A Systematic Literature Review and Classification of Approaches for Keyword Search Over Graph-Shaped Data
Year: 2025
Type: review
Abstract: Knowledge graphs provide machine-interpretable data that allow automatic data understanding and deduction of new facts. However, machines are not the only consumers of such semantic data. Human users could also benefit from graph-structured data by browsing and exploring it to detect interesting associations and draw conclusions. To achieve that, methods that allow for search over knowledge graphs are highly sought after. Keyword search is an intuitive and common way to retrieve relevant data (e.g., documents) and can also be leveraged to search over knowledge graphs. In this survey paper, we derive the typical architecture of a system for keyword search over graph-shaped data, we formally define the problem, we highlight related challenges, and we compare with existing relevant surveys to identify the gaps. We conduct a comprehensive review of studies dealing with the topic of keyword search over graph-shaped data (e.g., knowledge graphs) following a systematic method. Based on that, we derive and define different aspects for classifying existing works. We also give an overview of how those systems are evaluated and highlight possible future research directions.
Source: Semantic Web
Authors: Leila Feddoul, Frank Löffler, Sirko Schindler
Language: English
Topic: Semantic Web and Ontologies
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7091266921