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Analysis of architectural space is commonly conducted by examining architectural drawings that project spatial information by means of walls and partitions. To capture the lived experience of space, which is richer than what we can see from drawings, a new method is proposed to quantify the cognitive dimension of space and re-present it as an audible format.

Using an urban vernacular house in Seoul as a case study, this research takes a syntactic approach to quantify one's changing perception through their movement from the main gate to the most private reception room. Based on Luigi Moretti's theory of hollow space, a new method is proposed to measure the level of spatial pressure exerted on a navigating body. The numerical data of spatial pressure are then converted to a sound using musical techniques of the chromatic scale and chorale textures.

Building on Moretti's abstract concept, it has been shown that a rule-based quantification of users' spatial perception is possible. In addition, unlike conventional approaches of treating architecture as a static entity, this study showed an alternative approach to represent it as a sequence of sensorial experience that can be readily converted to a sound of music.

This research developed a quantification method to measure the perception of pressure inside buildings by revisiting Luigi Moretti's theory proposed in 1952. It has been also demonstrated that the visual stimuli in space can be translated into an audible experience. This new method is applicable to a wide range of buildings including important historic architecture.

The safety-related features of destinations affect tourist experiences and consequently influence destination choices. This research investigates the role of spatial profile and safety in the destination choices of digital nomads.

The study was designed using the multi-research method. To determine the spatial patterns of digital nomads' destination choices, Getis-Ord’s Gi is utilized, and spatial regression techniques are employed to ascertain the role of safety in these choices.

The main result of the research is that the most visited cities are spatially clustered in Asia, Europe and America. In this regard, digital nomads' destination choices exhibit similarities to those of traditional tourists. However, safety plays a significant role in destination preferences.

The research findings provide valuable insight into the relationship between digital nomads' travel preferences and safety, thereby serving as a significant source of information for destination marketing and management.

The purpose of this study is to discover what attributes of casino buffet restaurants are the most important for customers’ willingness to pay (WTP).

Choice-based conjoint analysis was used in this study to test seven attributes: food, price/value, real price, service, atmosphere, the number of reviews and user-generated star ratings. Sawtooth Software was used to do the conjoint analysis, and a series of significance t-tests were run to determine the significance of each attribute on WTP with Statistical Package for the Social Sciences (SPSS).

Based on a survey of 483 respondents who had visited a buffet at a casino within the last two years, this study found that food is ranked as the most significant attribute of a casino buffet restaurant, followed by real price and service quality.

Theoretically, this work is the first to the authors’ knowledge to apply the antecedents of behavioral intention to willingness-to-pay for niche restaurants. Practically, the results of this study will help casino buffet operators as they re-open after COVID-19. Future studies could collect data in the post-pandemic environment and examine WTP at casino buffets in different geographic locations.

The compliance checking of Building Information Modeling (BIM) models is crucial throughout the lifecycle of construction. The increasing amount and complexity of information carried by BIM models have made compliance checking more challenging, and manual methods are prone to errors. Therefore, this study aims to propose an integrative conceptual framework for automated compliance checking of BIM models, allowing for the identification of errors within BIM models.

This study first analyzed the typical building standards in the field of architecture and fire protection, and then the ontology of these elements is developed. Based on this, a building standard corpus is built, and deep learning models are trained to automatically label the building standard texts. The Neo4j is utilized for knowledge graph construction and storage, and a data extraction method based on the Dynamo is designed to obtain checking data files. After that, a matching algorithm is devised to express the logical rules of knowledge graph triples, resulting in automated compliance checking for BIM models.

Case validation results showed that this theoretical framework can achieve the automatic construction of domain knowledge graphs and automatic checking of BIM model compliance. Compared with traditional methods, this method has a higher degree of automation and portability.

This study introduces knowledge graphs and natural language processing technology into the field of BIM model checking and completes the automated process of constructing domain knowledge graphs and checking BIM model data. The validation of its functionality and usability through two case studies on a self-developed BIM checking platform.

