Industrial Semantics-Aware Digital Twins: A Hybrid Graph Matching Approach for Asset Administration Shells

• URL: http://arxiv.org/abs/2601.06613v1
• Date: Sat, 10 Jan 2026 16:26:54 GMT
• Title: Industrial Semantics-Aware Digital Twins: A Hybrid Graph Matching Approach for Asset Administration Shells
• Authors: Ariana Metović, Nicolai Maisch, Samed Ajdinović, Armin Lechler, Andreas Wortmann, Oliver Riedel,
• Abstract summary: This paper proposes a hybrid graph matching approach to enable semantics-aware comparison of Digital Twin representations.<n>The method combines rule-based pre-filtering using SPARQL with embedding-based similarity calculation leveraging RDF2vec.
• Score: 2.05231433582637
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Although the Asset Administration Shell (AAS) standard provides a structured and machine-readable representation of industrial assets, their semantic comparability remains a major challenge, particularly when different vocabularies and modeling practices are used. Engineering would benefit from retrieving existing AAS models that are similar to the target in order to reuse submodels, parameters, and metadata. In practice, however, heterogeneous vocabularies and divergent modeling conventions hinder automated, content-level comparison across AAS. This paper proposes a hybrid graph matching approach to enable semantics-aware comparison of Digital Twin representations. The method combines rule-based pre-filtering using SPARQL with embedding-based similarity calculation leveraging RDF2vec to capture both structural and semantic relationships between AAS models. This contribution provides a foundation for enhanced discovery, reuse, and automated configuration in Digital Twin networks.

Related papers

• Query as Anchor: Scenario-Adaptive User Representation via Large Language Model [28.30329175937291]
  We propose Query-as-Anchor, a framework shifting user modeling from static encoding to dynamic, query-aware synthesis.<n>We first construct UserU, an industrial-scale pre-training dataset that aligns behavioral sequences with user understanding semantics.<n>We introduce Cluster-based Soft Prompt Tuning to enforce discriminative latent structures.<n>For deployment, anchoring queries at sequence termini enables KV-cache-accelerated inference with negligible incremental latency.
  arXiv  Detail & Related papers  (2026-02-16T06:09:31Z)
• Black-box Model Merging for Language-Model-as-a-Service with Massive Model Repositories [21.899117703417517]
  We propose a derivative-free optimization framework based on the evolutionary algorithm (Evo-Merging)<n>Our method consists of two key components: (1) sparsity-based denoising, designed to identify and filter out irrelevant or redundant information across models, and (2) sign-aware scaling, which dynamically computes optimal combination weights for the relevant models based on their performance.<n>Our approach achieves state-of-the-art results on a range of tasks, significantly outperforming existing strong baselines.
  arXiv  Detail & Related papers  (2025-09-16T10:55:50Z)
• Structural Similarity-Inspired Unfolding for Lightweight Image Super-Resolution [88.20464308588889]
  We propose a Structural Similarity-Inspired Unfolding (SSIU) method for efficient image SR.<n>This method is designed through unfolding an SR optimization function constrained by structural similarity.<n>Our model outperforms current state-of-the-art models, boasting lower parameter counts and reduced memory consumption.
  arXiv  Detail & Related papers  (2025-06-13T14:29:40Z)
• Model Assembly Learning with Heterogeneous Layer Weight Merging [57.8462476398611]
  We introduce Model Assembly Learning (MAL), a novel paradigm for model merging.<n>MAL integrates parameters from diverse models in an open-ended model zoo to enhance the base model's capabilities.
  arXiv  Detail & Related papers  (2025-03-27T16:21:53Z)
• Relation Extraction with Instance-Adapted Predicate Descriptions [9.021267901894912]
  Relation extraction plays a major role in downstream applications such as knowledge discovery and question answering.<n>In this paper, we revisit fine-tuning such smaller models using a novel dual-encoder architecture with a joint contrastive and cross-entropy loss.<n>Our approach achieved F1 score improvements ranging from 1% to 2% over state-of-the-art methods with a simple but elegant formulation.
  arXiv  Detail & Related papers  (2025-03-22T15:36:41Z)
• A Top-down Graph-based Tool for Modeling Classical Semantic Maps: A Crosslinguistic Case Study of Supplementary Adverbs [50.982315553104975]
  Semantic map models (SMMs) construct a network-like conceptual space from cross-linguistic instances or forms.<n>Most SMMs are manually built by human experts using bottom-up procedures.<n>We propose a novel graph-based algorithm that automatically generates conceptual spaces and SMMs in a top-down manner.
  arXiv  Detail & Related papers  (2024-12-02T12:06:41Z)
• Adaptable Embeddings Network (AEN) [49.1574468325115]
  We introduce Adaptable Embeddings Networks (AEN), a novel dual-encoder architecture using Kernel Density Estimation (KDE) AEN allows for runtime adaptation of classification criteria without retraining and is non-autoregressive. The architecture's ability to preprocess and cache condition embeddings makes it ideal for edge computing applications and real-time monitoring systems.
  arXiv  Detail & Related papers  (2024-11-21T02:15:52Z)
• Generation of Asset Administration Shell with Large Language Model Agents: Toward Semantic Interoperability in Digital Twins in the Context of Industry 4.0 [0.6749750044497732]
  This research introduces a novel approach for achieving semantic interoperability in digital twins. It assists the creation of Asset Administration Shell (AAS) as digital twin model within the context of Industry 4.0.
  arXiv  Detail & Related papers  (2024-03-25T21:37:30Z)
• Revisiting the Evaluation of Image Synthesis with GANs [55.72247435112475]
  This study presents an empirical investigation into the evaluation of synthesis performance, with generative adversarial networks (GANs) as a representative of generative models. In particular, we make in-depth analyses of various factors, including how to represent a data point in the representation space, how to calculate a fair distance using selected samples, and how many instances to use from each set.
  arXiv  Detail & Related papers  (2023-04-04T17:54:32Z)
• Duality-Induced Regularizer for Semantic Matching Knowledge Graph Embeddings [70.390286614242]
  We propose a novel regularizer -- namely, DUality-induced RegulArizer (DURA) -- which effectively encourages the entities with similar semantics to have similar embeddings. Experiments demonstrate that DURA consistently and significantly improves the performance of state-of-the-art semantic matching models.
  arXiv  Detail & Related papers  (2022-03-24T09:24:39Z)
• Recent Developments Combining Ensemble Smoother and Deep Generative Networks for Facies History Matching [58.720142291102135]
  This research project focuses on the use of autoencoders networks to construct a continuous parameterization for facies models. We benchmark seven different formulations, including VAE, generative adversarial network (GAN), Wasserstein GAN, variational auto-encoding GAN, principal component analysis (PCA) with cycle GAN, PCA with transfer style network and VAE with style loss.
  arXiv  Detail & Related papers  (2020-05-08T21:32:42Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.