge graphs. Furthermore, we design a dynamic remote knowledge graph fusion mechanism that enables interactive, cross-unit and cross-source faceted search. Experimental evaluation on real-world scholarly datasets demonstrates significant improvements in relevance and interpretability for unit-sensitive queries. The system enhances both knowledge discovery efficiency and retrieval accuracy, offering a scalable technical pathway for scientometrics and cross-domain knowledge integration.

Technology Category

• Data Mining & Knowledge Management: Linked Open Data, Knowledge Graphs & KB Completion
• Knowledge Representation and Reasoning: Knowledge Acquisition
• Search and Optimization: Distributed Search

Application Category

• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Graph Algorithms and Modeling for the Web: Querying, indexing, and retrieval in Web-related graphs

Abstract

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.

Problem

• Facilitates comparison and filtering of measured data in scholarly knowledge graphs.
• Addresses challenges of heterogeneous measurement units across different research studies.
• Enables dynamic integration of content from remote knowledge graphs for enhanced search.

Innovation

• Faceted search for structured content comparison
• Dynamic integration of remote knowledge graphs
• Unit harmonization enabling heterogeneous data filtering