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A Knowledge Graph Approach for Exploratory Search in Research Institutions

Tim Schopf, Nektarios Machner, Florian Matthes

TL;DR

The paper addresses the challenge of understanding large research landscapes within institutions, where unstructured sources hinder exploratory search. It proposes a knowledge-graph-based approach that semantically links topics, researchers, units, and publications, leveraging an automatically constructed FoS hierarchy (~200k concepts) derived from MAG. A proof-of-concept architecture using NodeJS, Neo4j, and a React frontend demonstrates search, hierarchical navigation, analytics, and recommendations to improve discovery and potential collaboration. The work highlights data-quality and scope limitations (single-institution, reliance on Fos-linked publications) but outlines a clear path toward an MVP and systematic evaluation to advance exploratory knowledge search in research institutions.

Abstract

Over the past decades, research institutions have grown increasingly and consequently also their research output. This poses a significant challenge for researchers seeking to understand the research landscape of an institution. The process of exploring the research landscape of institutions has a vague information need, no precise goal, and is open-ended. Current applications are not designed to fulfill the requirements for exploratory search in research institutions. In this paper, we analyze exploratory search in research institutions and propose a knowledge graph-based approach to enhance this process.

A Knowledge Graph Approach for Exploratory Search in Research Institutions

TL;DR

The paper addresses the challenge of understanding large research landscapes within institutions, where unstructured sources hinder exploratory search. It proposes a knowledge-graph-based approach that semantically links topics, researchers, units, and publications, leveraging an automatically constructed FoS hierarchy (~200k concepts) derived from MAG. A proof-of-concept architecture using NodeJS, Neo4j, and a React frontend demonstrates search, hierarchical navigation, analytics, and recommendations to improve discovery and potential collaboration. The work highlights data-quality and scope limitations (single-institution, reliance on Fos-linked publications) but outlines a clear path toward an MVP and systematic evaluation to advance exploratory knowledge search in research institutions.

Abstract

Over the past decades, research institutions have grown increasingly and consequently also their research output. This poses a significant challenge for researchers seeking to understand the research landscape of an institution. The process of exploring the research landscape of institutions has a vague information need, no precise goal, and is open-ended. Current applications are not designed to fulfill the requirements for exploratory search in research institutions. In this paper, we analyze exploratory search in research institutions and propose a knowledge graph-based approach to enhance this process.
Paper Structure (11 sections, 2 figures)

This paper contains 11 sections, 2 figures.

Table of Contents

  1. INTRODUCTION
  2. REQUIREMENTS ANALYSIS
  3. Exploratory Search in Research Institutions
  4. Requirements
  5. CONSTRUCTING THE RESEARCH INSTITUTION KNOWLEDGE GRAPH
  6. PROOF OF CONCEPT
  7. Architecture
  8. Data Model
  9. Features
  10. LIMITATIONS
  11. CONCLUSION

Figures (2)

  • Figure 1: Simplified ontology of the research institution knowledge graph. Here, only the University entity, representing German universities, with its respective sub-units is expanded. Other types of research entities are grouped in the node Other Research Institution. All entities are transitively connected to the Field of Study entity. This means that each researcher and each sub-unit of the research institute can be linked to their respective fos by traversing the kg.
  • Figure 2: Mock-up of the proof of concept homepage. Users can either use the search to retrieve specific information or explore the existing research landscape at the institution by using the hierarchically structured fos concepts. On the homepage, the fos concept of each high-level research domain is shown in a convenient layout. Additionally, users can immediately gain insights into current research trends within the institution. Here, users can examine research trends within different depth levels of the fos hierarchy.