日本経団連が国家エネルギー戦略確立を提言

This paper describes the outcomes of an ongoing collaboration between Siemens and the University of Oxford, with the goal of facilitating the design of ontologies and their deployment in applications. Ontologies are often used in industry to capture the conceptual information models underpinning applications. We start by describing the role that such models play in two use cases in the manufacturing and energy production sectors. Then, we discuss the formalisation of information models using ontologies, and the relevant reasoning services. Finally, we present SOMM—a tool that supports engineers with little background on semantic technologies in the creation of ontology-based models and in populating them with data. SOMM implements a fragment of OWL 2 RL extended with a form of integrity constraints for data validation, and it comes with support for schema and data reasoning, as well as for model integration. Our preliminary evaluation demonstrates the adequacy of SOMM’s functionality and performance

Task-driven knowledge graph filtering improves prioritizing drugs for repurposing.

BACKGROUND: Drug repurposing aims at finding new targets for already developed drugs. It becomes more relevant as the cost of discovering new drugs steadily increases. To find new potential targets for a drug, an abundance of methods and existing biomedical knowledge from different domains can be leveraged. Recently, knowledge graphs have emerged in the biomedical domain that integrate information about genes, drugs, diseases and other biological domains. Knowledge graphs can be used to predict new connections between compounds and diseases, leveraging the interconnected biomedical data around them. While real world use cases such as drug repurposing are only interested in one specific relation type, widely used knowledge graph embedding models simultaneously optimize over all relation types in the graph. This can lead the models to underfit the data that is most relevant for the desired relation type. For example, if we want to learn embeddings to predict links between compounds and diseases but almost the entirety of relations in the graph is incident to other pairs of entity types, then the resulting embeddings are likely not optimised to predict links between compounds and diseases. We propose a method that leverages domain knowledge in the form of metapaths and use them to filter two biomedical knowledge graphs (Hetionet and DRKG) for the purpose of improving performance on the prediction task of drug repurposing while simultaneously increasing computational efficiency. RESULTS: We find that our method reduces the number of entities by 60% on Hetionet and 26% on DRKG, while leading to an improvement in prediction performance of up to 40.8% on Hetionet and 14.2% on DRKG, with an average improvement of 20.6% on Hetionet and 8.9% on DRKG. Additionally, prioritization of antiviral compounds for SARS CoV-2 improves after task-driven filtering is applied. CONCLUSION: Knowledge graphs contain facts that are counter productive for specific tasks, in our case drug repurposing. We also demonstrate that these facts can be removed, resulting in an improved performance in that task and a more efficient learning process

Shape encoding for semantic healing of design models and knowledge transfer to scan-to-BIM

Automated parsing of design data will increasingly be a prerequisite for efficient data- and analytics-driven management of building portfolios. The high complexity and low rigidity of building information modelling (BIM) model exchange standards such as Industry Foundation Classes result in considerable differences in data quality and impede direct data availability for analytics-based decision support. Mis- or unclassified building elements are a common issue and can lead to tedious manual reworks. At the same time, scan-to-BIM processes still require considerable manual effort to identify subclass element geometry. This work leverages the benefits of a three-dimensional lightweight, geometric algorithm to generate meaningful geometric features autonomously that assist shape classification in erroneous design models and pre-segmented point clouds. Geometric deep learning is introduced in two steps; a discussion about the benefits of graph convolutional networks (GCNs) is given before a set of experiments on BIM element data sets is conducted. Utilising explainable artificial intelligence methods, the GCN performance is made suitable for human-algorithm interaction. Leveraging element geometry solely, the classification reaches a promising average performance of above 83% for the model-healing task with a reduced computation time. The encoded geometric knowledge from the design models is shown to be helpful in showcasing examples of segment classification in point clouds.ISSN:2397-875

Event-enhanced learning for KG completion

Statistical learning of relations between entities is a popular approach to address the problem of missing data in Knowledge Graphs. In this work we study how relational learning can be enhanced with background of a special kind: event logs, that are sequences of entities that may occur in the graph. Events naturally appear in many important applications as background. We propose various embedding models that combine entities of a Knowledge Graph and event logs. Our evaluation shows that our approach outperforms state-of-the-art baselines on real-world manufacturing and road traffic Knowledge Graphs, as well as in a controlled scenario that mimics manufacturing processes

SOMM: Industry Oriented Ontology Management Tool

This paper describes the outcomes of an ongoing collaboration between Siemens and the University of Oxford, with the goal of facilitating the design of ontologies and their deployment in applications. Ontologies are mainly used in Siemens to capture the conceptual information models underpinning a wide range of applications. We start by describing the key role that such models play in two use cases in the manufacturing and energy production sectors. Then, we discuss the formalisation of information models using ontologies, and the relevant reasoning services. Finally, we present SOMM—a tool that supports engineers with little background on semantic technologies in the creation of ontology-based models and in populating them with data. SOMM implements a fragment of OWL 2 RL extended with a form of integrity constraints for data validation, and it comes with support for schema and data reasoning, as well as for model integration. Our evaluation demonstrates the adequacy of SOMM’s functionality and performance for Siemens applications

Event-Enhanced Learning for {KG} Completion

Statistical learning of relations between entities is a popular approach to address the problem of missing data in Knowledge Graphs. In this work we study how relational learning can be enhanced with background of a special kind: event logs, that are sequences of entities that may occur in the graph. Events naturally appear in many important applications as background. We propose various embedding models that combine entities of a Knowledge Graph and event logs. Our evaluation shows that our approach outperforms state-of-the-art baselines on real-world manufacturing and road traffic Knowledge Graphs, as well as in a controlled scenario that mimics manufacturing processes

Capturing industrial information models with ontologies and constraints

This paper describes the outcomes of an ongoing collaboration between Siemens and the University of Oxford, with the goal of facilitating the design of ontologies and their deployment in applications. Ontologies are mainly used in Siemens to capture the conceptual information models underpinning a wide range of applications. We start by describing the key role that such models play in two use cases in the manufacturing and energy production sectors. Then, we discuss the formalisation of information models using ontologies, and the relevant reasoning services. Finally, we present SOMM|a tool that supports engineers with little background on semantic technologies in the creation of ontology-based models and in populating them with data. SOMM implements a fragment of OWL 2 RL extended with a form of integrity constraints for data validation, and it comes with support for schema and data reasoning, as well as for model integration. Our evaluation demonstrates the adequacy of SOMM's functionality and performance for Siemens applications