A Semantic Encoding of Object Centric Event Data
- URL: http://arxiv.org/abs/2511.03351v1
- Date: Wed, 05 Nov 2025 10:44:02 GMT
- Title: A Semantic Encoding of Object Centric Event Data
- Authors: Saba Latif, Fajar J. Ekaputra, Maxim Vidgof, Sabrina Kirrane, Claudio Di Ciccio,
- Abstract summary: The Object-Centric Event Data (OCED) is a novel meta-model aimed at providing a common ground for process data records centered around events and objects.<n>In this paper, we introduce an approach built upon Semantic Web technologies for the realization of semantic-enhanced OCED.
- Score: 0.7340017786387767
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The Object-Centric Event Data (OCED) is a novel meta-model aimed at providing a common ground for process data records centered around events and objects. One of its objectives is to foster interoperability and process information exchange. In this context, the integration of data from different providers, the combination of multiple processes, and the enhancement of knowledge inference are novel challenges. Semantic Web technologies can enable the creation of a machine-readable OCED description enriched through ontology-based relationships and entity categorization. In this paper, we introduce an approach built upon Semantic Web technologies for the realization of semantic-enhanced OCED, with the aim to strengthen process data reasoning, interconnect information sources, and boost expressiveness.
Related papers
- Position Paper: Metadata Enrichment Model: Integrating Neural Networks and Semantic Knowledge Graphs for Cultural Heritage Applications [8.732274235941974]
We present the Metadata Enrichment Model (MEM), a conceptual framework designed to enrich metadata for digitized collections.<n>MEM combines fine-tuned computer vision models, large language models and structured knowledge graphs.<n>We release a dataset of digitized incunabula from the Jagiellonian Digital Library.
arXiv Detail & Related papers (2025-05-29T15:22:18Z) - Customized Information and Domain-centric Knowledge Graph Construction with Large Language Models [0.0]
We propose a novel approach based on knowledge graphs to provide timely access to structured information.
Our framework encompasses a text mining process, which includes information retrieval, keyphrase extraction, semantic network creation, and topic map visualization.
We apply our methodology to the domain of automotive electrical systems to demonstrate the approach, which is scalable.
arXiv Detail & Related papers (2024-09-30T07:08:28Z) - Agent-driven Generative Semantic Communication with Cross-Modality and Prediction [57.335922373309074]
We propose a novel agent-driven generative semantic communication framework based on reinforcement learning.
In this work, we develop an agent-assisted semantic encoder with cross-modality capability, which can track the semantic changes, channel condition, to perform adaptive semantic extraction and sampling.
The effectiveness of the designed models has been verified using the UA-DETRAC dataset, demonstrating the performance gains of the overall A-GSC framework.
arXiv Detail & Related papers (2024-04-10T13:24:27Z) - From Dialogue to Diagram: Task and Relationship Extraction from Natural
Language for Accelerated Business Process Prototyping [0.0]
This paper introduces a contemporary solution, where central to our approach, is the use of dependency parsing and Named Entity Recognition (NER)
We utilize Subject-Verb-Object (SVO) constructs for identifying action relationships and integrate semantic analysis tools, including WordNet, for enriched contextual understanding.
The system adeptly handles data transformation and visualization, converting verbose extracted information into BPMN (Business Process Model and Notation) diagrams.
arXiv Detail & Related papers (2023-12-16T12:35:28Z) - Semantic Communications for Artificial Intelligence Generated Content
(AIGC) Toward Effective Content Creation [75.73229320559996]
This paper develops a conceptual model for the integration of AIGC and SemCom.
A novel framework that employs AIGC technology is proposed as an encoder and decoder for semantic information.
The framework can adapt to different types of content generated, the required quality, and the semantic information utilized.
arXiv Detail & Related papers (2023-08-09T13:17:21Z) - A Unified Framework for Integrating Semantic Communication and
AI-Generated Content in Metaverse [57.317580645602895]
Integrated Semantic Communication and AI-Generated Content (ISGC) has attracted a lot of attentions recently.
ISGC transfers semantic information from user inputs, generates digital content, and renders graphics for Metaverse.
We introduce a unified framework that captures ISGC two primary benefits, including integration gain for optimized resource allocation.
arXiv Detail & Related papers (2023-05-18T02:02:36Z) - Imitation Learning-based Implicit Semantic-aware Communication Networks:
Multi-layer Representation and Collaborative Reasoning [68.63380306259742]
Despite its promising potential, semantic communications and semantic-aware networking are still at their infancy.
We propose a novel reasoning-based implicit semantic-aware communication network architecture that allows multiple tiers of CDC and edge servers to collaborate.
We introduce a new multi-layer representation of semantic information taking into consideration both the hierarchical structure of implicit semantics as well as the personalized inference preference of individual users.
arXiv Detail & Related papers (2022-10-28T13:26:08Z) - Entity-Graph Enhanced Cross-Modal Pretraining for Instance-level Product
Retrieval [152.3504607706575]
This research aims to conduct weakly-supervised multi-modal instance-level product retrieval for fine-grained product categories.
We first contribute the Product1M datasets, and define two real practical instance-level retrieval tasks.
We exploit to train a more effective cross-modal model which is adaptively capable of incorporating key concept information from the multi-modal data.
arXiv Detail & Related papers (2022-06-17T15:40:45Z) - Context Decoupling Augmentation for Weakly Supervised Semantic
Segmentation [53.49821324597837]
Weakly supervised semantic segmentation is a challenging problem that has been deeply studied in recent years.
We present a Context Decoupling Augmentation ( CDA) method to change the inherent context in which the objects appear.
To validate the effectiveness of the proposed method, extensive experiments on PASCAL VOC 2012 dataset with several alternative network architectures demonstrate that CDA can boost various popular WSSS methods to the new state-of-the-art by a large margin.
arXiv Detail & Related papers (2021-03-02T15:05:09Z) - Semantic Web Environments for Multi-Agent Systems: Enabling agents to
use Web of Things via semantic web [6.85316573653194]
Multi-agent system (MAS) technologies are the right abstraction for developing decentralized and open Web applications.
The aim of the project is to transform "Agent and propose an approach to transform "Agent and A&A" into a Web-readable format with in line web formats.
arXiv Detail & Related papers (2020-02-20T11:18:29Z)
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.