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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.

Defects in concrete surfaces are inevitably recurring during construction, which needs to be checked and accepted during construction and completion. Traditional manual inspection of surface defects requires inspectors to judge, evaluate and make decisions, which requires sufficient experience and is time-consuming and labor-intensive, and the expertise cannot be effectively preserved and transferred. In addition, the evaluation standards of different inspectors are not identical, which may lead to cause discrepancies in inspection results. Although computer vision can achieve defect recognition, there is a gap between the low-level semantics acquired by computer vision and the high-level semantics that humans understand from images. Therefore, computer vision and ontology are combined to achieve intelligent evaluation and decision-making and to bridge the above gap.

Combining ontology and computer vision, this paper establishes an evaluation and decision-making framework for concrete surface quality. By establishing concrete surface quality ontology model and defect identification quantification model, ontology reasoning technology is used to realize concrete surface quality evaluation and decision-making.

Computer vision can identify and quantify defects, obtain low-level image semantics, and ontology can structurally express expert knowledge in the field of defects. This proposed framework can automatically identify and quantify defects, and infer the causes, responsibility, severity and repair methods of defects. Through case analysis of various scenarios, the proposed evaluation and decision-making framework is feasible.

This paper establishes an evaluation and decision-making framework for concrete surface quality, so as to improve the standardization and intelligence of surface defect inspection and potentially provide reusable knowledge for inspecting concrete surface quality. The research results in this paper can be used to detect the concrete surface quality, reduce the subjectivity of evaluation and improve the inspection efficiency. In addition, the proposed framework enriches the application scenarios of ontology and computer vision, and to a certain extent bridges the gap between the image features extracted by computer vision and the information that people obtain from images.

The purpose of this paper is to explore the role knowledge plays in expert commercial valuer practice to unpack the way theoretical and experiential knowledge operates in order to improve practice and education.

Adopting a cognitivist perspective and identifying meta-reasoning, using a grounded theory methodology, through the study of 11 chartered valuation surveyors practicing in Birmingham, United Kingdom, the distinctive theoretical and experiential knowledge they used was elicited through their in-depth reflection on a valuation task followed by analytical interviews exploring meaning and reasons of actions described.

The results confirmed that multi-sourced and rich valuation knowledge was a key attribute of a valuation expert. However, the experiential knowledge was not used to undertake the task but to select the methods and knowledge appropriate for the task and context. This meta-reasoning is a key to the speed, accuracy and justification of their practices. Thus, the experience gained from many years of valuation provides expert valuers with meta-reasoning involving knowledge of what, how and when to deal with problems in different circumstances such as the knowledge of markets and handling of clients.

Making meta-reasoning a key aspect of valuation will identify its characteristics more clearly, thus assisting the development of practitioners and providing a new focus for education to advance professional goals.

Meta-reasoning and meta-cognitive knowledge have not been identified as a key to successful valuation practice. This meta-reasoning allows a subtle balance of theory and experience in valuation practice that is appropriate to the situation.

Pew and Mavor (1998) called for an integrative representation of human behavior for use in models of individual combatants and organizations. Models with integrated representation of behavior have only been achieved at rudimentary levels according to those performing the studies (e.g. Pew & Mavor, 1998; Tulving, 2002) and those building the models (e.g. Warwick et al., 2002). This chapter will address aspects of cognitive performance that are important to incorporate into models of combat based on acceptance of theory, strength of empirical data, or for other reasons such as to bridge gaps where incomplete knowledge exists about cognitive behavior and performance. As a starting point, this chapter will assess which of Pew and Mavor’s recommendations are still appropriate as determined by a review of selected literature on cognition and its representation. We will also provide some review and extensions of key literature on cognition and modeling and suggest a way ahead to close the remaining gaps. Different aspects of cognition are described with recent findings, and most are followed by an example of how they have been represented in computer models or a discussion of challenges to their representation in modeling.

This paper describes an architecture for the usage of Instructional Design (ID) knowledge in intelligent instructional systems. In contrast with other architectures, ontologies are used to represent ID knowledge about both what to teach and how to teach. Moreover, set‐theoretic reasoning is used for the provision of inferential services. In particular, the paper shows how set‐theoretic deductions can be applied (i) to support the modelling of ID knowledge bases, (ii) to retrieve suitable teaching methods from them, and (iii) to detect errors in a training design. The intelligent knowledge management environment CONCEPTOOL is used to demonstrate the benefits of the proposed architecture.

The primary objective of this research is to investigate the impact of task‐technology fit on users’ performance when using artificial intelligence systems for auditing tasks. Four artificial intelligence auditing systems, two problem‐solving programs, and four questionnaires were developed. A laboratory experiment was performed with 292 undergraduate auditing students. The results suggested that the effect of task‐technology fit on accuracy in solving problems was marginal for case‐based reasoning with unstructured tasks. No significant effect was found on problem‐solving accuracy for rule‐based reasoning with structured tasks. The task‐technology fit, however, marginally increased users’ certainty of the correctness of their solutions.

Behavioural studies of valuers have suggested that valuers rely on a number of cognitive strategies involving reasoning and intuition when undertaking a valuation task. However, there are few studies of the actual reasoning mechanisms in valuation. In other fields, much attention has been paid to forward and backward reasoning, as this shows the choices and decisions that are made in undertaking a complex task. This paper studied this during a valuation task. The purpose of this paper is twofold: first, to develop a methodological approach for empirical research on valuers’ reasoning, and, second, to report expert-novice differences on valuers’ use of forward and backward reasoning during a valuation problem solving.

