Analogical reasoning over Multimodal Knowledge Graphs (MMKGs) couples abductive relation induction with inductive tail completion. However, existing approaches rely on static fusion mechanisms that overlook the inherent asymmetry of modal relevance: while visual cues elucidate concrete entities, they are often noisy or irrelevant for abstract concepts, where text and graph structure provide decisive signals. Furthermore, prior methods fail to enforce consistency between induced relations and the modality patterns implied by the analogical context. To bridge this gap, we introduce RMAR, a Relation-aware Multimodal Analogical Reasoning framework with two complementary paths. An explicit path estimates modality fingerprints to score compatibility during relation induction and guide fusion during tail completion. An implicit path employs adaptive gating to blend structural, textual, and visual signals conditioned on the specific query context. To address the limitations of current benchmarks, which overrepresent concrete entities, we release MCNetAnalogy, and its companion graph, MCNetKG, a rigorous dataset enriched with abstract concepts and actions. RMAR is backbone-agnostic and works with multimodal knowledge graph embedding (MKGE) and transformer-based (MPT) pipelines. Extensive experiments demonstrate that RMAR delivers consistent gains across both embedding-based and transformer-based backbones, achieving a 29% relative improvement on MCNetAnalogy. Ablation studies confirm that RMAR's relation-aware modulation is particularly effective when modal evidence is weak or ambiguous.

Water treatment plants face growing challenges in managing complex infrastructure assets due to fragmented data systems and poor interoperability between design and operational tools. Traditional workflows require manual re-entry of Building Information Modeling (BIM) data into asset management platforms, leading to inefficiencies and information loss. This paper introduces WAM-ONTO, a semantic framework designed to enhance water infrastructure asset management by leveraging building information modeling, ontologies, and rule-based reasoning. WAM-ONTO integrates the Industry Foundation Classes (IFC4) standard with Web Ontology Language (OWL2) to formally represent asset types, lifecycle properties, and spatial-functional relationships. The framework comprises 996 ontology classes structured into twelve domain-specific categories, enriched with 61 object properties and 89 data properties. Semantic reasoning is implemented using SWRL rules and SPARQL queries, enabling automated classification, risk profiling, and maintenance prioritization. The framework was validated through expert interviews with fourteen domain specialists, consistency checks using multiple reasoners, and performance tests demonstrating 94.2% domain coverage and 91% automated classification accuracy on real-world facility data. Expert validation achieved 96% consensus on practical utility and industry alignment. A Clean-in-Place system case study demonstrates WAM-ONTO’s ability to preserve design knowledge during BIM-to-operations transitions while enabling real-time decision support for maintenance planning and risk assessment.

The ability for computers to interpret and utilise computerised building regulations is crucial for automated compliance checking of buildings, enabling a faster and more objective building permit review. Since natural language regulations are often not designed to be represented formally, integrating formal representations into the regulation drafting process could drive this change. Through in-depth qualitative expert interviews with regulators worldwide, this study examines the drafting process of building regulations. It aims to establish a methodology for developing formal representations in parallel with natural language versions. To be able to do so, the first step is to understand the current workflow and its requirements. The interviewees in this study highlighted several benefits of parallel drafting, including improved regulation quality and reduced inconsistencies. However, they also acknowledged challenges such as the difficulty of modifying existing laws and the significant time and costs required for such changes. This study explores various strategies for integrating formal representations into the drafting process of building regulations. Notably, in many countries, significant progress has been made towards automated compliance checking. However, much of this progress has taken place independently of legislative bodies, limiting the potential of automation in the building permit process. Nonetheless, early efforts by legislators to adopt formal representations are emerging. This study aims to raise awareness of these developments and contribute to the broader understanding of how formal representations can enhance the regulation drafting process.

Recent developments show that large transformer-based language models have the capability to generate coherent text and source code in response to user prompts. This capability can be used in the construction domain to interpret building regulations and convert them into a formal representation usable for automated compliance checking. While base-size models can already be taught to perform semantic parsing with decent quality, this paper shows how Intermediate Representations (IRs) can be used to improve the semantic parsing quality. With reversible IRs, the training time was reduced to almost a quarter of the initial duration, and through adding a hierarchical parsing step, improvements of up to 6.6% on F1 scores were reached. Furthermore, intermediate representations provide a novel and interpretable method towards a human-in-the-loop approach for translating building regulations into a formal representation.