A Business-Model-Driven Approach to Task-Planning Knowledge Graph Construction

As the complexity of mission planning increases, relying on the subjective experience of planners is no longer sufficient to meet the needs of modern mission planning. Knowledge mapping, as a structured knowledge management technique, provides an effective solution for systematically integrating knowledge in the task-planning domain. The mission-planning business model is able to systematically capture and portray domain knowledge in mission planning through a formal representation of mission planning processes, rules, and constraints. Thus, it becomes an important source of knowledge for mission-planning knowledge mapping. This paper proposes a business-model-driven knowledge graph construction method for mission planning. First, under the support of conceptual business knowledge, the multidimensional task-planning ontology network expression method is utilized to construct the task-planning ontology network, and then the data-based business knowledge is structured to transform it into business data mapping to complete the acquisition of business knowledge. Then, the task-planning ontology network is constructed using the multidimensional task-planning ontology network representation method under the support of conceptual knowledge. Subsequently, a domain knowledge categorization algorithm based on Ullman subgraph matching is used to realize the matching mapping between the ontology network and business data mapping to complete the categorization of task-planning domain knowledge. Finally, the generated task-planning domain knowledge graph is stored in the Neo4j graph database. In order to ensure the completeness of the knowledge graph, an adaptive adjustment method based on its actual effectiveness is conceived, which is able to detect and adjust the completeness of the knowledge graph. The effectiveness of the proposed methodology is validated by constructing a space-station mission-planning knowledge graph driven by a space-station mission-planning business model.

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Publication Details

Journal
Applied Sciences
Published
2024-11-28
DOI
https://doi.org/10.3390/app142311090
Primary Topic
Semantic Web and Ontologies
Type
article
Field-Weighted Citation Impact
0.00
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article

A Business-Model-Driven Approach to Task-Planning Knowledge Graph Construction

Xinglong Chen, Tianguo Jin, Xiaoqian Liu, Bingxiang Zeng et al.
Applied Sciences
Semantic Web and Ontologies
article

A Business-Model-Driven Approach to Task-Planning Knowledge Graph Construction

Xinglong Chen, Tianguo Jin, Xiaoqian Liu, Bingxiang Zeng, Dongliang Zhang
article en

Abstract

As the complexity of mission planning increases, relying on the subjective experience of planners is no longer sufficient to meet the needs of modern mission planning. Knowledge mapping, as a structured knowledge management technique, provides an effective solution for systematically integrating knowledge in the task-planning domain. The mission-planning business model is able to systematically capture and portray domain knowledge in mission planning through a formal representation of mission planning processes, rules, and constraints. Thus, it becomes an important source of knowledge for mission-planning knowledge mapping. This paper proposes a business-model-driven knowledge graph construction method for mission planning. First, under the support of conceptual business knowledge, the multidimensional task-planning ontology network expression method is utilized to construct the task-planning ontology network, and then the data-based business knowledge is structured to transform it into business data mapping to complete the acquisition of business knowledge. Then, the task-planning ontology network is constructed using the multidimensional task-planning ontology network representation method under the support of conceptual knowledge. Subsequently, a domain knowledge categorization algorithm based on Ullman subgraph matching is used to realize the matching mapping between the ontology network and business data mapping to complete the categorization of task-planning domain knowledge. Finally, the generated task-planning domain knowledge graph is stored in the Neo4j graph database. In order to ensure the completeness of the knowledge graph, an adaptive adjustment method based on its actual effectiveness is conceived, which is able to detect and adjust the completeness of the knowledge graph. The effectiveness of the proposed methodology is validated by constructing a space-station mission-planning knowledge graph driven by a space-station mission-planning business model.

Applied SciencesVol. 14(23)
Harbin Institute of Technology (CN)
Sustainable cities and communities
Openalex Percentile: Top 80%
Semantic Web and Ontologies
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