Integrating Biological and Engineering Ontologies

Abstract

As methods for engineering data acquisition improve, methods for storing, generating knowledge from, and sharing that data for efficient reuse have become more important. Knowledge management in the engineering community can greatly benefit from advancements made in knowledge management in biology. The biological community has already made progress in knowledge management through projects such as the Gene Ontology and CellML, and it behooves the engineering community to learn from their successes. Engineering and biology overlap in the field of biosimulation, (i.e. finite-element analysis of biological systems, see www.biomesh.org) which gives an opportunity to integrate successful ontologies from the biology community into the engineering community. Previous research has led to the creation of the Biomesh project, which is a collection of biological finite element (FE) models. These FE models relate to a particular anatomical structure of an organism, and to the set of biological material properties associated with the models. Thus, knowledge management for this application requires knowledge integration from three distinct fields: engineering (materials and models), anatomy, and biological classification. The existing e-Design Framework offers the Engineering Analysis Models ontology and Materials ontology to store knowledge about materials and FE models. Similarly, the existing Minimal Anatomical Terms ontology and the NCBI Organismal Classification taxonomy were used to store information about anatomy and biological classification, respectively. In this paper these ontologies are interlinked in a single, synergistic ontology to expose and integrate knowledge in a transparent manner between previously disparate domains. A case study is presented to demonstrate the usefulness of the approach in which knowledge from a biological material and FE model are methodically stored in the new ontology, and the organismal classification and anatomical structure of the model are immediately exposed to the user.

Food mechanical properties and dietary ecology

“…As many researchers will be interested in the properties gained from these studies, for both comparative and meta‐studies (Dominy et al, ; Onoda et al, ), the construction of a database for sharing raw data will become increasingly important. The biological community has become characterized by the sharing of large amounts of data in recent years, particularly through the use of ontologies (Grosse et al, ; Rockwell et al, ; McPherson et al, ; McPherson, ), two of the most famous being the Gene Ontology and Open Biomedical Ontologies consortiums (Ashburner et al, ; Smith et al, ).…”

Semantic Computing for Big Data: Approaches, Tools, and Emerging Directions (2011-2014)

“…• Ontology engineering and big data o Construction ontology: Large-scale structured, unstructured, and semi-structured data is transferred into ontological forms. A number of approaches have been proposed to deal with this issue [36] [37] [38] [40] [41] [52] [53]. o Big data access: Since accessing relevant information is an increasingly difficult task, the concept of ontology-based data access (OBDA) [9] has been proposed as a suitable approach.…”

A Knowledge-Based Method for Innovative Design for Additive Manufacturing Supported by Modular Ontologies

“…Others have introduced modular and cross-domain ontologies for the purpose of aiding in specific engineering activities. Past efforts include the e-design framework, a modular set of ontologies that aim to coordinate information relating to engineering analysis [35], engineering optimization [39], design decisions [40], design innovation [34], engineering relationships [41], laminated composites [42], product innovation [43], biomodels [44], medical device conceptual design [45], and ergonomic design principles [46]. Additive manufacturing has also been an area of interest for knowledge management through ontologies and other information models [42].…”