Work

Using dynamic knowledge graphs to detect emerging communities of knowledge

Year: 2024

Type: article

Abstract: Knowledge graphs represent relationships between entities. These graphs can take dynamic forms to trace changes along time through text models and further used by reasoning systems with the intent to answer queries. In this research we explore their applicability for extracting temporal patterns of knowledge in the form of communities. To this end, we propose a method for generating knowledge relationships over unconnected components of a knowledge graph, allowing for a targeted exploration of emerging contents in corpora. This analysis is applied to the corpora of the Conference on Knowledge Discovery and Data Mining (KDD) publications over the last decade. We find the key knowledge communities over time and rank the underlying concepts. Results show that the publication efforts increasingly focus on graph research and the creation of relationships instead of new concepts. The acquired results confirm the validity of the proposed knowledge discovery methodology for community-centered analysis of emerging changes in dynamic knowledge graphs.

Source: Knowledge-Based Systems

DOI: 10.1016/j.knosys.2024.111671

Authors: Joao T. Aparicio, Elisabete Arsenio, Francisco Santos, Rui Henriques

Institutions: Instituto de Engenharia de Sistemas e Computadores Investigação e Desenvolvimento, University of Lisbon, Instituto Superior Técnico, National Laboratory for Civil Engineering

Language: English

Topic: Complex Network Analysis Techniques

Subfield: Statistical and Nonlinear Physics

Field: Physics and Astronomy

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $3,250

OpenAlex ID: W4393315308

FWCI: 3.844

Cites: 82

Cited by: 26

Related to: 10