A Cooperative Approach for Knowledge-based Business Process Design in a Public Authority
- URL: http://arxiv.org/abs/2507.19842v2
- Date: Tue, 14 Oct 2025 13:38:17 GMT
- Title: A Cooperative Approach for Knowledge-based Business Process Design in a Public Authority
- Authors: Mohammad Azarijafari, Luisa Mich, Michele Missikoff, Oleg Missikoff,
- Abstract summary: This paper presents a knowledge-based method to support business experts in designing business processes.<n>The construction of the knowledge base starts from simple, text-based, knowledge artefacts and then progresses towards more structured, formal representations.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Enterprises are currently undergoing profound transformations due to the unpostponable digital transformation. Then, to remain competitive, enterprises must adapt digital solutions, transforming their organisational structures and operations. This organisational shift is also important for small and medium-sized enterprises. A key innovation frontier is the adoption of process-oriented production models. This paper presents a knowledge-based method to support business experts in designing business processes. The method requires no prior expertise in Knowledge Engineering and guides designers through a structured sequence of steps to produce a diagrammatic workflow of the target process. The construction of the knowledge base starts from simple, text-based, knowledge artefacts and then progresses towards more structured, formal representations. The approach has been conceived to allow a shared approach for all stakeholders and actors who participate in the BP design.
Related papers
- Step-by-step Layered Design Generation [47.423344283764074]
We propose a novel problem setting called Step-by-Step Layered Design Generation.<n>It tasks a machine learning model with generating a design that adheres to a sequence of instructions from a designer.<n>To complement our new problem setting, we introduce a new evaluation suite, including a dataset and a benchmark.
arXiv Detail & Related papers (2025-12-03T00:59:43Z) - Web-CogReasoner: Towards Knowledge-Induced Cognitive Reasoning for Web Agents [49.88380945341337]
We decompose a web agent's capabilities into two essential stages: knowledge content learning and cognitive processes.<n>To facilitate knowledge acquisition, we construct the Web-CogDataset, a structured resource curated from 14 real-world websites.<n>Building on this foundation, we operationalize these processes through a novel knowledge-driven Chain-of-Thought (CoT) reasoning framework.
arXiv Detail & Related papers (2025-08-03T17:17:52Z) - How Do Experts Make Sense of Integrated Process Models? [6.637963166503315]
In this study, we explore how expert process workers make sense of the information provided through integrated modeling approaches.<n>By studying expert process workers engaged in tasks based on integrated modeling of business processes and rules, we provide insights that pave the way for a better understanding of sensemaking practices.
arXiv Detail & Related papers (2025-05-27T03:32:28Z) - KERAIA: An Adaptive and Explainable Framework for Dynamic Knowledge Representation and Reasoning [46.85451489222176]
KERAIA is a novel framework and software platform for symbolic knowledge engineering.<n>It addresses the persistent challenges of representing, reasoning with, and executing knowledge in dynamic, complex, and context-sensitive environments.
arXiv Detail & Related papers (2025-05-07T10:56:05Z) - A Survey of Frontiers in LLM Reasoning: Inference Scaling, Learning to Reason, and Agentic Systems [93.8285345915925]
Reasoning is a fundamental cognitive process that enables logical inference, problem-solving, and decision-making.<n>With the rapid advancement of large language models (LLMs), reasoning has emerged as a key capability that distinguishes advanced AI systems.<n>We categorize existing methods along two dimensions: (1) Regimes, which define the stage at which reasoning is achieved; and (2) Architectures, which determine the components involved in the reasoning process.
arXiv Detail & Related papers (2025-04-12T01:27:49Z) - How Do LLMs Acquire New Knowledge? A Knowledge Circuits Perspective on Continual Pre-Training [92.88889953768455]
Large Language Models (LLMs) face a critical gap in understanding how they internalize new knowledge.<n>We identify computational subgraphs that facilitate knowledge storage and processing.
arXiv Detail & Related papers (2025-02-16T16:55:43Z) - StructRAG: Boosting Knowledge Intensive Reasoning of LLMs via Inference-time Hybrid Information Structurization [94.31508613367296]
Retrieval-augmented generation (RAG) is a key means to effectively enhance large language models (LLMs)
We propose StructRAG, which can identify the optimal structure type for the task at hand, reconstruct original documents into this structured format, and infer answers based on the resulting structure.
