IOPE: Interactive Ontology Population and Enrichment Guided by Ontological Constraints

Baghernezhad-Tabasi, Shadi; Druette, Loïc; Jouanot, Fabrice; Meurger, Celine; Rousset, Marie-Christine

doi:10.1007/978-3-030-90888-1_25

Cited by 3 publications

References 13 publications

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IOPE: Interactive Ontology Population and Enrichment Guided by Ontological Constraints

Baghernezhad-Tabasi

Druette

Jouanot

et al. 2021

Web Information Systems Engineering – WISE 2021

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In this paper, we focus on the construction of specialized ontologies that capture skills of experienced experts in a particular domain with the goal of sharing them with a larger community of trainees or less experienced experts in the domain. Our main contribution is the automatic construction of a Graphical User Interface (GUI) named IOPE built from the ontological constraints of an input ontology, as the support for the controlled update process of the considered ontology. The resulting GUI functions as a guidance for the experts with no knowledge of OWL/RDFS, which enables them to easily explore and update their ontologies. We illustrate the functionality of IOPE on an ontology for simulation-based medical workshops called OntoSAMSEI.

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IOPE: Interactive Ontology Population and Enrichment Guided by Ontological Constraints

Baghernezhad-Tabasi

Druette

Jouanot

et al. 2021

Web Information Systems Engineering – WISE 2021

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Human-AI Interaction for Semantic Knowledge Enrichment of AI Model Output

Chala,

Graß

2023

Artificial Intelligence in Manufacturing

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Modern manufacturing requires developing a framework of AI solutions that capture and process data from various sources including from human-AI collaboration. This chapter tries to describe the concept of domain knowledge fusion in human-AI collaboration for manufacturing. Human interaction with AI is enabled in such a way that the domain expert not only inspects the output of the AI model but also injects engineered knowledge in order to retrain AI models for iterative improvement. Domain knowledge fusion is a technique that involves combining knowledge from multiple domains or sources to produce a more complete solution by augmenting learned knowledge, i.e., the knowledge generated by the AI model with engineered knowledge, i.e., the knowledge provided by the domain expert. The concept developed in this chapter demonstrates how the domain expert interacts with AI systems to observe and decide the veracity of the learned knowledge with respect to the given context. It enables humans to collaborate with AI systems through intuitive interfaces that help domain experts in interpreting insights, validating the findings, and applying domain knowledge to gain a deeper understanding of the data.

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Towards Digitalization of Business Processes: Building a Business Domain Ontology for Project Management

Mamedova

2022

Wseas Transactions on Systems and Control

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The article presents an ontological model of the subject area. It was designed to improve the efficiency of the software development process and reduce development costs. This ontological model is recommended to be used in the formation of the company’s software architecture. The article contains data on the process and results of developing an ontological model of the selected subject area. The theoretical justification of the application of the ontological approach is presented. The business domain of relations between IT directors during the implementation of the digitalization project of the company’s business process is defined as a subject area. The sources of the metadata of the ontological model were the standards for the organization of collective activity – ISO 21500:2012 and PRINCE2. The ontological model was developed in accordance with the basic characteristics of the modeling process and the mathematical apparatus that established the relationship of concepts and the order of inheritance of attributes. The project topics became layers of the ontological model, the responsibility for the consistent implementation of which is borne by IT directors, as well as the functional tasks and functional roles of IT management distributed among them. The order and direction of interaction of the IT management in the project is shown by the relationships between the entities of the model. The chosen form of visualization - semantic network - allows you to demonstrate the result of the development of an ontological model and can be considered as a ready-made product to support the semantics of end-user requests in the company.

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IOPE: Interactive Ontology Population and Enrichment Guided by Ontological Constraints

Cited by 3 publications

References 13 publications

IOPE: Interactive Ontology Population and Enrichment Guided by Ontological Constraints

IOPE: Interactive Ontology Population and Enrichment Guided by Ontological Constraints

Human-AI Interaction for Semantic Knowledge Enrichment of AI Model Output

Towards Digitalization of Business Processes: Building a Business Domain Ontology for Project Management

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