Automated Extraction and Creation of FBS Design Reasoning Knowledge Graphs from Structured Data in Product Catalogues Lacking Contextual Information
This addresses the challenge of automating knowledge management for companies with legacy structured product data, though it appears incremental as it applies existing rule-based and NLP techniques to a specific domain problem.
The researchers tackled the problem of labor-intensive knowledge graph creation from structured product data lacking contextual information by developing a rule-based method and digital workflow to automatically extract and create Function-Behaviour-Structure ontology-based knowledge graphs from legacy structured data like specification sheets and product catalogues, demonstrating effectiveness through a pilot industrial implementation.
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 address this gap. The developed method and workflow employ rule-based techniques to extract and create a Function Behaviour-Structure (FBS) ontology-based KG from legacy structured data, especially specification sheets and product catalogues. The solution approach consists of two main components: a process for deriving context and context-based classification rules for FBS ontology concepts and a workflow for populating and retrieving the FBS ontology-based KG. KG and Natural Language Processing (NLP) are used to automate knowledge extraction, representation, and retrieval. The workflow's effectiveness is demonstrated via pilot implementation in an industrial context. Insights gained from the pilot study are reported regarding the challenges and opportunities, including discussing the FBS ontology and concepts.