ifcOWL-DfMA a new ontology for the offsite construction domain

Architecture, Engineering and Construction (AEC) is a fragmented in-dustry dealing with heterogeneous data formats coming from different domains. Building Information Modelling (BIM) is one of the most important efforts to manage information collaboratively within the AEC industry. The Industry Foun-dation Classes (IFC) can be used as a data format to achieve data exchange be-tween diverse software applications in a BIM process. The advantage of using Semantic Web Technologies to overcome these challenges has been recognised by the AEC community and the ifcOWL ontology, which transforms the IFC schema to a Web Ontology Language (OWL) representation, is now a de facto standard. Even though the ifcOWL ontology is very extensive, there is a lack of detailed knowledge representation in terms of process and sub-processes explain-ing Design for Manufacturing and Assembly (DfMA) for offsite construction, and also a lack of knowledge on how product and productivity measurement such as production costs and durations are incurred, which is essential for evaluation of alternative DfMA design options. In this article we present a new ontology named ifcOWL-DfMA as a new domain specific module for ifcOWL with the aim of representing offsite construction domain terminology and relationships in a machine-interpretable format. This ontology will play the role of a core vocab-ulary for the DfMA design management and can be used in many scenarios such as life cycle cost estimation. To demonstrate the usage of ifcOWL-DfMA ontol-ogy a production line of wall panels is presented. We evaluate our approach by querying the wall panel production model about information such as activity se-quence, cost estimation per activity and also the direct material cost. This ulti-mately enable users to evaluate the overall product from the system

Knowledge graph embeddings:Open challenges and opportunities

While Knowledge Graphs (KGs) have long been used as valuable sources of structured knowledge, in recent years, KG embeddings have become a popular way of deriving numeric vector representations from them, for instance, to support knowledge graph completion and similarity search. This study surveys advances as well as open challenges and opportunities in this area. For instance, the most prominent embedding models focus primarily on structural information. However, there has been notable progress in incorporating further aspects, such as semantics, multi-modal, temporal, and multilingual features. Most embedding techniques are assessed using human-curated benchmark datasets for the task of link prediction, neglecting other important real-world KG applications. Many approaches assume a static knowledge graph and are unable to account for dynamic changes. Additionally, KG embeddings may encode data biases and lack interpretability. Overall, this study provides an overview of promising research avenues to learn improved KG embeddings that can address a more diverse range of use cases