With the development of data mining technology, diverse and broader domain knowledge can be extracted automatically. However, the research on applying knowledge mapping and data visualization techniques to genealogical data is limited. This paper aims to fill this research gap by providing a systematic framework and process guidance for practitioners seeking to uncover hidden knowledge from genealogy.

Based on a literature review of genealogy's current knowledge reasoning research, the authors constructed an integrated framework for knowledge inference and visualization application using a knowledge graph. Additionally, the authors applied this framework in a case study using “Manchu Clan Genealogy” as the data source.

The case study shows that the proposed framework can effectively decompose and reconstruct genealogy. It demonstrates the reasoning, discovery, and web visualization application process of implicit information in genealogy. It enhances the effective utilization of Manchu genealogy resources by highlighting the intricate relationships among people, places, and time entities.

This study proposed a framework for genealogy knowledge reasoning and visual analysis utilizing a knowledge graph, including five dimensions: the target layer, the resource layer, the data layer, the inference layer, and the application layer. It helps to gather the scattered genealogy information and establish a data network with semantic correlations while establishing reasoning rules to enable inference discovery and visualization of hidden relationships.

The purpose of this work is to explore the new possibilities enabled by the recent introduction of RDF-star, an extension that allows for statements about statements within the Resource Description Framework (RDF). Alongside Named Graphs, this approach offers opportunities to leverage a meta-level for data modeling and data applications.

In this extended paper, the authors build onto three modeling use cases published in a previous paper: (1) provide provenance information, (2) maintain backwards compatibility for existing models, and (3) reduce the complexity of a data model. The authors present two scenarios where they implement the use of the meta-level to extend a data model with meta-information.

The authors present three abstract patterns for actively using the meta-level in data modeling. The authors showcase the implementation of the meta-level through two scenarios from our research project: (1) the authors introduce a workflow for triple annotation that uses the meta-level to enable users to comment on individual statements, such as for reporting errors or adding supplementary information. (2) The authors demonstrate how adding meta-information to a data model can accommodate highly specialized data while maintaining the simplicity of the underlying model.

Through the formulation of data modeling patterns with RDF-star and the demonstration of their application in two scenarios, the authors advocate for data modelers to embrace the meta-level.

With RDF-star being a very new extension to RDF, to the best of the authors’ knowledge, they are among the first to relate it to other meta-level approaches and demonstrate its application in real-world scenarios.

Adequate means for easily viewing, browsing and searching knowledge graphs (KGs) are a crucial, still limiting factor. Therefore, this paper aims to present virtual properties as valuable user interface (UI) concept for ontologies and KGs able to improve these issues. Virtual properties provide shortcuts on a KG that can enrich the scope of a class with other information beyond its direct neighborhood.

Virtual properties can be defined as enhancements of shapes constraint language (SHACL) property shapes. Their values are computed on demand via protocol and RDF query language (SPARQL) queries. An approach is demonstrated that can help to identify suitable virtual property candidates. Virtual properties can be realized as integral functionality of generic, frame-based UIs, which can automatically provide views and masks for viewing and searching a KG.

The virtual property approach has been implemented at Bosch and is usable by more than 100,000 Bosch employees in a productive deployment, which proves the maturity and relevance of the approach for Bosch. It has successfully been demonstrated that virtual properties can significantly improve KG UIs by enriching the scope of a class with information beyond its direct neighborhood.

SHACL-defined virtual properties and their automatic identification are a novel concept. To the best of the author’s knowledge, no such approach has been established nor standardized so far.

This study aims to extract knowledge of ancient Chinese scientific and technological documents bibliographic summaries (STDBS) and provide the knowledge graph (KG) comprehensively and systematically. By presenting the relationship among content, discipline, and author, this study focuses on providing services for knowledge discovery of ancient Chinese scientific and technological documents.

