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This study presents an ontological approach to assess the architectural outputs of generative adversarial networks. This paper aims to assess the performance of the generative adversarial network in representing building knowledge.

The proposed ontological assessment consists of five steps. These are, respectively, creating an architectural data set, developing ontology for the architectural data set, training the You Only Look Once object detection with labels within the proposed ontology, training the StyleGAN algorithm with the images in the data set and finally, detecting the ontological labels and calculating the ontological relations of StyleGAN-generated pixel-based architectural images. The authors propose and calculate ontological identity and ontological inclusion metrics to assess the StyleGAN-generated ontological labels. This study uses 300 bay window images as an architectural data set for the ontological assessment experiments.

The ontological assessment provides semantic-based queries on StyleGAN-generated architectural images by checking the validity of the building knowledge representation. Moreover, this ontological validity reveals the building element label-specific failure and success rates simultaneously.

This study contributes to the assessment process of the generative adversarial networks through ontological validity checks rather than only conducting pixel-based similarity checks; semantic-based queries can introduce the GAN-generated, pixel-based building elements into the architecture, engineering and construction industry.

The purpose of this paper is to reuse learning resources from course module and forum discussion in ODL settings and structure it with ontological representation.

Thus, an ontology is designed by extending simple knowledge organization system specification to structure the learning resources. Furthermore, a semantic forum system is proposed as a front end mechanism to represent the ontological structure designed for the learner to easily access, search and navigate the relevant knowledge of interest. In addition, this study evaluates the effectiveness of the proposed system along with three variables, namely, learners’ perceptions, system design perceptions and system content perceptions. Accordingly, a close-ended online survey was developed and administered to 74 online learners.

The findings demonstrate positive perceptions of the proposed system which is based on ontological representation as an effective learning system that is able to enhance the understanding of courses taught.

This paper presents an ontological structure approach to add meaning to the learning resources, indexed in such a way that it can be reused, searched, processed and shared.

The purpose of this study was to construct a mind counseling ontology to efficiently facilitate the diagnosis of the diseases of mind. To determine the structure of mind counseling ontology, this study conducted analysis on structural forms available in counseling books and other related fields and adopted essential ones in the explanation of counseling. The processing of the diseases of mind was divided into three stages: cause, symptoms and counseling. The stages were analyzed one by one in terms of process, functional elements and relevant technique necessary at each stage.

In the mind counseling list, there are 12 different diagnoses of diseases of mind that are classified into four classes. Thus, the causes, symptoms, prescription and medical history for 12 diseases of mind are defined as a higher rank concept of mind counseling ontology. The causes, symptoms, prescription and medical history consist of definition, affective characteristics and related factors, while the potential diagnosis consists of definition and risk factor. This information does specify detailed notions in the diagnosis of diseases of mind, but considering the limitation of not being able to represent all the diseases, this study enables a counseling center to give and use individual definitions of diagnostic terminology of their own.

This study adopted the top-down approach, in which mind counseling ontology defines a higher rank concept, the terminology in diagnosing diseases of mind, based on the list of terms from the counseling record that specifies the abstract concepts of the diagnosis. The bottom-up approach was also incorporated, which defines the diagnostic terms extracted from the counseling record as a subordinate concept of the mind counseling ontology. Thus, the development of the mind counseling ontology involves the combination of top-down and bottom-up approaches to the construction of ontology.

This research has significance in that it deals with the fundamental problem of the mind aiming for a true change and healing of it, which is the ultimate purpose of this ontology, especially in the circumstances where research on ontology in diagnosing the diseases of mind is unprecedented.

This paper examines visual representation in marketing communication from a distinctive, interdisciplinary perspective that draws on ethics, visual studies and critical race theory. An ontological approach is offered as an alternative to phenomenologically based approaches in marketing scholarship that use consumer responses to generate data. Suggests ways to clarify complex issues of representational ethics in marketing by applying a semiotically‐based analysis that places ontological identity at the center of societal marketing concerns. Analyzes representations of the exotic Other in disparate marketing campaigns, including advertising, tourist promotions and music, as examples of bad faith marketing strategy. Music is an important force in marketing communication, yet marketing studies have rarely considered music and its visual representations as data for inquiry. Feels that considering visual representation within marketing from an ontological standpoint contributes additional insight into societal marketing and places global marketing processes within the intersection of ethics, aesthetics and representation.

