Projecting knowledge graph queries into an embedding space using geometric models (points, boxes and spheres) can help to answer queries for large incomplete knowledge graphs. In this work, we propose a symbolic learning-free approach using fuzzy logic to address the shape-closure problem that restricted geometric-based embedding models to only a few shapes (e.g. ConE) for answering complex logical queries. The use of symbolic approach facilitates non-closure geometric models (e.g. point, box) to handle logical operators (including negation). This enabled our newly proposed spherical embeddings (SpherE) in this work to use a polar coordinate system to effectively represent hierarchical relation. Results show that the SpherE model can answer existential positive first-order logic and negation queries. We show that SpherE significantly outperforms the point and box embeddings approaches while generating semantically meaningful hierarchy-aware embeddings.

Maintenance technicians working in heavy industry play a vital role in sustaining and repairing equipment. Technicians are expected to follow written procedures, usually derived from equipment manuals. Historically maintenance procedures have been paper-based. Organisations are increasingly turning to digital devices, but this transition needs to account for the specific needs of the technicians and the context of their work. In this paper, we assess the impact of a digital tool for maintenance procedures on the work of technicians. To do this, we perform a three-phase industry case study. We use dimensions from the Job Characteristics Model (JCM) (autonomy, feedback, task variety, task identity, task significance) to examine the effects of context-specific design features (i.e., navigation control, task presentation, procedure inputs and feedback, and timed work) on maintenance technicians’ work. Our findings indicate that JCM is a relevant tool for assessing the impact of user interface designs on manual work.

Maintenance short texts (MST), derived from maintenance work order records, encapsulate crucial information in a concise yet information-rich format. These user-generated technical texts provide critical insights into the state and maintenance activities of machines, infrastructure, and other engineered assets–pillars of the modern economy. Despite their importance for asset management decision-making, extracting and leveraging this information at scale remains a significant challenge. This paper presents MaintIE, a multi-level fine-grained annotation scheme for entity recognition and relation extraction, consisting of 5 top-level classes: PhysicalObject, State, Process, Activity and Property and 224 leaf entities, along with 6 relations tailored to MSTs. Using MaintIE, we have curated a multi-annotator, high-quality, fine-grained corpus of 1,076 annotated texts. Additionally, we present a coarse-grained corpus of 7,000 texts and consider its performance for bootstrapping and enhancing fine-grained information extraction. Using these corpora, we provide model performance measures for benchmarking automated entity recognition and relation extraction. The MaintIE scheme, corpus, and model are publicly available at https://github.com/nlp-tlp/maintie under the MIT license, encouraging further community exploration and innovation in extracting valuable insights from MSTs.

Abstract Long term impacts of shortcuts and compromises taken during software development are described by the metaphor Technical Debt (TD). TD is an emerging business issue in modern interdependent software systems. Our focus is on personnel involved in managing complex adaptive software systems used in automated Remote Operations Centers (ROC’s) responsible for operating industrial equipment more than 1200 km away in the Australian outback. Recognizing and managing TD in these complex adaptive software systems is challenging. This paper builds on advances Serious Games have made toward improving situational awareness, applying these lessons to TD. The prototype Serious Game TD-Sim is tested with employees working in a ROC. Thematic analysis is used to assess pre- and post-game impacts on situational awareness of TD and this is triangulated with in-game information trails. A baseline efficacy of improved TD awareness is established, sufficient to warrant further game development. Results also identify areas for improving future game design. This study is unique, applying a Serious Game to build awareness of TD in complex adaptive software systems, something yet to be covered by traditional TD research.

Maintenance of industrial equipment is done by fitters, electricians and other maintainers. For safety and quality control, maintainers must follow procedures; historically these have been paper-based. Asset-owning organisations seek to transition maintainers to digital platforms. However, there are limited studies on the potential impact of digitisation on maintenance work and the maintainers that perform it. Our challenge is to identify interface design considerations that support the safe and reliable execution of work. We looked specifically at maintenance procedures and conducted semi-structured interviews with process-plant maintainers. Thematic analysis identified eight factors influencing maintainers’ perceptions towards using digital technologies in their work. We map these factors to three categories, work identity, agency and community. These categories are consistent with concepts from the Job Characteristics Model (JCM). The contribution of this work is the relevance of job characteristics in guiding user interface design for maintainers, for which we make a number of recommendations.

Engineered transport networks occur in many domains: road networks, power grids, the internet and utility distribution. Such networks present a generic problem — how does one model and predict failure of components within them? In this paper we will use the example of a metropolitan water distribution network to model and predict failure based on topology, topological network distance, and spatial-temporal geographical distance. We show that certain topologies are either more or less likely to fail than chance, and that co-occurrence of failure within certain geographical distances can be reliably modelled and predicted in certain specific circumstances.

This paper develops a prognostics model to estimate the Mean Residual Life of Rail Wagon Bearings within certain confidence intervals. The prognostics model is constructed using a Proportional Hazards approach, informed by imperfect data from a bearing acoustic monitoring system, and a failure database. The model supports prediction within a defined maintenance planning window from the time of receipt of the latest acoustic condition monitoring information. We use the model to decide whether to replace a bearing, or leave it until collection of the next condition monitoring indicators. The model is tested on a limited number of cases, and demonstrates good predictive capability. Opportunities to improve the performance of the model are identified, and the processes necessary and time required to build the model are described. Lessons learned from dealing with real field data will assist those interested in using prognostics to support maintenance planning activities.