This study compiles ancient Chinese STDBS and designs a knowledge mining and graph visualization framework. The authors define the summaries' entities, attributes, and relationships for knowledge representation, use deep learning techniques such as BERT-BiLSTM-CRF models and rules for knowledge extraction, unify the representation of entities for knowledge fusion, and use Neo4j and other visualization techniques for KG construction and application. This study presents the generation, distribution, and evolution of ancient Chinese agricultural scientific and technological knowledge in visualization graphs.

The knowledge mining and graph visualization framework is feasible and effective. The BERT-BiLSTM-CRF model has domain adaptability and accuracy. The knowledge generation of ancient Chinese agricultural scientific and technological documents has distinctive time features. The knowledge distribution is uneven and concentrated, mainly concentrated on C1-Planting and cultivation, C2-Silkworm, and C3-Mulberry and water conservancy. The knowledge evolution is apparent, and differentiation and integration coexist.

This study is the first to visually present the knowledge connotation and association of ancient Chinese STDBS. It solves the problems of the lack of in-depth knowledge mining and connotation visualization of ancient Chinese STDBS.

Despite the fact that hygiene and sanitation are becoming more critical for improving the present situation in developing nations, the factors that affect them are not well covered in the present research. This paper investigates the quality of the hygiene and sanitization factors and identifies the interrelations between the identified factors.

A graph theory-based approach is proposed to assess the factors influencing the practice, and a critical service index (CSI) is used to quantify the same.

Two Indian villages are used to illustrate the implementation of the suggested approach. This represents the validation of the suggested method, as well as assisting in the development of essential suggestions for increasing the quality of hygiene and sanitization in the Indian context. In spite of the increasing importance of hygiene and sanitation for improving the current situation in developing countries, the factors that influence them are not well-researched.

This study contributes in two ways. First, it provides an organized methodology for quantifying hygiene and sanitation factors and a critical service index that incorporates the findings. The suggested approach may also be used to evaluate and classify other sectors. Second, it shows how the methodology was used to create key recommendations for two Indian villages, which may be considered the first effort in India’s hygiene and sanitation initiatives.

This research discussed improvements in sanitation and hygiene habits among Indian households, which have not been achieved as expected under the Swachh Bharat Mission.

Although India is seen as a key player in the global economy, it is still below its potential level of growth. In this age of globalism, integration with the global economy through trade and foreign investments fosters domestic growth. For India, although this integration has strengthened over the years, there are certain gaps that remain to be addressed. Though numerous studies in the literature have tried to find answers to these questions, an important aspect that has not been considered by these studies relates to India’s federal structure and the role of states in determining the aggregate economic outcome. As Foreign Direct Investment (FDI) inflows to India are concentrated in a few states, this paper aims to provide an assessment of the reasons behind this trend.

This paper aims to investigate the reasons behind the interstate differences with respect to FDI inflows in India. The analytical work undertaken for this paper is based on secondary data, collected and collated from various sources. The approach adopted for this paper includes a heat graph analysis to examine whether there is a clear pattern in terms of the state-specific factors for high FDI states versus the low FDI states. This data analysis is followed by an econometric estimation to gauge the impact of state-specific factors in determining the FDI inflows.

As per the secondary data–driven heat graph and econometric analysis, factors like industrial output, social sector expenditure, judicial quality, connectivity indicators, labor cost and availability of credit, act as differentiators between high and low FDI-receiving states. It then becomes imperative to bridge the gap between the two sets of states in terms of these specific factors. Implementation and success of policy interventions can only be derived at the state level and therefore needs more decentralized approach.

This paper tries to identify the reasons that are responsible for FDI inflows being concentrated in a few Indian states. This involves a comprehensive analysis of several variables to understand whether there is a clear pattern where high-FDI states are also in a better position with respect to these attributes. This effort to factor in the federal aspect of a macroeconomic indicator like FDI provides new dynamic to this area of work.