The study utilised a verbal protocol analysis (VPA) to elicit think-aloud data from a purposive sample of a group of valuers of different levels of expertise undertaking a commercial-valuation task. Through a content analysis interpretive strategy, the transcripts were analysed into different cognitive segments identifying the forward and backward reasoning strategies.

The findings showed that valuers accomplished the valuation task by dividing the overall problem into sub-problems. These sub-problems are thereafter solved by integrating available data with existing knowledge by relying more on forward reasoning than backward reasoning. However, there were effects associated with the level of expertise in the way the processes of forward and backward reasoning are used, with the expert and intermediate valuers being more thorough and comprehensive in their reasoning process than the novices.

This study explores the possibility that forward and backward reasoning play an important role in commercial valuation problem solving using a limited sample of valuers. Given this, data cannot be generalised to all valuation practice settings but may motivate future research that examines the effectiveness of forward and backward reasoning in diverse valuation practice settings and develops a holistic model of valuation reasoning.

The findings of this study are applicable to valuation practice. Future training efforts need to evaluate the usefulness of teaching problem solving and explicitly recognise forward and backward reasoning, along with other problem-solving strategies uncovered in this study, as standard training strategies for influencing the quality of valuation decisions.

By adopting VPA, this study employs an insightful and rich dataset which allows an interpretation of thoughts of valuers into cognitive reasoning strategies that provide a deeper level of understanding of how valuers solve valuation problem; this has not been possible in previous related valuation studies.

The implied assembly constraints of a computer-aided design (CAD) model (e.g. hierarchical constraints, geometric constraints and topological constraints) represent an important basis for product assembly sequence intelligent planning. Assembly prior knowledge contains factual assembly knowledge and experience assembly knowledge, which are important factors for assembly sequence intelligent planning. This paper aims to improve monotonous assembly sequence planning for a rigid product, intelligent planning of product assembly sequences based on spatio-temporal semantic knowledge is proposed.

A spatio-temporal semantic assembly information model is established. The internal data of the CAD model are accessed to extract spatio-temporal semantic assembly information. The knowledge system for assembly sequence intelligent planning is built using an ontology model. The assembly sequence for the sub-assembly and assembly is generated via attribute retrieval and rule reasoning of spatio-temporal semantic knowledge. The optimal assembly sequence is achieved via a fuzzy comprehensive evaluation.

The proposed spatio-temporal semantic information model and knowledge system can simultaneously express CAD model knowledge and prior knowledge for intelligent planning of product assembly sequences. Attribute retrieval and rule reasoning of spatio-temporal semantic knowledge can be used to generate product assembly sequences.

The assembly sequence intelligent planning example of linear motor highlights the validity of intelligent planning of product assembly sequences based on spatio-temporal semantic knowledge.

The spatio-temporal semantic information model and knowledge system are built to simultaneously express CAD model knowledge and assembly prior knowledge. The generation algorithm via attribute retrieval and rule reasoning of spatio-temporal semantic knowledge is given for intelligent planning of product assembly sequences in this paper. The proposed method is efficient because of the small search space.

The preparation of future teachers of young children should incorporate attention to the developmental markers at the heart of developmentally appropriate practice and ground early childhood subject matter learning in disciplinary perspectives, engagement, and thinking essential for later disciplinary learning. With this focus in mind, I described an instructional sequence designed to engage teacher candidates in historical reasoning tasks where they considered the conceptual resources they used to support their own historical reasoning as a point of entry for considering the conceptual resources young children have at their disposal. I presumed that such a comparison would allow candidates to develop the kind of content knowledge for teaching, enabling them to best leverage children’s historical reasoning as a means of deepening children’s historical knowledge and understanding. The analysis indicated that candidates began to construct initial developmental trajectories of children’s historical reasoning and raised pedagogical questions suggesting they began to envision themselves as teachers of historical inquiry.

Real-time location sensing (RTLS) systems offer a significant potential to advance the management of construction processes by potentially providing real-time access to the locations of workers and equipment. Many location-sensing technologies tend to perform poorly for indoor work environments and generate large data sets that are somewhat difficult to process in a meaningful way. Unfortunately, little is still known regarding the practical benefits of converting raw worker tracking data into meaningful information about construction project progress, effectively impeding widespread adoption in construction.

The presented framework is designed to automate as many steps as possible, aiming to avoid manual procedures that significantly increase the time between progress estimation updates. The authors apply simple location tracking sensor data that does not require personal handling, to ensure continuous data acquisition. They use a generic and non-site-specific knowledge base (KB) created through domain expert interviews. The sensor data and KB are analyzed in an abductive reasoning framework implemented in Answer Set Programming (extended to support spatial and temporal reasoning), a logic programming paradigm developed within the artificial intelligence domain.

This work demonstrates how abductive reasoning can be applied to automatically generate rich and qualitative information about activities that have been carried out on a construction site. These activities are subsequently used for reasoning about the progress of the construction project. Our framework delivers an upper bound on project progress (“optimistic estimates”) within a practical amount of time, in the order of seconds. The target user group is construction management by providing project planning decision support.

The KB developed for this early-stage research does not encapsulate an exhaustive body of domain expert knowledge. Instead, it consists of excerpts of activities in the analyzed construction site. The KB is developed to be non-site-specific, but it is not validated as the performed experiments were carried out on one single construction site.

The presented work enables automated processing of simple location tracking sensor data, which provides construction management with detailed insight into construction site progress without performing labor-intensive procedures common nowadays.

While automated progress estimation and activity recognition in construction have been studied for some time, the authors approach it differently. Instead of expensive equipment, manually acquired, information-rich sensor data, the authors apply simple data, domain knowledge and a logical reasoning system for which the results are promising.