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

Golsa Heidari, Markus Stocker, Sören Auer

TL;DR

The paper tackles the problem of cross-study, content-level retrieval in digital libraries by introducing a unit-aware faceted search framework built on the Open Research Knowledge Graph (ORKG). It leverages the QUDT ontology and UCUM-based unit conversions to semantically align measured data across heterogeneous units, enabling dynamic, unit-driven filtering and comparison. The approach is implemented within ORKG, demonstrates real-world examples (e.g., sea level projections), and supports federation with remote knowledge graphs to broaden coverage. This work advances digital libraries toward intelligent, quantitative, and cross-domain scholarly discovery by transforming measured data into harmonized, searchable facets.

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.

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

TL;DR

The paper tackles the problem of cross-study, content-level retrieval in digital libraries by introducing a unit-aware faceted search framework built on the Open Research Knowledge Graph (ORKG). It leverages the QUDT ontology and UCUM-based unit conversions to semantically align measured data across heterogeneous units, enabling dynamic, unit-driven filtering and comparison. The approach is implemented within ORKG, demonstrates real-world examples (e.g., sea level projections), and supports federation with remote knowledge graphs to broaden coverage. This work advances digital libraries toward intelligent, quantitative, and cross-domain scholarly discovery by transforming measured data into harmonized, searchable facets.

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.

Paper Structure

This paper contains 12 sections, 3 figures, 1 table.

Table of Contents

  1. Introduction
  2. Related Work
  3. Information Extraction in Knowledge Graphs
  4. Faceted Search in Knowledge Graphs
  5. Methodology
  6. Conceptual Model
  7. Workflow
  8. Implementation
  9. Discussion
  10. Conclusions and Future Works
  11. Acknowledgments.
  12. Disclosure of Interests.

Figures (3)

  • Figure 1: Conceptual model for representing measured data in ORKG, aligned with the QUDT ontology. Each measurement is modeled as a quantityValue entity that links a numericValue, a unit (with its UCUM code), and a corresponding quantityKind. This structure enables semantic comparability, unit-aware filtering, and support for dynamic unit conversion in the faceted search system.
  • Figure 2: Workflow of unit harmonization using ORKG scholarly knowledge graph, QUDT ontology, and UCUM-based unit conversion service. The system processes user-selected units, materializes metadata from QUDT, performs conversions via UCUM, and presents harmonized results for comparison.
  • Figure 3: Illustration of ORKG comparison, which enables a unit conversion. The comparison is available at https://orkg.org/comparisons/R175109.