Automated Extraction and Creation of FBS Design Reasoning Knowledge Graphs from Structured Data in Product Catalogues Lacking Contextual Information
Vijayalaxmi Sahadevan, Sushil Mario, Yash Jaiswal, Divyanshu Bajpai,, Vishal Singh, Hiralal Aggarwal, Suhas Suresh, Manjunath Maigur

TL;DR
This paper presents a rule-based method and workflow for automatically extracting and creating FBS ontology-based knowledge graphs from structured product data like catalogues, addressing the lack of contextual information.
Contribution
It introduces a novel workflow that automates FBS knowledge graph creation from legacy structured data using rule-based techniques and NLP, filling a gap in existing methods.
Findings
Effective in industrial pilot implementation
Addresses structured data without standard ontologies
Highlights challenges and opportunities in knowledge graph creation
Abstract
Ontology-based knowledge graphs (KG) are desirable for effective knowledge management and reuse in various decision making scenarios, including design. Creating and populating extensive KG based on specific ontological models can be highly labour and time-intensive unless automated processes are developed for knowledge extraction and graph creation. Most research and development on automated extraction and creation of KG is based on extensive unstructured data sets that provide contextual information. However, some of the most useful information about the products and services of a company has traditionally been recorded as structured data. Such structured data sets rarely follow a standard ontology, do not capture explicit mapping of relationships between the entities, and provide no contextual information. Therefore, this research reports a method and digital workflow developed to…
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Taxonomy
TopicsManufacturing Process and Optimization · Industrial Technology and Control Systems
MethodsOntology