Experiments show that StructRAG achieves state-of-the-art performance, particularly excelling in challenging scenarios.
arXiv Detail & Related papers (2024-10-11T13:52:44Z) - Digital Twins of Business Processes: A Research Manifesto [1.773489607375694]
The Internet of Things has heavily been adopted in organizational and industrial settings to monitor and automatize physical processes.
Advanced ways of managing and maintaining business processes come within reach as there is a Digital Twin of a business process.
This manifesto paper aims to contribute to the current state of the art by clarifying the relationship between business processes and Digital Twins.
arXiv Detail & Related papers (2024-09-25T15:43:46Z) - Procedure Model for Building Knowledge Graphs for Industry Applications [0.0]
The graph-based integration of previously unconnected information with domain knowledge provides new insights.
This paper presents a practical step-by-step procedure model for building an RDF knowledge graph.
arXiv Detail & Related papers (2024-09-20T11:46:37Z) - Enhancing Question Answering for Enterprise Knowledge Bases using Large Language Models [46.51659135636255]
EKRG is a novel Retrieval-Generation framework based on large language models (LLMs)
We introduce an instruction-tuning method using an LLM to generate sufficient document-question pairs for training a knowledge retriever.
We develop a relevance-aware teacher-student learning strategy to further enhance the efficiency of the training process.
arXiv Detail & Related papers (2024-04-10T10:38:17Z) - Process Modeling With Large Language Models [42.0652924091318]
This paper explores the integration of Large Language Models (LLMs) into process modeling.
We propose a framework that leverages LLMs for the automated generation and iterative refinement of process models.
Preliminary results demonstrate the framework's ability to streamline process modeling tasks.
arXiv Detail & Related papers (2024-03-12T11:27:47Z) - Extracting Process-Aware Decision Models from Object-Centric Process
Data [54.04724730771216]
This paper proposes the first object-centric decision-mining algorithm called Integrated Object-centric Decision Discovery Algorithm (IODDA)
IODDA is able to discover how a decision is structured as well as how a decision is made.
arXiv Detail & Related papers (2024-01-26T13:27:35Z) - Framework for continuous transition to Agile Systems Engineering in the
Automotive Industry [0.0]
We propose an agile Systems Engineering (SE) Framework for the automotive industry to meet the new agility demand.
In addition to the methodological background, we present results of a pilot project in the chassis development department of a German automotive manufacturer.
arXiv Detail & Related papers (2023-11-21T10:21:47Z) - The Innovation-to-Occupations Ontology: Linking Business Transformation
Initiatives to Occupations and Skills [10.010383370458115]
Several recent studies have attempted to predict the emergence of new roles and skills in the labour market from online job ads.
Our approach successfully matches occupations to transformation initiatives under ten different scenarios.
This framework presents an innovative approach to guide enterprises and educational institutions on the workforce requirements for specific business transformation initiatives.
arXiv Detail & Related papers (2023-10-27T05:57:41Z) - PET: A new Dataset for Process Extraction from Natural Language Text [15.16406344719132]
We develop the first corpus of business process descriptions annotated with activities, gateways, actors and flow information.
We present our new resource, including a detailed overview of the annotation schema and guidelines, as well as a variety of baselines to benchmark the difficulty and challenges of business process extraction from text.
arXiv Detail & Related papers (2022-03-09T16:33:59Z) - Knowledge Integration of Collaborative Product Design Using Cloud
Computing Infrastructure [65.2157099438235]
The main focus of this paper is the concept of ongoing research in providing the knowledge integration service for collaborative product design and development using cloud computing infrastructure.
Proposed knowledge integration services support users by giving real-time access to knowledge resources.
arXiv Detail & Related papers (2020-01-16T18:44:27Z)
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.