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3K: Knowledge-Enriched Digital Twin Framework

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Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/978-3-030-27878-6_9openalex-snapshot

Cloud-Based Digital Twin for Industrial Robotics

inferredcites →https://doi.org/10.1007/s10618-007-0077-7openalex-snapshot

Data mining with Temporal Abstractions: learning rules from time series

inferredcites →https://doi.org/10.1016/j.aei.2023.101915openalex-snapshot

From simple digital twin to complex digital twin part II: Multi-scenario applications of digital twin shop floor

inferredcites →https://doi.org/10.1016/j.autcon.2022.104695openalex-snapshot

Digital twin enabled fault detection and diagnosis process for building HVAC systems

inferredcites →https://doi.org/10.1016/j.cirpj.2020.02.002openalex-snapshot

Characterising the Digital Twin: A systematic literature review

inferredcites →https://doi.org/10.1016/j.eng.2019.01.014openalex-snapshot

Digital Twins and Cyber–Physical Systems toward Smart Manufacturing and Industry 4.0: Correlation and Comparison

inferredcites →https://doi.org/10.1016/j.future.2023.12.013openalex-snapshot

Ontologies in digital twins: A systematic literature review

inferredcites →https://doi.org/10.1016/j.ins.2020.02.073openalex-snapshot

A survey of evolutionary computation for association rule mining

inferredcites →https://doi.org/10.1080/09613218.2020.1840328openalex-snapshot

Physiological sensing-driven personal thermal comfort modelling in consideration of human activity variations

inferredcites →https://doi.org/10.1109/access.2023.3259360openalex-snapshot

An Analysis of Machine Learning-Based Semantic Matchmaking

inferredcites →https://doi.org/10.1109/icdcs54860.2022.00070openalex-snapshot

A Digital-Twin Based Architecture for Software Longevity in Smart Homes

inferredcites →https://doi.org/10.1109/itnt55410.2022openalex-snapshot

2022 VIII International Conference on Information Technology and Nanotechnology (ITNT)

inferredcites →https://doi.org/10.1109/itnt55410.2022.9848748openalex-snapshot

Development of crop-simulation multiagent system for smart digital twin of plant

inferredcites →https://doi.org/10.1115/1.4050376openalex-snapshot

Quality Deviation Control for Aircraft Using Digital Twin

inferredcites →https://doi.org/10.1145/1390156.1390294openalex-snapshot

Extracting and composing robust features with denoising autoencoders

inferredcites →https://doi.org/10.1145/335191.335372openalex-snapshot

Mining frequent patterns without candidate generation

inferredcites →https://doi.org/10.2200/s01125ed1v01y202109dsk022openalex-snapshot

Knowledge Graphs

inferredcites →https://doi.org/10.24251/hicss.2020.204openalex-snapshot

Knowledge Discovery in Smart City Digital Twins

inferredcites →https://doi.org/10.25984/1881324openalex-snapshot

LBNL Fault Detection and Diagnostics Datasets

inferredcites →https://doi.org/10.3390/s21113583openalex-snapshot

Digital Twin-Based Safety Risk Coupling of Prefabricated Building Hoisting

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.2139/ssrn.5356320openalex-extracted

Pyaerial: Scalable Association Rule Mining from Tabular Data

inferred← citeshttps://doi.org/10.1016/j.softx.2025.102341openalex-snapshot

PyAerial: Scalable association rule mining from tabular data

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