Biomedical ontology alignment: An approach based on representation learning

While representation learning techniques have shown great promise in application to a number of different NLP tasks, they have had little impact on the problem of ontology matching. Unlike past work that has focused on feature engineering, we present a novel representation learning approach that is tailored to the ontology matching task. Our approach is based on embedding ontological terms in a high-dimensional Euclidean space. This embedding is derived on the basis of a novel phrase retrofitting strategy through which semantic similarity information becomes inscribed onto fields of pre-trained word vectors. The resulting framework also incorporates a novel outlier detection mechanism based on a denoising autoencoder that is shown to improve performance. An ontology matching system derived using the proposed framework achieved an F-score of 94% on an alignment scenario involving the Adult Mouse Anatomical Dictionary and the Foundational Model of Anatomy ontology (FMA) as targets. This compares favorably with the best performing systems on the Ontology Alignment Evaluation Initiative anatomy challenge. We performed additional experiments on aligning FMA to NCI Thesaurus and to SNOMED CT based on a reference alignment extracted from the UMLS Metathesaurus. Our system obtained overall F-scores of 93.2% and 89.2% for these experiments, thus achieving state-of-the-art results

Integrated product relationships management : a model to enable concurrent product design and assembly sequence planning

The paper describes a novel approach to product relationships management in the context of concurrent engineering and product lifecycle management (PLM). Current industrial practices in product data management and manufacturing process management systems require better efficiency, flexibility, and sensitivity in managing product information at various levels of abstraction throughout its lifecycle. The aim of the proposed work is to manage vital yet complex and inherent product relationship information to enable concurrent product design and assembly sequence planning. Indeed, the definition of the product with its assembly sequence requires the management and the understanding of the numerous product relationships, ensuring consistency between the product and its components. This main objective stresses the relational design paradigm by focusing on product relationships along its lifecycle. This paper gives the detailed description of the background and models which highlight the need for a more efficient PLM approach. The proposed theoretical approach is then described in detail. A separate paper will focus on the implementation of the proposed approach in a PLM-based application, and an in-depth case study to evaluate the implementation of the novel approach will also be given

The Industrial Ontologies Foundry proof-of-concept project

The current industrial revolution is said to be driven by the digitization that exploits connected information across all aspects of manufacturing. Standards have been recognized as an important enabler. Ontology-based information standard may provide benefits not offered by current information standards. Although there have been ontologies developed in the industrial manufacturing domain, they have been fragmented and inconsistent, and little has received a standard status. With successes in developing coherent ontologies in the biological, biomedical, and financial domains, an effort called Industrial Ontologies Foundry (IOF) has been formed to pursue the same goal for the industrial manufacturing domain. However, developing a coherent ontology covering the entire industrial manufacturing domain has been known to be a mountainous challenge because of the multidisciplinary nature of manufacturing. To manage the scope and expectations, the IOF community kicked-off its effort with a proof-of-concept (POC) project. This paper describes the developments within the project. It also provides a brief update on the IOF organizational set up

Analysis of plastic waste circularity through LCA

Upcycling processes are better aligned with the Circular Economy model, which defends that the plastic waste is a valuable resource with the potential to be recirculated in a new material cycle. To ensure the highest number of cycles, products, components and material should be kept at their highest utility and value (Webster, 2017). However, this is not what is happening in the recycling sector because upcycling processes are more complex, and energy and resource-intensive. As a result, the environmental benefits of plastic upcycling are frequently called into question and downcycling methods are implemented owing to their lower complexity and costs, regardless of the irreversible and meaningful loss of quality. In this work, three plastic waste management scenarios have been assessed to determine their potential to contribute to the implementation of the Circular Economy. The chosen waste treatment methods are upcycling of plastic scrap through deinking technology, downcycling by re-extrusion and, finally, incineration. The environmental impacts have been computed through LCA methodology. The results show that depending on the assumptions made, LCA can lead to conclusions which are opposite to the Circular Economy principles, thus favouring the downcycling and incineration of plastic waste with high potential to be recirculated. Therefore, to make a fairer comparison between upcycling and other waste treatment options, two modifications have been suggested. First, the target market for recycled pellets should be included in the computation since it is reliant on the material´s quality. Downcycled dark pellets can be used in applications which cover 24% of the total market. Conversely, upcycled pellets can reach 100% of the market. And second, the energy produced during incineration cannot substitute the energy from fossil fuels. The heating value of plastics is usually higher than the energy consumed during raw pellets production. Therefore, recycling will be always seen as the least favourable option. Nevertheless, according to the Circular Economy principles, the energy has to come from renewable sources. Therefore, if our society is moving forward to this new model, fossil fuels should not be considered. Finally, it has been demonstrated that increasing the quality of recycled plastics through upcycling processes is more beneficial than increasing the recycling rates. This is to say that recycle more is good, but what is needed is to recycle better. This work is aligned with two of the conference topics: LCA of municipal and industrial waste management scenarios. LCA of the management of specific waste streams in a circular economy perspective. Reference: Webster, K., 2017. The circular economy: A wealth of flows. Ellen MacArthur Foundation Publishin

A competence-based industrial learning approach for factories of the future

Manufacturing industry can improve its competitiveness through innovation and technological excellence, and appropriate Industrial Learning can help to achieve this goal through allowing the manufacturing workforce to acquire new skills related to the advanced developments in information and communication technologies. This raises the need for new Industrial Learning tools and methods from the viewpoint of learning content, learning processes, and delivery mechanisms. In this paper, we present a generic competence-based approach for Industrial Learning developed in the framework of ActionPlanT project. The approach is composed of (i) an Industrial Learning model which serves to represent and understand competence-based learning, and (ii) a methodology which implements through a number of steps the Industrial Learning actions defined using the Industrial Learning model in industrial organisations. Both the model and the methodology are presented in details. A metrics-based method for evaluating the implementation of the learning actions defined using the approach is also describe

A First-Order Logic Formalization of the Industrial Ontology Foundry Signature Using Basic Formal Ontology

Basic Formal Ontology (BFO) is a top-level ontology used in hundreds of active projects in scientific and other domains. BFO has been selected to serve as top-level ontology in the Industrial Ontologies Foundry (IOF), an initiative to create a suite of ontologies to support digital manufacturing on the part of representatives from a number of branches of the advanced manufacturing industries. We here present a first draft set of axioms and definitions of an IOF upper ontology descending from BFO. The axiomatization is designed to capture the meanings of terms commonly used in manufacturing and is designed to serve as starting point for the construction of the IOF ontology suite

An Ontology-based model for providing Semantic Maintenance.

International audienceMaintenance is becoming more and more crucial in Asset Lifecycle Management information models. Issues such as collecting, handling and using the asset data produced during its lifecycle in a lean and efficient manner are on top of today's research. Customer satisfaction, compliance with environmental friendly legislation, product quality, high performance and reliability are only a few of the benefits improved maintenance methods and tools may provide to enterprises. In this work we combine the benefits of two previous developed models and we develop a model for Semantic Maintenance. The first model we are based on is the PROMISE semantic object model which was made for supporting Closed-Loop Product Lifecycle Management. The second model is the semantic model of e-maintenance developed in PROTEUS project. The new model described in this paper is named “SMAC-Model”. Its aim is to provide advanced maintenance services as well as feedback for the Beginning of Life and input for End of Life. The model is generic and may be used in various Asset Lifecycle Management cases. It is developed to facilitate complex physical assets and to work in industrial environment