The purpose of this paper is to improve the conceptual-based search by incorporating structural ontological information such as concepts and relations. Generally, Semantic-based information retrieval aims to identify relevant information based on the meanings of the query terms or on the context of the terms and the performance of semantic information retrieval is carried out through standard measures-precision and recall. Higher precision leads to the (meaningful) relevant documents obtained and lower recall leads to the less coverage of the concepts.

In this paper, the authors enhance the existing ontology-based indexing proposed by Kohler et al., by incorporating sibling information to the index. The index designed by Kohler et al., contains only super and sub-concepts from the ontology. In addition, in our approach, we focus on two tasks; query expansion and ranking of the expanded queries, to improve the efficiency of the ontology-based search. The aforementioned tasks make use of ontological concepts, and relations existing between those concepts so as to obtain semantically more relevant search results for a given query.

The proposed ontology-based indexing technique is investigated by analysing the coverage of concepts that are being populated in the index. Here, we introduce a new measure called index enhancement measure, to estimate the coverage of ontological concepts being indexed. We have evaluated the ontology-based search for the tourism domain with the tourism documents and tourism-specific ontology. The comparison of search results based on the use of ontology “with and without query expansion” is examined to estimate the efficiency of the proposed query expansion task. The ranking is compared with the ORank system to evaluate the performance of our ontology-based search. From these analyses, the ontology-based search results shows better recall when compared to the other concept-based search systems. The mean average precision of the ontology-based search is found to be 0.79 and the recall is found to be 0.65, the ORank system has the mean average precision of 0.62 and the recall is found to be 0.51, while the concept-based search has the mean average precision of 0.56 and the recall is found to be 0.42.

When the concept is not present in the domain-specific ontology, the concept cannot be indexed. When the given query term is not available in the ontology then the term-based results are retrieved.

In addition to super and sub-concepts, we incorporate the concepts present in same level (siblings) to the ontological index. The structural information from the ontology is determined for the query expansion. The ranking of the documents depends on the type of the query (single concept query, multiple concept queries and concept with relation queries) and the ontological relations that exists in the query and the documents. With this ontological structural information, the search results showed us better coverage of concepts with respect to the query.

The present paper aims to describe an approach for building the Semantic Web rules for interoperation between heterogeneous learning objects, namely course outlines from different universities, and one of the rule uses: identifying (in)compatibilities between course descriptions.

As proof of concept, a rule set is implemented using the rule markup language (RuleML), a member of XML‐based languages. This representation in RuleML allows the rule base to be platform‐independent, flexibly extensible and executable.

The RuleML source representation is easily converted to other XML‐based languages (such as RDF, OWL and XMI) as well as incorporated into, and extracted from, existing XML‐based repositories (such as IEEE LOM and CanLOM) using XSL Transformations (XSLT).

The RuleML facts and rules represented in the positional slotted language are used by the OO jDREW reasoning engine to detect and map between semantically equivalent components of course outlines as the key step in their interoperation. In particular, this will enable the precise delivery of learning objects (e.g. course outlines) from repositories to a specific learner's context.

Although the particular scenario is discussed in the present paper, the proposed approach can be applied to other tasks related to enabling semantic interoperability.

This paper aims to examine the concept of standardization beyond its traditional use in generating and implementing standards and good practice guidelines (S&GPG) by looking at existing and emerging trends.

This paper utilizes two primary approaches to categorizing S&GPG for better comprehension: categorization based on provenance as well as based on subject matter.

A significant concern related to categorizing S&GPG based on provenance or subject is the constant proliferation of standards being developed and introduced every year. This rapid growth in standards requires frequent re-categorization to keep up with the dynamic nature of this field. To tackle this problem, this paper explores emerging concepts such as ontological representation and frameworks that offer archives and records management (ARM) professionals.

Standardization refers to establishing uniform rules through mutual agreement to ensure consistency. The study of standardization goes beyond the development of individual S&GPG, encompassing their practical application in work settings. Categorizing standards alone may not fully capture their actual use. However, abstraction mechanisms like ontological representations, models and frameworks can demonstrate how these standards have been leveraged. This paper provides illustrative examples rather than an exhaustive list to showcase how these mechanisms have been applied in research projects or as practical tools.

