Methodology for Multi-Aspect Ontology Development: Ontology for Decision Support Based on Human-Machine Collective Intelligence

Abstract

The advantage of human-machine collective intelligence for decision support systems is the ability to make better decisions due to the mitigation of human biases in the generation of potential solutions and their evaluation. So far, the potential of human-machine collective intelligence was used only in few decision support systems, however teamwork between humans and machines has not been achieved. This is partly due to the lack of interoperability in these systems. In earlier works, the authors proposed the apparatus of multi-aspect ontologies implying the integration of multiple domain ontologies to provide interoperability between humans and machines and coordinate interrelated processes going on in the systems of the considered type. Such ontologies have proved efficient for systems that require intensive information and knowledge exchange between loosely-related dynamic autonomous domains (e.g., enterprise knowledge management, product lifecycle management, or human-machine collective intelligence systems). However, existing ontology development methodologies fail to recommend a process that would support cross-domain knowledge integration during the multi-aspect ontology development. Moreover, the structure of the multiaspect ontology imposes some restrictions on the integration approach. The paper proposes such a methodology for the multi-aspect ontology development that incorporates the aspect integration approach at multiple levels. The methodology is applied to develop a multi-aspect ontology for decision support based on human-machine collective intelligence. An example from the "e-tourism" domain demonstrates the applicability of the proposed methodology as well as the usage of the multi-aspect ontology for a humanmachine environment aimed at solving real-world problems. The proposed methodology can facilitate the development of ontologies for complex knowledge-based systems that operate with knowledge from multiple loosely-connected domains.

История СПБ ФИЦ РАН: 45 Лет Научной Деятельности

“…Разработан контекстно-управляемый подход к интеллектуальной поддержке принятия решений на основе анализа цифровых следов пользователей, описываемых Большими Данными из системы «пользователь-цифровая среда», для построения моделей их цифровой жизни (digital life) и выявления обобщенных паттернов групп пользователей со схожими предпочтениями и поведением в результате контекстно-зависимой онтологической кластеризации пользователей, а также предсказания их поведенческих активностей [36,37]. Разработан метод автоматизированной актуализации компетенций экспертов на основе анализа истории их участия в выполнении проектов в рамках экспертной сети, отличающийся онтологическим описанием знаний о проблемной области и использованием технологии управления контекстом и позволяющий учитывать дополнительные знания о компетенции экспертов, выполненных ими проектах и успешности их выполнения [38]. Разработан комплекс методов для повышения эффективности совместной работы в системах поддержки принятия решений на основе человеко-машинного коллективного интеллекта, включающий метод стимулирования самоорганизации посредством учета ее предпосылок (например, разнообразие и определенный уровень избыточности навыков внутри группы, совместимость участников) в математической модели задачи формирования команд, метод обеспечения развития участников системы и метод рекомендации протоколов, обеспечивающий оценку состояния обсуждения и формирование рекомендаций на основе правил, а также их уточнение с помощью алгоритмов машинного обучения, учитывающих обратную связь [39].…”

Augmentation of contextual knowledge based on domain dominant words for IoT applications interoperability

“…During the software testing phase, ontology solves the knowledge silo problem which contains the software testing process information and failure details [12]. The cross-domain knowledge is integrated into multi-aspect ontology which supports the decision system [13]. The semantic virtualization technique suggests the removal of the vertical barrier between various IoT application standards which leads to data acquisition plans [14].…”

User Ontology for Intelligent Decision Support Based on User Digital Life

“…The apparatus of multi-aspect ontologies [2] is used to model a user of the intelligent decision support system implemented based on the conceptual framework (Figure 1). Three aspects corresponding to three framework's components are proposed to represent a user in the user ontology: a user profile, a user digital life model, and a user segment.…”

“…The general ontology level is built to support the integration. For this, the methodology of the development of multi-aspect ontologies [2] is followed, which developed co-authored with the authors of the present paper. The schema of the general level creation is as follows.…”

“…A user type represents a category of users distinguished by common preferences and decision-making behaviour. The apparatus of multi-aspect ontologies [2] is used to develop a user ontology. This ontology models a user from three perspectives: a user profile aspect, a user segment aspect, and an aspect of user digital life model.…”