AIERO: An algorithm for identifying engineering relationships in ontologies

Semantic technologies are playing an increasingly popular role as a means for advancing the capabilities of knowledge management systems. Among these advancements, researchers have successfully leveraged semantic technologies, and their accompanying techniques, to improve the representation and search capabilities of knowledge management systems. This paper introduces a further application of semantic techniques. We explore semantic relatedness as a means of facilitating the development of more “intelligent” engineering knowledge management systems. Using semantic relatedness quantifications to analyze and rank concept pairs, this novel approach exploits semantic relationships to help identify key engineering relationships, similar to those leveraged in change management systems, in product development processes. As part of this work, we review several different semantic relatedness techniques, including a meronomic technique recently introduced by the authors. We introduce an aggregate measure, termed “An Algorithm for Identifying Engineering Relationships in Ontologies,” or AIERO, as a means to purposely quantify semantic relationships within product development frameworks. To assess its consistency and accuracy, AIERO is tested using three separate, independently developed ontologies. The results indicate AIERO is capable of returning consistent rankings of concept pairs across varying knowledge frameworks. A PCB (printed circuit board) case study then highlights AIERO’s unique ability to leverage semantic relationships to systematically narrow where engineering interdependencies are likely to be found between various elements of product development processes

Towards Industrial Implementation of Emerging Semantic Technologies

Every new design, project, or procedure within a company generates a considerable amount of new information and important knowledge. Furthermore, a tremendous amount of legacy knowledge already exists in companies in electronic and non-electronic formats, and techniques are needed for representing, structuring and reusing this knowledge. Many researchers have spent considerable time and effort developing semantic knowledge management systems, which in theory are presumed to address these problems. Despite significant research investments, little has been done to implement these systems within an industrial setting. In this paper we identify five main requirements to the development of an industry-ready application of semantic knowledge management systems and discuss how each of these can be addressed. These requirements include the ease of new knowledge management software adoption, the incorporation of legacy information, the ease of use of the user interface, the security of the stored information, and the robustness of the software to support multiple file types and allow for the sharing of information across platforms. Collaboration with Raytheon, a defense and aerospace systems company, allowed our team to develop and demonstrate a successful adoption of semantic abilities by a commercial company. Salient features of this work include a new tool, the e-Design MemoExtractor Software Tool, designed to mine and capture company information, a Raytheon-specific extension to the e-Design Framework, and a novel semantic environment in the form of a customized semantic wikiSMW+. The advantages of this approach are discussed in the context of the industrial case study with Raytheon

A Semantic Information Model for Capturing and Communicating Design Decisions

A semantic information model to improve reuse and communication of engineering design knowledge is presented in this paper. We consider design to be a process involving a sequence of decisions informed by the current state of information. As such, the information model developed is structured to reflect the conceptualizations of engineering design decisions with a particular emphasis on semantically capturing design rationale. Through the approach presented, knowledge reuse is achieved by communicating design rationale. A case study is presented to illustrate two key features of the approach: (1) seamless integration of separate modular domain ontologies and instance knowledge related to engineering design that are needed to support decision making and (2) the explicit documentation of design rationale through design decisions

An Efficient Method of Modeling Material Properties Using a Thermal Diffusion Analogy: An Example Based on Craniofacial Bone

The ability to incorporate detailed geometry into finite element models has allowed researchers to investigate the influence of morphology on performance aspects of skeletal components. This advance has also allowed researchers to explore the effect of different material models, ranging from simple (e.g., isotropic) to complex (e.g., orthotropic), on the response of bone. However, bone's complicated geometry makes it difficult to incorporate complex material models into finite element models of bone. This difficulty is due to variation in the spatial orientation of material properties throughout bone. Our analysis addresses this problem by taking full advantage of a finite element program's ability to solve thermal-structural problems. Using a linear relationship between temperature and modulus, we seeded specific nodes of the finite element model with temperatures. We then used thermal diffusion to propagate the modulus throughout the finite element model. Finally, we solved for the mechanical response of the finite element model to the applied loads and constraints. We found that using the thermal diffusion analogy to control the modulus of bone throughout its structure provides a simple and effective method of spatially varying modulus. Results compare favorably against both experimental data and results from an FE model that incorporated a complex (orthotropic) material model. This method presented will allow researchers the ability to easily incorporate more material property data into their finite element models in an effort to improve the model's accuracy

Genome-wide association for major depression through age at onset stratification:Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium

Background Major depressive disorder (MDD) is a disabling mood disorder and, despite a known heritable component, a large meta-analysis of GWAS revealed no replicable genetic risk variants. Given prior evidence of heterogeneity by age-at-onset (AAO) in MDD, we tested whether genome-wide significant risk variants for MDD could be identified in cases subdivided by AAO. Method Discovery case-control GWASs were performed where cases were stratified using increasing/decreasing AAO-cutoffs; significant SNPs were tested in nine independent replication samples, giving a total sample of 22,158 cases and 133,749 controls for sub-setting. Polygenic score analysis was used to examine if differences in shared genetic risk exists between earlier and adult onset MDD with commonly co-morbid disorders of schizophrenia, bipolar disorder, Alzheimer’s disease, and coronary artery disease. Results We identify one replicated genome-wide significant locus associated with adult-onset (>27 years) MDD (rs7647854, OR=1.16, 95%CI=1.11-1.21, p=5.2x10-11). Using polygenic score analyses, we show that earlier-onset MDD is genetically more similar to schizophrenia and bipolar disorder than adult-onset. Conclusions We demonstrate that using additional phenotype data previously collected by genetic studies to tackle phenotypic heterogeneity in MDD can successfully lead to the discovery of genetic risk factor despite reduced sample size. Furthermore, our results suggest that the genetic susceptibility to MDD differs between adult- and earlier-onset MDD, with earlier-onset cases having a greater genetic overlap with schizophrenia and bipolar disorder