This paper explores the emerging topic of standardization from the perspective of ontological representations and models or frameworks. In addition, it also contributes to the discussion of the 2022 version of ARMA International’s Information Governance Implementation Model and the 2020 version of the World Bank Group's Records Management Roadmap, providing unique insights into these topics.

This paper aims to serve two main purposes. In the first instance it aims to it provide an overview addressing the state‐of‐the‐art in the area of activity recognition, in particular, in the area of object‐based activity recognition. This will provide the necessary material to inform relevant research communities of the latest developments in this area in addition to providing a reference for researchers and system developers who ware working towards the design and development of activity‐based context aware applications. In the second instance this paper introduces a novel approach to activity recognition based on the use of ontological modeling, representation and reasoning, aiming to consolidate and improve existing approaches in terms of scalability, applicability and easy‐of‐use.

The paper initially reviews the existing approaches and algorithms, which have been used for activity recognition in a number of related areas. From each of these, their strengths and weaknesses are discussed with particular emphasis being placed on the application domain of sensor enabled intelligent pervasive environments. Based on an analysis of existing solutions, the paper then proposes an integrated ontology‐based approach to activity recognition. The proposed approach adopts ontologies for modeling sensors, objects and activities, and exploits logical semantic reasoning for the purposes of activity recognition. This enables incremental progressive activity recognition at both coarse‐grained and fine‐grained levels. The approach has been considered within the realms of a real world activity recognition scenario in the context of assisted living within Smart Home environments.

Existing activity recognition methods are mainly based on probabilistic reasoning, which inherently suffer from a number of limitations such as ad hoc static models, data scarcity and scalability. Analysis of the state‐of‐the‐art has helped to identify a major gap between existing approaches and the need for novel recognition approaches posed by the emerging multimodal sensor technologies and context‐aware personalised activity‐based applications in intelligent pervasive environments. The proposed ontology based approach to activity recognition is believed to be the first of its kind, which provides an integrated framework‐based on the unified conceptual backbone, i.e. activity ontologies, addressing the lifecycle of activity recognition. The approach allows easy incorporation of domain knowledge and machine understandability, which facilitates interoperability, reusability and intelligent processing at a higher level of automation.

The comprehensive overview and critiques on existing work on activity recognition provide a valuable reference for researchers and system developers in related research communities. The proposed ontology‐based approach to activity recognition, in particular the recognition algorithm has been built on description logic based semantic reasoning and offers a promising alternative to traditional probabilistic methods. In addition, activities of daily living (ADL) activity ontologies in the context of smart homes have not been, to the best of one's knowledge, been produced elsewhere.

This paper proposes an innovative intelligent simulation-based construction planning framework that introduces a new approach to simulation-based construction planning.

In this approach, the authors developed an ontological inference engine as an integrated part of a constraint-based simulation system that configures the construction processes, defines activities and manages resources considering a variety of requirements and constraints during the simulation. It allows for the incorporation of the latest project information and a deep level of construction planning knowledge in the planning. The construction planning knowledge is represented by an ontology and several semantic rules. Also, the proposed framework uses the project building information model (BIM) to extract information regarding the construction product and the relations between elements. The extracted information is then converted to an ontological format to be useable by the framework.

The authors implemented the framework in a case study project and tested its usefulness and capabilities. It successfully generated the construction processes, activities and required resources based on the construction product, available resources and the planning rules. It also allowed for a variety of analyses regarding different construction strategies and resource planning. Moreover, 4D BIM models that provide a very good understanding of the construction plan can be automatically generated using the proposed framework.

The active integration between BIM, discrete-event simulation (DES) and ontological knowledge base and inference engine defines a new class of construction simulation with expandable applications.

The aim of this paper is discussing about the similarities between the life cycle of knowledge management and the processes in which learning objects are created, evaluated and used.

The paper describes LO and learning designs and depicts their integration into the knowledge life cycle (KLC) of the KMCI, showing the main ontological definitions required to represent the proposed integration, putting them into relation with previous research in the topic.

The main points in that the “learning object” concept can be connected to existing KLC models in order to provide a more comprehensive framework for reuse‐oriented e‐learning and knowledge management.

The paper describes how to integrate learning‐oriented artefacts as part of the organizational context, in particular, as part of the knowledge life cycle (KLC). It also depicts the integration of a reuse‐oriented e‐learning framework into the KLC of the KMCI in the form of ontological definitions.