<p>With the explosion of information, enormous volumes of data are created, collected and stored. However, one of the major challenging tasks is that the type of data is diverse, heterogeneous and fragmented. The availability of integrating of enormous volumes of data has allowed successfully covering the information from multiple sources. Integrating data from multiple sources is of great significance to draw collective information of multi-faceted concept. The data and knowledge extracting from multiple sources should be incorporated to cover its versatility in multi-faceted conceptual modelling. Meanwhile, as a large and complex representation, graph-structured data is the natural target to cover the domain information in many applications, such as social networks and molecular structures. The graph-structured data comprises rich relational information among elements, which receives considerable attention in recent years. &nbsp;For example, a knowledge graph is a typical type of graph-structured data that contains the data and knowledge. Typically, a knowledge graph is composed of entities (nodes) and edges. Edge is regarded as a fact, which shows a specific relational connection between two nodes. As an abstraction to encode knowledge in a specific domain, constructing knowledge graphs based on graph-structured data provides a promising way to integrate the rich relational information among elements. Therefore, on the basis of integration of multi-source information, exploiting knowledge graphs has allowed modelling multi-faceted considerably. As a promising study, we believe that this research will greatly help in multi-faceted modelling in many industrial applications.</p>

Exploiting Graph-Structured Data for Multi-Faceted Conceptual Modelling

Paper Proceedings: https://asmedigitalcollection.asme.org/IDETC-CIE/IDETC-CIE2021/volume/85369

ASME 2021 International Design Engineering Technical Conferences & Computers and Information in Engineering Conference (IDETC/CIE2021)

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